Corpus

dynamics-dynamics that make the whole system "more than the sum of its parts." We examine the relationship between prediction performance and emergence by leveraging quantitative metrics of emergence and modeling environmental time-series prediction within a bio-inspired computational framework called reservoir computing. Notably, three key results reveal a robust bidirectional coupling between prediction performance and emergence: (1) optimizing hyperparameters for performance enhances emergent dynamics, and vice versa; (2) emergent dynamics serve as a highly sufficient and often also necessary condition for prediction success in most environments; and (3) training with larger datasets results in stronger emergent dynamics, encoding task-relevant information. These findings emphasize the importance of emergence-based approaches for studying neural networks-biological or artificial-as they enable network-level insights, complementing traditional single-neuron-based analyses.

Stroboscopic light stimulation (SLS) on closed eyes typically induces simple visual hallucinations, characterized by vivid, geometric, and colourful patterns. A dataset of 898 sentences, extracted from 407 open subjective reports, was recently compiled as part of the Dreamachine programme (https://dreamachine.world/) (Collective Act, 2022), an immersive multisensory experience that combines SLS and spatial sound in a collective setting. Although open reports extend the range of reportable phenomenology, their analysis presents significant challenges, particularly in systematically identifying patterns. To address this challenge, we implemented a data-driven approach leveraging large language models and topic modelling to uncover and interpret latent experiential topics directly from the Dreamachine's text-based reports. Our analysis confirmed the presence of simple visual hallucinations typically documented in scientific studies of SLS, while also revealing experiences of altered states of consciousness and complex hallucinations. Building on these findings, our computational approach expands the systematic study of subjective experience by enabling data-driven analyses of open-ended phenomenological reports, capturing experiences not readily identified through standard questionnaires. By revealing rich and multifaceted aspects of experiences, our study broadens our understanding of stroboscopically induced phenomena while highlighting the potential of natural language processing and large language models in the field of computational phenomenology. More generally, this approach provides a practically applicable methodology for uncovering subtle hidden patterns of subjective experience across diverse research domains. Open-source implementation and an interactive web application are provided to facilitate application of this methodology.

Voluntary actions are associated with sense of agency and distortions of perceived duration. This study examined how sensorimotor coupling and visual perspective modulate perceived duration and sense of agency during manual actions in four pre-registered experiments using virtual reality. Participants moved their hands while observing a virtual hand moving (a)synchronously, and evaluated movement duration and their sense of agency. We found that sensorimotor coupling provided by synchronous visual feedback is associated with longer perceived duration relative to action-related time compression, compared with delayed feedback or pre-recorded movements of another person (Experiments 1 and 2). Sensorimotor coupling also modulated sense of agency and may serve as a shared basis for perceived time and agency. Comparable modulations across first- and third-person perspectives (Experiments 1 and 2), anatomical configurations (Experiment 3), and attention on external entities (Experiment 4) suggest a shared predictive system for sensory consequences of self-generated actions and others’ reactions. This modulation was observed irrespective of anatomical configuration and when attention was directed toward objects unrelated to one’s own body, suggesting that the underlying mechanism is not confined to representations specific to the self, but may instead reflect domain-general predictive computations. These findings, together with the virtual reality technique developed, offer new insights into how humans experience passage of time and agency during voluntary action.

Hallucinations are percepts that occur in the absence of corresponding sensory input, reflecting internally generated activity within sensory systems. Among visual hallucinations, geometric formations are common, yet their phenomenological structure remains poorly characterised. They occur across contexts ranging from psychedelic experiences to psychiatric and neurological disorders, likely involving related mechanisms within the early sensory cortices. Despite their relevance across disciplines, existing measures rely primarily on verbal reports or coarse rating scales, which do not capture geometric structure.To address this limitation, we developed and validated image recreation methods in which healthy participants were trained to recreate images of their hallucinations induced by stroboscopic light at frequencies known to elicit visual hallucinations under laboratory conditions. In Experiment 1 (N = 55), participants used a freehand drawing interface; in Experiment 2 (N = 54), a generative image recreation interface was employed. Training procedures mitigated individual differences in expressive ability. Validation trials demonstrated that geometric features were recoverable at the population level when image stimuli were used in place of hallucination-inducing stimuli. Across experiments, visual qualities of induced hallucinations varied systematically with stimulation frequency, consistent with prior reports.The data revealed a broader repertoire of geometric forms than typically described in the literature, including structures not predicted by current computational models of simple hallucinations. Applying these methods to other contexts could clarify the range of geometric hallucinations the visual system tends to produce under controlled perturbation across psychedelic, pathological, and laboratory settings. More precise characterisation of hallucination phenomenology may refine constraints on theoretical models of how visual experience is constructed in normal and altered states.

Voluntary actions are associated with sense of agency and distortions of perceived duration. This study examined how sensorimotor coupling and visual perspective modulate perceived duration and sense of agency during manual actions in four pre-registered experiments using virtual reality. Participants moved their hands while observing a virtual hand moving (a)synchronously, and evaluated movement duration and their sense of agency. We found that sensorimotor coupling provided by synchronous visual feedback is associated with longer perceived duration relative to action-related time compression, compared with delayed feedback or pre-recorded movements of another person (Experiments 1 and 2). Sensorimotor coupling also modulated sense of agency and may serve as a shared basis for perceived time and agency. Comparable modulations across first- and third-person perspectives (Experiments 1 and 2), anatomical configurations (Experiment 3), and attention on external entities (Experiment 4) suggest a shared predictive system for sensory consequences of self-generated actions and others’ reactions. This modulation was observed irrespective of anatomical configuration and when attention was directed toward objects unrelated to one’s own body, suggesting that the underlying mechanism is not confined to representations specific to the self, but may instead reflect domain-general predictive computations. These findings, together with the virtual reality technique developed, offer new insights into how humans experience passage of time and agency during voluntary action.

Hallucinations arise from disruptions in neural processes that construct perceptual experience from sensory input. Progress in consciousness science may therefore benefit from methods that precisely characterise their phenomenology. We introduce and validate the 6-Dimensional Visual Hallucination Questionnaire (6D-VHQ), a brief instrument designed to quantify visual hallucination content across contexts. The 6D-VHQ measures six dimensions: geometric and semantic content, detail level, vividness, entropy, and focality. It focuses on perceptual content rather than broader altered-state features.We report three studies: an image-based validation study, an application to stroboscopically induced visual hallucinations, and an application to psychedelic closed-eye visuals. Across 962 responses, the questionnaire showed high internal consistency, a factor structure aligned with the intended dimensions, and stable performance across settings. High inter-item redundancy indicated an initially over-complete but coherent implementation of the instrument. Compared with the 11-Dimensional Altered States of Consciousness questionnaire (11D-ASC), both instruments similarly captured simple and complex visual hallucinations despite differing psychometric properties. While the 11D-ASC showed low internal consistency, the 6D-VHQ has high internal consistency alongside high inter-dimensional redundancy. While the 11D-ASC correlates to participant traits, and 6D-VHQ is largely invariant to these, capturing the differences in visual phenomenology between different classes of hallucinatory experiences. The 6D-VHQ showed that stroboscopic stimulation predominantly elicited vivid simple geometric hallucinations, whereas psychedelic experiences showed combined geometric content alongside more vivid, detailed, and semantic visuals. The 6D-VHQ provides an operationalisation of a six-dimensional model of visual hallucination content that distinguishes broad classes of hallucinations across induction methods.

The integrated information theory of consciousness (IIT) is uniquely ambitious in proposing a mathematical formula, derived from apparently fundamental properties of conscious experience, to describe the quantity and quality of consciousness for any physical system that possesses it. IIT has generated considerable debate, which has engendered some misunderstandings and misrepresentations. Here we address and hope to remedy this. We begin by concisely summarising the essentials of IIT. Given IIT is supposed to apply universally, we do this with reference to an arbitrary patch of matter, as opposed to the usual system of discrete computational units. Then, after briefly summarising IIT's theoretical and empirical achievements, we focus on five points which we consider especially important for driving forward new theory and increasing understanding. First, a high value of the measure $Φ$ is not synonymous with `more consciousness'. We describe how $Φ$ might be replaced with a suite of quantities to obtain a multi-dimensional characterisation of states of consciousness. Second, we describe with nuance the distinct flavour of panpsychism implied by IIT -- whereby space (and time) are tiled with substrates of (proto-) consciousness -- and find this is not problematic for the theory. Third, $Φ$ is not well-defined for real physical systems, and has not been computed on any real physical system. Fourth, so far only proxies for IIT measures have been computed, and not approximations. Fifth, for IIT to fit with current successful theories in fundamental physics, a reformulation in terms of continuous fields would be needed.

Abstract Visual hallucinations (VHs) occur across psychedelic states and diverse psychiatric and neurological conditions, yet their phenomenology remains difficult to characterise. Empirical research on VHs is hindered by the lack of large-scale phenomenological datasets, which limits both mechanistic accounts and the systematic characterisation of when and how they arise. Stroboscopic light stimulation (SLS) viewed with closed eyes provides a reliable, non-pharmacological method of inducing VHs in healthy populations. These hallucinations typically consist of vivid colours and dynamic geometric patterns that resemble simple VHs described in both psychedelic and clinical contexts, suggesting partially overlapping neural mechanisms. We developed and applied an unsupervised computer-vision pipeline to analyse a large dataset of 10,598 drawings made following exposure to hallucination-inducing SLS. These drawings were produced by attendees of Dreamachine, a large-scale public installation designed to elicit stroboscopically induced visual hallucinations (SIVHs). We extracted feature embeddings with a self-supervised deep vision transformer, then applied dimensionality reduction and density-based clustering to identify recurrent visual motifs in a data-driven manner. The majority of drawings contained geometric forms, consistent with prior observations of simple VHs under SLS. However, we also identified novel and underreported geometric formations, such as concentric squares, crosses, hyperbolic patterns, and other geometries. Our results show how an unsupervised computer-vision pipeline can organise large, openly shared phenomenological datasets into interpretable classes. By mapping the diversity of simple geometric VHs at scale, this work places new constraints on existing theoretical accounts and motivates targeted experimental work linking SLS parameters, neural dynamics, and geometric visual hallucinations.

Abstract Stroboscopic light stimulation (SLS) is a candidate non-pharmacological intervention that induces transient visual and affective experiences, with potential application in depression. Before efficacy testing, clinical development requires safety, tolerability and feasibility data. We report a staged, single-site programme in adults reporting depressive symptoms. Work Package (WP) 1 tested 11 SLS parameter sets for safety and tolerability. An interim bridge study assessed whether a low-phenomenology SLS control reduced subjective visual effects while preserving session context. WP2 randomised 84 participants to four weekly supervised 31-minute sessions of the intervention or a low-phenomenology control. In WP1, 31 participants were analysed; no severe adverse reactions occurred, mean discomfort was low (0.49/10), and the highest session-level upper 80% confidence limit was 1.13/10, well below the prespecified threshold. The interim study supported experiential separation between intervention and control. In WP2, endpoint data were available for 70/84 participants (83.3%): 39/42 in the intervention arm and 31/42 in the control arm. Overall retention met the criterion, but lower control-arm retention remains a design issue; protocol adherence was high, discomfort remained low, and no serious SLS-attributable adverse events occurred. Exploratory depressive-symptom changes suggested a possible BDI-II signal, but do not establish efficacy. Supervised SLS met key safety, tolerability, and feasibility criteria, and a lower visual-phenomenology active control can be carried forward, while masking and comparator credibility remain to be established. The next step is a diagnostically defined, CTU-governed Phase 2a feasibility trial that pre-registers a locked protocol and tests masking, credibility, retention and endpoint precision.

Across four studies (total n = 2,042, two preregistered, one partly preregistered), we investigated the degree to which experiences in psychological experiments may arise from participants controlling their experience to satisfy their goals, i.e., phenomenological control (PC). Trait PC has previously been shown to predict some experimental measures of changes in experience when demand characteristics are not controlled. Here, we investigated the reach of PC as a demand effect by contrasting relationships between Phenomenological Control Scale scores (PCS; a test of direct imaginative suggestion) and effects presumed to either be sensitive to beliefs (visually evoked auditory response or vEAR; tingling in the scalp or ASMR) or insensitive to beliefs (the Müller Lyer illusion; the vertical-horizontal illusion). PC accounted for much of the effect for the posited belief-sensitive effects but not for the classic visual illusions (Study 1). This pattern of results remained after controlling for context effects (Study 2) and measurement differences across procedures (Study 3). Finally, we present the first causal evidence that the two established psychological effects which are predicted by PC (vEAR and ASMR) can be modulated by participants high in PC in accordance with demand characteristics delivered by direct suggestion (Study 4). PC may account for the effects of psychological experiments when an effect is sensitive to belief, but not where it is not. This presents a previously unrecognised threat to validity in psychology experiments analogous to that of placebo effects in medicine.

Hallucinations are percepts that occur in the absence of corresponding sensory input, reflecting internally generated activity within sensory systems. Among visual hallucinations, geometric formations are common, yet their phenomenological structure remains poorly characterised. They occur across contexts ranging from psychedelic experiences to psychiatric and neurological disorders, likely involving related mechanisms within the early sensory cortices. Despite their relevance across disciplines, existing measures rely primarily on verbal reports or coarse rating scales, which do not capture geometric structure. To address this limitation, we developed and validated image recreation methods in which healthy participants were trained to recreate images of their hallucinations induced by stroboscopic light at frequencies known to elicit visual hallucinations under laboratory conditions. In Experiment 1 (N = 55), participants used a freehand drawing interface; in Experiment 2 (N = 54), a generative image recreation interface was employed. Training procedures mitigated individual differences in expressive ability. Validation trials demonstrated that geometric features were recoverable at the population level when image stimuli were used in place of hallucination-inducing stimuli. Across experiments, visual qualities of induced hallucinations varied systematically with stimulation frequency, consistent with prior reports. The data revealed a broader repertoire of geometric forms than typically described in the literature, including structures not predicted by current computational models of simple hallucinations. Applying these methods to other contexts could clarify the range of geometric hallucinations the visual system tends to produce under controlled perturbation across psychedelic, pathological, and laboratory settings. More precise characterisation of hallucination phenomenology may refine constraints on theoretical models of how visual experience is constructed in normal and altered states.

Hallucinations arise from disruptions in neural processes that construct perceptual experience from sensory input. Progress in consciousness science may therefore benefit from methods that precisely characterise their phenomenology. We introduce and validate the 6-Dimensional Visual Hallucination Questionnaire (6D-VHQ), a brief instrument designed to quantify visual hallucination content across contexts. The 6D-VHQ measures six dimensions: geometric and semantic content, detail level, vividness, entropy, and focality. It focuses on perceptual content rather than broader altered-state features. We report three studies: an image-based validation study, an application to stroboscopically induced visual hallucinations, and an application to psychedelic closed-eye visuals. Across 962 responses, the questionnaire showed high internal consistency, a factor structure aligned with the intended dimensions, and stable performance across settings. High inter-item redundancy indicated an initially over-complete but coherent implementation of the instrument. Compared with the 11-Dimensional Altered States of Consciousness questionnaire (11D-ASC), both instruments similarly captured simple and complex visual hallucinations despite differing psychometric properties. While the 11D-ASC showed low internal consistency, the 6D-VHQ has high internal consistency alongside high inter-dimensional redundancy. While the 11D-ASC correlates to participant traits, and 6D-VHQ is largely invariant to these, capturing the differences in visual phenomenology between different classes of hallucinatory experiences. The 6D-VHQ showed that stroboscopic stimulation predominantly elicited vivid simple geometric hallucinations, whereas psychedelic experiences showed combined geometric content alongside more vivid, detailed, and semantic visuals. The 6D-VHQ provides an operationalisation of a six-dimensional model of visual hallucination content that distinguishes broad classes of hallucinations across induction methods.

The integrated information theory of consciousness (IIT) is uniquely ambitious in proposing a mathematical formula, derived from apparently fundamental properties of conscious experience, to describe the quantity and quality of consciousness for any physical system that possesses it. IIT has generated considerable debate, which has engendered some misunderstandings and misrepresentations. Here we address and hope to remedy this. We begin by concisely summarising the essentials of IIT. Given IIT is supposed to apply universally, we do this with reference to an arbitrary patch of matter, as opposed to the usual system of discrete computational units. Then, after briefly summarising IIT's theoretical and empirical achievements, we focus on five points which we consider especially important for driving forward new theory and increasing understanding. First, a high value of the measure $Φ$ is not synonymous with `more consciousness'. We describe how $Φ$ might be replaced with a suite of quantities to obtain a multi-dimensional characterisation of states of consciousness. Second, we describe with nuance the distinct flavour of panpsychism implied by IIT -- whereby space (and time) are tiled with substrates of (proto-) consciousness -- and find this is not problematic for the theory. Third, $Φ$ is not well-defined for real physical systems, and has not been computed on any real physical system. Fourth, so far only proxies for IIT measures have been computed, and not approximations. Fifth, for IIT to fit with current successful theories in fundamental physics, a reformulation in terms of continuous fields would be needed.

Granger causality (GC) is widely used in neuroimaging to estimate directed statistical dependence between brain regions using time series of brain activity. A known problem is that fMRI measures brain activity indirectly via the blood-oxygen-level-dependent (BOLD) signal, which can distort GC estimates by introducing different time-to-peak responses across brain regions. However, how these distortions affect the validity of inferred connections is not fully understood. Previous studies have shown that false positives are not introduced if the haemodynamic response function (HRF) is minimum-phase; but whether the HRF is actually minimum-phase has remained contentious. Here, we address this issue by studying the transfer functions of three realistic biophysical models. We find that the minimum-phase condition is met for a wide range of physiologically plausible parameter values. Therefore, statistical testing of GC can be viable even if the HRF varies across brain regions, with the following two limitations. First, the minimum-phase condition is violated for parameter combinations that generate an initial dip in the HRF. Second, slow sampling of the BOLD signal (seconds) compared to the timescales of neural signal propagation (milliseconds) may still introduce spurious GC inferences. Beyond GC analysis, the closed-form expressions for the transfer functions of these popular HRF models are valuable for modeling fMRI time series since they balance mathematical tractability with biological plausibility.

ABSTRACT This study investigates the isolation and characterization of starch from green banana peels and preparation of biodegradable blend films thereof. Sodium metabisulfite was found more effective than sodium bisulfite in isolating starch, resulting in a higher yield. The extracted starch was verified through FTIR, XRD, SEM, and TGA analyses. Biodegradable blend film was prepared via solvent casting with the addition of 40 wt.% glycerol as plasticizer. FTIR analysis revealed good interactions between chitosan and green banana peel starch in the blend film. X‐ray diffraction, differential scanning calorimetry, and scanning electron microscopy confirmed that starch and chitosan in blend films displayed miscibility, interactions, and some microphase separation. Mechanical testing revealed that the film achieved an elongation at break of 76% when chitosan and starch were combined in equal proportions (50:50 by weight). Luminous transmittance decreased from 82% to 35% as starch content increased, while water absorption dropped from 89% to 61%. Soil burial tests demonstrated that the blend films exhibited greater biodegradability, as evidenced by higher weight loss and reduced tensile strength (0.53 MPa for CS4) compared to virgin chitosan film. Among the various formulations, the film containing 45% starch, 55% chitosan, and 40% glycerol (CS3) exhibited optimal performance, showcasing good mechanical strength, superior optical properties, effective water absorption, controlled swelling behavior, and improved biodegradability.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I'll instead make the case that consciousness depends on our nature as living organisms - a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them - we underestimate ourselves.

Our ability to understand and control complex systems of many interacting parts remains limited. A key challenge is that we still do not know how best to describe-and quantify-the many-to-many dynamical interactions that characterize their complexity. To address this limitation, we introduce the mathematical framework of Integrated Information Decomposition, or [Formula: see text]ID. [Formula: see text]ID provides a comprehensive framework to disentangle and characterize the information dynamics of complex multivariate systems. On the theoretical side, [Formula: see text]ID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer, information storage, and dynamical complexity as aggregates of these modes, thereby overcoming some of their known theoretical shortcomings. On the empirical side, we validate our theoretical results with computational models and examples from over 1,000 biological, social, physical, and synthetic dynamical systems. Altogether, [Formula: see text]ID improves our understanding of the behavior of widely used measures for characterizing complex systems across disciplines and leads to new more refined analyses of dynamical complexity.

Exposure to rapid and bright stroboscopic light has long been reported to induce vivid visual hallucinations of colour and geometric formations. This phenomenon was first documented by Purkinje over 200 years ago. Since then, significant progress has been made in understanding the effects of stroboscopic light and the experiences it induces through multiple waves of interest from the scientific, therapeutic, and broader cultural communities. Despite these advances, fundamental questions remain unanswered, including comprehensive characterizations of its phenomenology, its precise physiological origins, under which conditions it may lead to altered states of consciousness phenomena, and potential clinical or therapeutic applications. This narrative review provides a historical summary of research into stroboscopic light stimulation (SLS) alongside its use in recreation and lay-therapeutic contexts. It also discusses the phenomenology of these experiences, current perspectives on the potential neural mechanisms of stroboscopically induced experiences, and provides an outlook for future research in this field.

Abstract Hemispherotomy is a neurosurgical procedure for treating refractory epilepsy, which entails disconnecting a significant portion of the cortex, potentially encompassing an entire hemisphere, from its cortical and subcortical connections. While this intervention prevents the spread of seizures, it raises important questions. Given the complete isolation from sensory-motor pathways, it remains unclear whether the disconnected cortex retains any form of inaccessible awareness. More broadly, the activity patterns that large portions of the deafferented cortex can sustain in awake humans remain poorly understood. We address these questions by exploring for the first time the electrophysiological state of the isolated cortex before and after surgery in ten awake pediatric patients. Post-surgery, the isolated cortex exhibited prominent slow oscillations (<2 Hz) and a broad-band shift in power spectral density from high to low frequencies. This resulted in a marked decrease of the spectral exponent, a validated consciousness marker, indicating broad-band slowing characteristic of unconscious states. When compared with a reference pediatric sample across the sleep-wake cycle, the spectral exponent of the contralateral cortex aligned with wakefulness, whereas that of the isolated cortex was consistent with deep NREM sleep. However, spindles did not emerge in the isolated cortex due to the lack of subcortical inputs, constituting a fundamental difference from physiological sleep. These findings demonstrate a unihemispheric sleep-like state during wakefulness, challenging the possibility that hemispherotomy might lead to inaccessible “islands of awareness.” Moreover, the persistence of sleep-like patterns years after disconnection provides unique insights into the electrophysiological effects of disconnections in the human brain.

Abstract Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on context, we collected and comprehensively analysed the largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with functional MRI and EEG during rest and naturalistic stimuli (meditation, music, and movie), before and after ingesting 19 mg of psilocybin (functional MRI ≈80 min post-dose; EEG ≈150 min post-dose). Half the participants ranked the experience among the most meaningful of their lives. Under psilocybin, functional MRI and EEG signals recorded during eyes-closed conditions became similar to those recorded during an eyes-open condition. Global functional connectivity increased in associative regions and decreased in sensory areas. Using machine learning to represent neural activity as low-dimensional trajectories, we found that psilocybin reorganised these into structured, context-sensitive patterns of brain activity that reflected both experimental condition and the quality of subjective experience, revealing an organisation that was missed by time-averaged connectivity measures. Under psilocybin, brain networks that ordinarily segregate internal and external processing coherently integrated and aligned neural dynamics with context. This context-alignment manifested as distinct and cohesive neural trajectories in participants reporting positively felt self- and boundary-dissolving effects, corresponding to the felt experience of being part of the environment, which we refer to as embeddedness —the subjective experience of being continuous with, rather than separate from, the surrounding environment. The strength of this context-alignment was associated with next-day mindset change, bridging the neural, experiential, and therapeutic dimensions of the psychedelic state. These findings show that the organisation of brain activity covaries with the experiential coherence of the psychedelic state, and provide a systems-level framework for how context-sensitive brain dynamics link neurobiology to subjective experience and behavioural change.

Conscious experience depends on the coordinated activity of neural processes that span multiple scales--from synapses to whole-brain dynamics. A recently introduced measure, dynamical independence, identifies, characterises and quantifies these multi-scale relationships using an information-theoretic dimensionality-reduction approach. Here, we use DI to examine changes in emergent dynamical structure in the human brain under three pharmacologically-distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed EEG, our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. By contrast, ketamine, which preserves dream-like phenomenology, shows the opposite pattern: reduced overall emergence yet a partial preservation of the macroscopic structure, mirroring wake. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-specific reconfigurations of emergent dynamical structure under anaesthesia, dissociate the amount of emergence from the organisation of emergent dynamics, and caution against equating emergence with level of consciousness.

Abstract Stroboscopic light stimulation (SLS) on closed eyes reliably evokes vivid geometric visual phenomena, and in some contexts, altered-state experiences, leading to its increasing use across research, public installations, recreational use, and exploratory clinical contexts. The main clinical risk for SLS is a photosensitive epileptic response. In contrast, non-epileptic sensitivities (e.g., migraine/photophobia, anxiety, autism/ADHD, psychosis) typically induce discomfort or distress rather than convulsive events. Here, we integrate a multi-laboratory safety survey of 1,070 participants, operational data from two commercial SLS providers, and a focused review of SLS and sensory-intolerance evidence. This synthesis was used to refine safety screening and to characterise adverse events. Across laboratory studies, 20 minor side effects (e.g., early withdrawal due to discomfort; ∼18.7 per 1,000) were reported, with no severe incidents requiring medical attention. From two commercial datasets (∼4.2 million closed-eye SLS sessions), 14 major adverse events requiring medical attention were documented (∼3.3 per million); minor reactions were not systematically captured. Synthesising these strands, we developed the evidence-based Sussex Strobe Safety Screening Questionnaire (4SQ), which uses non-specialist language to identify known sensitivities to SLS, alongside practical recommendations for risk mitigation across research, clinical, commercial, and public contexts. Together, these findings suggest that the absolute risk under SLS is low but non-zero. Screening tools can help exclude pre-existing conditions, while graded exposure (e.g., a short ’taster’ session, after which participants may opt out) and trained staff can mitigate the risk of first-episode events. Nonetheless, such events should be anticipated and minimised through clear screening pathways and managed through rehearsed on-site response plans.

Hemispherotomy is a neurosurgical procedure for treating refractory epilepsy, which entails disconnecting a significant portion of the cortex, potentially encompassing an entire hemisphere, from its cortical and subcortical connections. While this intervention prevents the spread of seizures, it raises important questions. Given the complete isolation from sensory-motor pathways, it remains unclear whether the disconnected cortex retains any form of inaccessible awareness. More broadly, the activity patterns that large portions of the deafferented cortex can sustain in awake humans remain poorly understood. We address these questions by exploring for the first time the electroencephalographic (EEG) state of the isolated cortex during wakefulness before and after surgery in 10 pediatric patients, focusing on non-epileptic background activity. Post-surgery, the isolated cortex exhibited prominent slow oscillations (<2 Hz) and a steeper broad-band spectral decay, reflecting a redistribution of power toward lower frequencies. This broad-band EEG slowing resulted in a marked decrease of the spectral exponent, a validated consciousness marker, reaching values characteristic of deep anesthesia and the vegetative state. When compared with a reference pediatric sample across the sleep-wake cycle, the spectral exponent of the contralateral cortex aligned with wakefulness, whereas that of the isolated cortex was consistent with deep NREM sleep. The findings of prominent slow oscillations and broad-band slowing provisionally support inferences of absent or reduced awareness in the isolated cortex. Moreover, the persistence of unihemispheric sleep-like patterns years after surgery provides unique insights into the long-term electrophysiological effects of cortical disconnections in the human brain.

Complex neural systems can display structured emergent dynamics. Capturing this structure remains a significant scientific challenge. Using information theory, we apply Dynamical Independence (DI) to uncover the emergent dynamical structure in a minimal 5-node biophysical neural model, shaped by the interplay of two key aspects of brain organisation: integration and segregation. In our study, functional integration within the biophysical neural model is modulated by a global coupling parameter, while functional segregation is influenced by adding dynamical noise, which counteracts global coupling. Leveraging transfer entropy, DI defines a dimensionally-reduced macroscopic variable (e.g., a coarse-graining) as emergent to the extent that it behaves as an independent dynamical process, distinct from the micro-level dynamics. Dynamical dependence (a departure from dynamical independence) is measured by minimising the transfer entropy from microlevel variables to macroscopic variables across spatial scales. Our results indicate that the degree of emergence of macroscopic variables is relatively minimised at balanced points of integration and segregation and maximised at the extremes. Additionally, our method identifies to which degree the macroscopic dynamics are localised across microlevel nodes, thereby elucidating the emergent dynamical structure through the relationship between microscopic and macroscopic processes. We find that deviation from a balanced point between integration and segregation results in a less localised, more distributed emergent dynamical structure as identified by DI. This finding suggests that a balance of functional integration and segregation is associated with lower levels of emergence (higher dynamical dependence), which may be crucial for sustaining coherent, localised emergent macroscopic dynamical structures. This work also provides a complete computational implementation for the identification of emergent neural dynamics that could be applied both in silico and in vivo.

Active inference, a first-principles framework for modelling the behaviour of sentient agents, is beginning to be applied in consciousness research. One hypothesis arising from the framework is that active inference is necessary for changes in conscious content. As one component of an extensive adversarial collaboration among competing theories of consciousness, active inference will be contrasted with two other theories of consciousness, neither of which posit that active inference is necessary for consciousness. Here, we thus present a Study Protocol designed to test the active inference hypothesis using a carefully controlled adaptation of the motion-induced blindness paradigm, where an 'active' condition with richer active inference is contrasted with a 'passive' condition. In the active condition, participants direct their gaze towards a target stimulus following its disappearance from consciousness, and report on its subsequent reappearance. In the passive condition, participants maintain central fixation, while the stimulus array is moved across the visual field (in a replay of the active condition based on eye-tracking data acquired during active trials). In two experiments, we plan to investigate target reappearance across active and passive conditions to evaluate the contribution of active inference to conscious awareness. Results will eventually be considered in the context of all the experiments conducted as part of the overall adversarial collaboration.

Thomas Nagel’s paper “What is it like to be a bat?” is probably the best known, and best loved, of all papers on the philosophy of consciousness. It is almost impossible – and certainly unwise – to write, talk, or think about consciousness without referring to it. This is reason enough for republishing the paper half a century after its original appearance. But there are other reasons. What Nagel said is just as acute, and just as necessary, now as it was then. Taking its fiftieth anniversary as a cue to re- engage with it – alongside his more recent thoughts – is both a delight and a (re)-education. This short essay is an invited Afterword for the 50th Anniversary (Italian) Edition: Cosa si prova a essere un pipistrello?, Raffaello Cortina Editore, Milano, 2025.

Individuals often exhibit decision biases when making forced-choice decisions, but are such biases under cognitive control? In this pre-registered study, we asked whether decision biases in a simple perceptual task can be reduced on instruction. Furthermore, we asked whether intentionally reducing decision biases improves metacognitive performance – the ability to infer one’s own choice accuracy. 69 participants performed an online numerosity discrimination task in which participants had to report whether there were more horizontal or more vertical lines presented in an imaginary grid, followed by subjective decision confidence. “Baseline” decision bias was defined as the participants’ decision bias after one block. Participants in the experimental group were informed of their bias in the first half of the experiment and were then asked to reduce that bias in the second half. Their behaviour was compared to that of participants from both an active control group (told to reduce their error rate, rather than bias) and a passive one (no instruction). Results showed that, relative to the passive control, decision bias can indeed be reduced on instruction, and that this occurs in a non-trivial fashion. Though participants in the active control group also reduced their bias (without explicit instruction to do so), analyses revealed that the decision strategy underlying this behaviour differed substantially from that of the experimental group. No effects on metacognitive performance were found. Altogether these results suggest that for low-stakes decisions, people can control their intrinsic decision biases once they are informed of them, regardless of how well they can infer their decision accuracy.

Individuals often exhibit decision biases when making forced-choice decisions, but are such biases under cognitive control? In this pre-registered study, we asked whether decision biases in a simple perceptual task can be reduced on instruction. Furthermore, we asked whether intentionally reducing decision biases improves metacognitive performance – the ability to infer one’s own choice accuracy. 69 participants performed an online numerosity discrimination task in which participants had to report whether there were more horizontal or more vertical lines presented in an imaginary grid, followed by subjective decision confidence. “Baseline” decision bias was defined as the participants’ decision bias after one block. Participants in the experimental group were informed of their bias in the first half of the experiment and were then asked to reduce that bias in the second half. Their behaviour was compared to that of participants from both an active control group (told to reduce their error rate, rather than bias) and a passive one (no instruction). Results showed that, relative to the passive control, decision bias can indeed be reduced on instruction, and that this occurs in a non-trivial fashion. Though participants in the active control group also reduced their bias (without explicit instruction to do so), analyses revealed that the decision strategy underlying this behaviour differed substantially from that of the experimental group. No effects on metacognitive performance were found. Altogether these results suggest that for low-stakes decisions, people can control their intrinsic decision biases once they are informed of them, regardless of how well they can infer their decision accuracy.

Across four studies (total n = 2,042, two preregistered, one partly preregistered), we investigated the degree to which experiences in psychological experiments may arise from participants controlling their experience to satisfy their goals, i.e., phenomenological control (PC). Trait PC has previously been shown to predict some experimental measures of changes in experience when demand characteristics are not controlled. Here, we investigated the reach of PC as a demand effect by contrasting relationships between Phenomenological Control Scale scores (PCS; a test of direct imaginative suggestion) and effects presumed to either be sensitive to beliefs (visually evoked auditory response or vEAR; tingling in the scalp or ASMR) or insensitive to beliefs (the Müller Lyer illusion; the vertical-horizontal illusion). PC accounted for much of the effect for the posited belief-sensitive effects but not for the classic visual illusions (Study 1). This pattern of results remained after controlling for context effects (Study 2) and measurement differences across procedures (Study 3). Finally, we present the first causal evidence that two established psychological effects which are predicted by PC (vEAR and ASMR) can be modulated by participants high in PC in accordance with demand characteristics delivered by direct suggestion (Study 4). PC may account for the effects of psychological experiments when an effect is sensitive to belief, but not where it is not. This presents a previously unrecognised threat to validity in psychology experiments analogous to that of placebo effects in medicine.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate our selves.

Understanding the biophysical basis of consciousness remains a substantial challenge for 21st-century science. This endeavor is becoming even more pressing in light of accelerating progress in artificial intelligence and other technologies. In this article, we provide an overview of recent developments in the scientific study of consciousness and consider possible futures for the field. We highlight how several novel approaches may facilitate new breakthroughs, including increasing attention to theory development, adversarial collaborations, greater focus on the phenomenal character of conscious experiences, and the development and use of new methodologies and ecological experimental designs. Our emphasis is forward-looking: we explore what “success” in consciousness science may look like, with a focus on clinical, ethical, societal, and scientific implications. We conclude that progress in understanding consciousness will reshape how we see ourselves and our relationship to both artificial intelligence and the natural world, usher in new realms of intervention for modern medicine, and inform discussions around both nonhuman animal welfare and ethical concerns surrounding the beginning and end of human life.

Recent findings have shown that psychedelics reliably enhance brain entropy (understood as neural signal diversity), and this effect has been associated with both acute and long-term psychological outcomes, such as personality changes. These findings are particularly intriguing, given that a decrease of brain entropy is a robust indicator of loss of consciousness (e.g., from wakefulness to sleep). However, little is known about how context impacts the entropy-enhancing effect of psychedelics, which carries important implications for how it can be exploited in, for example, psychedelic psychotherapy. This article investigates how brain entropy is modulated by stimulus manipulation during a psychedelic experience by studying participants under the effects of lysergic acid diethylamide (LSD) or placebo, either with gross state changes (eyes closed vs open) or different stimuli (no stimulus vs music vs video). Results show that while brain entropy increases with LSD under all of the experimental conditions, it exhibits the largest changes when subjects have their eyes closed. Furthermore, brain entropy changes are consistently associated with subjective ratings of the psychedelic experience, but this relationship is disrupted when participants are viewing a video─potentially due to a "competition" between external stimuli and endogenous LSD-induced imagery. Taken together, our findings provide strong quantitative evidence of the role of context in modulating neural dynamics during a psychedelic experience, underlining the importance of performing psychedelic psychotherapy in a suitable environment.

In daily life, we can not only estimate confidence in our inferences ('I'm sure I failed that exam'), but can also estimate whether those feelings of confidence are good predictors of decision accuracy ('I feel sure I failed, but my feeling is probably wrong; I probably passed'). In the lab, by using simple perceptual tasks and collecting trial-by-trial confidence ratings visual metacognition research has repeatedly shown that participants can successfully predict the accuracy of their perceptual choices. Can participants also successfully evaluate 'confidence in confidence' in these tasks? This is the question addressed in this study. Participants performed a simple, two-interval forced choice numerosity task framed as an exam. Confidence judgements were collected in the form of a 'predicted exam grade'. Finally, we collected 'meta-metacognitive' reports in a two-interval forced-choice design: trials were presented in pairs, and participants had to select that in which they thought their confidence (predicted grade) best matched their accuracy (actual grade), effectively minimizing their quadratic scoring rule (QSR) score. Participants successfully selected trials on which their metacognition was better when metacognitive performance was quantified using area under the type 2 ROC (AUROC2) but not when using the 'gold-standard' measure m-ratio. However, further analyses suggested that participants selected trials on which AUROC2 is lower in part via an extreme-confidence heuristic, rather than through explicit evaluation of metacognitive inferences: when restricting analyses to trials on which participants gave the same confidence rating AUROC2 no longer differed as a function of selection, and likewise when we excluded trials on which extreme confidence ratings were given. Together, our results show that participants are able to make effective metacognitive discriminations on their visual confidence ratings, but that explicit 'meta-metacognitive' processes may not be required.

Abstract Complex neural systems can display structured emergent dynamics. Capturing this structure remains a significant scientific challenge. Using information theory, we apply Dynamical Independence (DI) to uncover the emergent dynamical structure in a minimal 5-node biophysical neural model, shaped by the interplay of two key aspects of brain organisation: integration and segregation. In our study, functional integration within the biophysical neural model is modulated by a global coupling parameter, while functional segregation is influenced by adding dynamical noise, which counteracts global coupling. DI defines a dimensionally-reduced macroscopic variable (e.g., a coarse-graining) as emergent to the extent that it behaves as an independent dynamical process, distinct from the micro-level dynamics. We measure dynamical dependence (a departure from dynamical independence) for macroscopic variables across spatial scales. Our results indicate that the degree of emergence of macroscopic variables is relatively minimised at balanced points of integration and segregation and maximised at the extremes. Additionally, our method identifies to which degree the macroscopic dynamics are localised across microlevel nodes, thereby elucidating the emergent dynamical structure through the relationship between microscopic and macroscopic processes. We find that deviation from a balanced point between integration and segregation results in a less localised, more distributed emergent dynamical structure as identified by DI. This finding suggests that a balance of functional integration and segregation is associated with lower levels of emergence (higher dynamical dependence), which may be crucial for sustaining coherent, localised emergent macroscopic dynamical structures. This work also provides a complete computational implementation for the identification of emergent neural dynamics that could be applied both in silico and in vivo. Author summary Understanding how complex neural systems give rise to emergent macroscopic patterns is a central challenge in neuroscience. Emergence, where macroscopic structures appear from underlying microscopic interactions, plays a crucial role in brain function, yet identifying the specific dynamics involved remains elusive. Traditionally, methods have quantified the extent of emergence but have struggled to pinpoint the emergent dynamical structure itself. In this study, we develop and apply a method, based on a quantity called Dynamical Independence (DI), which simultaneously captures the extent of emergence and reveals the underlying dynamical structure in neurophysiological data. Using a minimal 5-node biophysical neural model, we explore how a balance between functional integration and segregation—two key organisational principles in the brain—affects emergent macroscopic dynamics. Our results show that a finely balanced system produces highly localised, coherent macroscopic structures, while extreme deviations lead to more distributed, less localised dynamics. This work provides a computational framework for identifying emergent dynamical structure in both theoretical models and potentially in empirical brain data, advancing our understanding of the brain’s complex organisation across higher-order scales.

Abstract According to sensorimotor accounts of perceptual experience, the subjective veridicality of an object (the sense of its ‘presence’) builds up gradually as one learns how changes in sensory inputs depend on bodily movements. To investigate how sensorimotor interactions shape visual experience, we designed a virtual-reality-based study that allowed us to manipulate the complexity of spatial dependencies governing interactions with unfamiliar 3D objects. Participants had to learn to manually control fully visible objects that could move in congruent, opposite, novel (orthogonal), or random directions in response to their movements. The sensorimotor control tasks occurred alternately with a continuous flash suppression (CFS) task evaluating the access of stationary objects to visual awareness, operationalised as the time taken for a 3D object to break the interocular suppression. We hypothesised that objects whose motion was experienced as depending on actions in a lawful, and thus encodable, manner (i.e., according to a congruent, opposite, or novel – but not random – dependency) would overcome suppression faster than objects moving randomly in response to actions (for which there is no world-related statistical structure to learn). However, while performance in the sensorimotor tasks consistently decreased along with the difficulty of the conditions (i.e., congruent &gt; opposite &gt; novel &gt; random), the pre-registered analysis yielded no significant differences in breakthrough times of objects manipulated under different coupling rules. An exploratory analysis assessing whether the acquisition of ‘sensorimotor mastery’ was associated with reduced breakthrough times also revealed no significant effects. Thus, our results suggest that one’s knowledge of how an object responds to action does not play a salient role in determining conscious access to visual stimuli. This extends previous evidence for a general ineffectiveness of sensorimotor spatial manipulations in interocular suppression paradigms. Notably, in all conditions, object movement remained tightly coupled (i.e., contingent) to the participant’s actions – and given such stimuli have already been shown to break suppression faster than uncoupled/pre-recorded visual inputs – it is possible that sensorimotor contingency was a sufficiently salient factor to override any influences related to how identifiable specific spatial dependencies were. Highlights Learning sensorimotor control over objects was examined in novel VR tasks. We varied the complexity of spatial dependencies between manual and visual rotations. Controllable objects were expected to enter visual awareness faster in b-CFS. No differences in breakthrough times for differently complex spatial coupling rules. Sensorimotor contingency outweighs spatial congruence in affecting visual awareness.

Brain-computer interfaces (BCIs) enable direct communication between the brain and external computers, allowing processing of brain activity and the ability to control external devices. While often used for medical purposes, BCIs may also hold great promise for nonmedical purposes to unlock human neurocognitive potential. In this Essay, we discuss the prospects and challenges of using BCIs for cognitive enhancement, focusing specifically on invasive enhancement BCIs (eBCIs). We discuss the ethical, legal, and scientific implications of eBCIs, including issues related to privacy, autonomy, inequality, and the broader societal impact of cognitive enhancement technologies. We conclude that the development of eBCIs raises challenges far beyond practical pros and cons, prompting fundamental questions regarding the nature of conscious selfhood and about who-and what-we are, and ought, to be.

<title>Abstract</title> Hemispherotomy is a surgical procedure that disconnects a large portion of the cerebral cortex from cortical and subcortical inputs in patients with severe refractory epilepsy. Whether the disconnected cortex - inaccessible to behavioral assessment - supports consciousness remains unknown. Functional MRI studies have indicated preserved resting-state networks within the disconnected hemisphere, raising the possibility that it may represent an ‘island of awareness’. However, these networks can also persist in unconscious states, such as anesthesia and deep sleep. Here we assess the capacity of the disconnected cortex to support consciousness by exploring its electrophysiological state, before and after hemispherotomy, in ten awake pediatric patients. After surgery, the disconnected cortex–but not the contralateral cortex–entered a state dominated by slow oscillations (&lt;2 Hz) resembling those observed during deep sleep; further, the spectral exponent, a previously validated marker of consciousness indexing the 1/f-like decay of the power spectral density, assumed values typically found in unconscious brain-injured and anesthetized adults. When compared to a reference pediatric sample, spectral exponent values were compatible with wakefulness in the contralateral cortex but attained levels typical of deep sleep over the disconnected cortex, suggesting that the disconnected cortex is not an island of awareness.

Exposure to rapid and bright stroboscopic light has long been reported to induce vivid visual hallucinations of colour and geometric formations. This phenomenon was first documented by Purkinje over 200 years ago. Since then, significant progress has been made in understanding the effects of stroboscopic light and the experiences it induces through multiple waves of interest from the scientific, therapeutic, and broader cultural communities. Despite these advances, fundamental questions remain unanswered, including comprehensive characterisations of its phenomenology, its precise physiological origins, under which conditions it may lead to altered states of consciousness phenomena, and potential clinical or therapeutic applications. This narrative review provides a historical summary of research into stroboscopic light stimulation alongside its use in recreation and lay-therapeutic contexts. It also discusses the phenomenology of these experiences, current perspectives on the potential neural mechanisms of stroboscopically induced experiences, and provides an outlook for future research in this field.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent, but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is, or convincingly appears to be, conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate our selves.

Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differences between VHs that have different aetiologies, such as those arising from Neurodegenerative conditions, visual loss, or psychedelic compounds. Here, we examine the potential mechanistic basis of these differences by leveraging recent advances in visualising the learned representations of a coupled classifier and generative deep neural network-an approach we call 'computational (neuro)phenomenology'. Examining three aetiologically distinct populations in which VHs occur-Neurodegenerative conditions (Parkinson's Disease and Lewy Body Dementia), visual loss (Charles Bonnet Syndrome, CBS), and psychedelics-we identified three dimensions relevant to distinguishing these classes of VHs: realism (veridicality), dependence on sensory input (spontaneity), and complexity. By selectively tuning the parameters of the visualisation algorithm to reflect influence along each of these phenomenological dimensions we were able to generate 'synthetic VHs' that were characteristic of the VHs experienced by each aetiology. We verified the validity of this approach experimentally in two studies that examined the phenomenology of VHs in Neurodegenerative and CBS patients, and in people with recent psychedelic experience. These studies confirmed the existence of phenomenological differences across these three dimensions between groups, and crucially, found that the appropriate synthetic VHs were rated as being representative of each group's hallucinatory phenomenology. Together, our findings highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience.

Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels; however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalism that articulates how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework establishes a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled.

Biological neural networks can perform complex computations to predict their environment, far above the limited predictive capabilities of individual neurons. While conventional approaches to understanding these computations often focus on isolating the contributions of single neurons, here we argue that a deeper understanding requires considering emergent dynamics - dynamics that make the whole system "more than the sum of its parts". Specifically, we examine the relationship between prediction performance and emergence by leveraging recent quantitative metrics of emergence, derived from Partial Information Decomposition, and by modelling the prediction of environmental dynamics in a bio-inspired computational framework known as reservoir computing. Notably, we reveal a bidirectional coupling between prediction performance and emergence, which generalises across task environments and reservoir network topologies, and is recapitulated by three key results: 1) Optimising hyperparameters for performance enhances emergent dynamics, and vice versa; 2) Emergent dynamics represent a near sufficient criterion for prediction success in all task environments, and an almost necessary criterion in most environments; 3) Training reservoir computers on larger datasets results in stronger emergent dynamics, which contain task-relevant information crucial for performance. Overall, our study points to a pivotal role of emergence in facilitating environmental predictions in a bio-inspired computational architecture.

Abstract Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differences between VHs that have different aetiologies, such as those arising from neurological conditions, visual loss, or psychedelic compounds. Here, we examine the potential mechanistic basis of these differences by leveraging recent advances in visualising the learned representations of a coupled classifier and generative deep neural network – an approach we call ‘computational (neuro)phenomenology’. Examining three aetiologically distinct populations in which VHs occur - neurological conditions (Parkinson’s Disease and Lewy Body Dementia), visual loss (Charles Bonnet Syndrome, CBS), and psychedelics - we identify three dimensions relevant to distinguishing these classes of VHs: realism (veridicality), dependence on sensory input (spontaneity), and complexity. By selectively tuning the parameters of the visualisation algorithm to reflect influence along each of these phenomenological dimensions we were able to generate ‘synthetic VHs’ that were characteristic of the VHs experienced by each aetiology. We verified the validity of this approach experimentally in two studies that examined the phenomenology of VHs in neurological and CBS patients, and in people with recent psychedelic experience. These studies confirmed the existence of phenomenological differences across these three dimensions between groups, and crucially, found that the appropriate synthetic VHs were representative of each group’s hallucinatory phenomenology. Together, our findings highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience.

Abstract Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models —which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states.

We introduce a notion of emergence for macroscopic variables associated with highly multivariate microscopic dynamical processes. Dynamical independence instantiates the intuition of an emergent macroscopic process as one possessing the characteristics of a dynamical system "in its own right," with its own dynamical laws distinct from those of the underlying microscopic dynamics. We quantify (departure from) dynamical independence by a transformation-invariant Shannon information-based measure of dynamical dependence. We emphasize the data-driven discovery of dynamically independent macroscopic variables, and introduce the idea of a multiscale "emergence portrait" for complex systems. We show how dynamical dependence may be computed explicitly for linear systems in both time and frequency domains, facilitating discovery of emergent phenomena across spatiotemporal scales, and outline application of the linear operationalization to inference of emergence portraits for neural systems from neurophysiological time-series data. We discuss dynamical independence for discrete- and continuous-time deterministic dynamics, with potential application to Hamiltonian mechanics and classical complex systems such as flocking and cellular automata.

Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising "prediction errors"-the differences between predicted and observed data. Implicit in this proposal is the idea that successful perception requires multiple cycles of neural activity. This is at odds with evidence that several aspects of visual perception-including complex forms of object recognition-arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity. Here, we propose that the feedforward sweep can be understood as performing amortized inference (applying a learned function that maps directly from data to beliefs) and recurrent processing can be understood as performing iterative inference (sequentially updating neural activity in order to improve the accuracy of beliefs). We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner by describing both in terms of a dual optimization of a single objective function. We show that the resulting scheme can be implemented in a biologically plausible neural architecture that approximates Bayesian inference utilising local Hebbian update rules. We demonstrate that our hybrid predictive coding model combines the benefits of both amortized and iterative inference-obtaining rapid and computationally cheap perceptual inference for familiar data while maintaining the context-sensitivity, precision, and sample efficiency of iterative inference schemes. Moreover, we show how our model is inherently sensitive to its uncertainty and adaptively balances iterative and amortized inference to obtain accurate beliefs using minimum computational expense. Hybrid predictive coding offers a new perspective on the functional relevance of the feedforward and recurrent activity observed during visual perception and offers novel insights into distinct aspects of visual phenomenology.

Perception can be shaped by our expectations, which can lead to perceptual illusions. Similarly, long-term memories can be shaped to fit our expectations, which can generate false memories. However, it is generally assumed that short-term memory for percepts formed just 1 or 2 seconds ago accurately represents the percepts as they were at the time of perception. Here 4 experiments consistently show that within this timeframe, participants go from reliably reporting what was there (perceptual inference accurately reflecting the bottom-up input), to erroneously but with high confidence reporting what they expected to be there (memory report strongly influenced by top-down expectations). Together, these experiments show that expectations can reshape perceptual representations over short time scales, leading to what we refer to as short-term memory (STM) illusions. These illusions appeared when participants saw a memory display which contained real and pseudo-letters (i.e. mirrored letters). Within seconds after the memory display disappeared, high confidence memory errors increased substantially. This increase in errors over time indicates that the high confidence errors do not (purely) result from incorrect perceptual encoding of the memory display. Moreover, high confidence errors occurred mainly for pseudo-to-real letter memories, and much less often for real-to-pseudo-letter memories, indicating that visual similarity is not the primary cause of this memory-bias. Instead 'world knowledge' (e.g., which orientation letters usually have) appear to drive these STM illusions. Our findings support a predictive processing view of the formation and maintenance of memory in which all memory stages, including STM, involve integration of bottom-up memory input with top-down predictions, such that prior expectations can shape memory traces.

Synaesthesia is a condition defined by additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. The associative nature of synaesthesia has motivated attempts to induce synaesthesia by means of associative learning. Two recent studies of this kind highlighted the potential for perceptual plasticity even in adulthood, by demonstrating that extensive associative training can generate not only behavioural and neurophysiological markers of synaesthesia, but also synaesthesia-like phenomenology. However, while the results of these studies provided tantalising evidence that a learning component may be involved in the development of synesthetic phenomenology, they only provided superficial descriptions regarding the training-related changes in induced synaesthesia-like (Induced) experience. Therefore, it was not possible to assess how closely the phenomenology of Induced and naturally occurring grapheme-colour synaesthesia (Lifelong) overlap. Here, we addressed this question by providing a new qualitative analysis, using grounded theory, of the phenomenological changes associated with learning new perceptual phenomenology (Induced group) and comparing the descriptive similarities in colour experience to equivalent qualitative data acquired from a new group of Lifelong participants. Using this approach, we were able to directly compare associated colour experiences between the Induced and Lifelong group to assess how closely these two types of novel perceptual experience align. Our results reveal that induced and synaesthetic experience are remarkably similar, displaying a high degree of phenomenological overlap across multiple experiential categories, including: stability of experience, location of colour experience, shape of co-occurring colour experience, relative strength of colour experience and automaticity of colour experience. Our results exemplify the benefits of qualitative methods by providing new evidence that intensive training of letter-colour associations can alter conscious perceptual experiences in non-synaesthetes, and that such alterations produce synaesthesia-like phenomenology, which substantially resembles experiences described in natural grapheme-colour synaesthesia. Our results have implications for the plasticity of visual perception and the role of learning and development in establishing perceptual traits.

Voluntary actions are associated with sense of agency and distortions of perceived duration. This study examined how sensorimotor coupling and visual perspective modulate perceived duration and sense of agency during manual actions in four pre-registered experiments using virtual reality. Participants moved their hands while observing a virtual hand moving (a)synchronously, and evaluated movement duration and their sense of agency. We found that sensorimotor coupling provided by synchronous visual feedback is associated with longer perceived duration relative to action-related time compression, compared with delayed feedback or pre-recorded movements of another person (Experiments 1 and 2). Sensorimotor coupling also modulated sense of agency and may serve as a shared basis for perceived time and agency. Comparable modulations across first- and third-person perspectives (Experiments 1 and 2), anatomical configurations (Experiment 3), and attention on external entities (Experiment 4) suggest a shared predictive system for sensory consequences of self-generated actions and others’ reactions. This modulation was observed irrespective of anatomical configuration and when attention was directed toward objects unrelated to one’s own body, suggesting that the underlying mechanism is not confined to representations specific to the self, but may instead reflect domain-general predictive computations. These findings, together with the virtual reality technique developed, offer new insights into how humans experience passage of time and agency during voluntary action.

In daily life, we can not only estimate confidence in our inferences (“I’m sure I failed that exam”), but also estimate whether those feelings of confidence are good predictors of decision accuracy (“I feel sure I failed, but my feeling is probably wrong; I probably passed”). In the lab, visual metacognition research has repeatedly shown, using simple perceptual tasks and collecting trial-by-trial confidence ratings, that participants can successfully predict the accuracy of their perceptual choices. On these tasks, can participants also successfully evaluate “confidence in confidence”? This is the question addressed in this study. Participants performed a simple, two interval forced choice numerosity task framed as an exam. Confidence judgements were collected in the form of a ‘predicted exam grade’. Finally, we collected ‘meta metacognitive’ reports in a two-interval forced-choice design: Trials were presented in pairs, and participants had to select that in which they thought their confidence (predicted grade) best matched their accuracy (actual grade), effectively minimising their QSR (quadratic scoring rule) score. Participants successfully selected trials on which their metacognition was better when metacognitive performance was quantified using area under the type 2 ROC (AUROC2) but not when using the ‘gold standard’ measure m-ratio. However, further analyses suggested that participants selected trials on which AUROC2 is lower in part via an extreme-confidence heuristic, rather than through explicit evaluation of metacognitive inferences: when restricting analyses to trials on which participants gave the same confidence rating AUROC2 no longer differed as a function of selection, and likewise when we excluded trials on which extreme confidence ratings were given. Together, our results show that participants are able to make effective metacognitive discriminations on their visual confidence ratings, but that explicit ‘meta-metacognitive’ processes may not be required.

Phenomenological control is the stable trait ability to alter subjective experience in accordance with goals. Although voluntary, phenomenological control is experienced as involuntary and has predominantly been studied within the context of hypnosis, in which direct verbal imaginative suggestions for a range of experiences are given by a designated ‘hypnotist’ (e.g., involuntary movement, paralysis, analgesia, amnesia or auditory, gustatory, tactile and visual hallucinations). However, hypnosis is not required for phenomenological control and the ability can be exercised in a variety of contexts, including in response to demand characteristics in scientific experiments. Here we trace the modern history of phenomenological control from the context of mesmerism in 18th century (which employed indirect, non-verbal suggestion) through to contemporary accounts. We focus on three theories: response expectancy theory (in which experience arises directly from expectancies), cold control theory (in which voluntary acts are experienced as involuntary due to being unaware of relevant intentions) and the predictive processing theory of hypnosis (in which voluntary acts arise from aberrant interplay of top-down predictions and bottom-up prediction error signals). We consider the pros and cons of each and explore the extent to which predictive processing might be extended to offer a full-fledged theory of phenomenological control.

Phenomenological control is the stable trait ability to alter subjective experience in accordance with goals. Although voluntary, phenomenological control is experienced as involuntary and has predominantly been studied within the context of hypnosis, in which direct verbal imaginative suggestions for a range of experiences are given by a designated ‘hypnotist’ (e.g., involuntary movement, paralysis, analgesia, amnesia or auditory, gustatory, tactile and visual hallucinations). However, hypnosis is not required for phenomenological control and the ability can be exercised in a variety of contexts, including in response to demand characteristics in scientific experiments. Here we trace the modern history of phenomenological control from the context of mesmerism in the 18th century (which employed indirect, non-verbal suggestion) through to contemporary accounts. We focus on three theories: response expectancy theory (in which experience arises directly from expectancies), cold control theory (in which voluntary acts are experienced as involuntary due to being unaware of relevant intentions) and the predictive processing (PP) theory of hypnosis (in which voluntary acts arise from aberrant interplay of top-down predictions and bottom-up prediction error signals). We consider the pros and cons of each and explore the extent to which PP might be extended to offer a full-fledged theory of phenomenological control.

Granger Causality (GC) is widely used in neuroimaging to estimate directed statistical dependence among brain regions using time series of brain activity. An important issue is that functional MRI (fMRI) measures brain activity indirectly via the blood-oxygen-level-dependent (BOLD) signal, which affects the temporal structure of the signals and distorts GC estimates. However, some notable applications of GC are not concerned with the GC magnitude but its statistical significance. This is the case for network inference, which aims to build a statistical model of the system based on directed relationships among its elements. The critical question for the viability of network inference in fMRI is whether the hemodynamic response function (HRF) and its variability across brain regions introduce spurious relationships, i.e., statistically significant GC values between BOLD signals, even if the GC between the neuronal signals is zero. It has been mathematically proven that such spurious statistical relationships are not induced if the HRF is minimum-phase, i.e., if both the HRF and its inverse are stable (producing finite responses to finite inputs). However, whether the HRF is minimum-phase has remained contentious. Here, we address this issue using multiple realistic biophysical models from the literature and studying their transfer functions. We find that these models are minimum-phase for a wide range of physiologically plausible parameter values. Therefore, statistical testing of GC is plausible even if the HRF varies across brain regions, with the following limitations. First, the minimum-phase condition is violated for parameter combinations that generate an initial dip in the HRF, confirming a previous mathematical proof. Second, the slow sampling of the BOLD signal (in seconds) compared to the timescales of neural signal propagation (milliseconds) may still introduce spurious GC.

Data and analysis code accompanying the study on sensorimotor spatial dependencies and conscious access to virtual 3D objects. Includes raw and preprocessed behavioral data, R analysis scripts, and R Markdown reports.

Data and analysis code accompanying the study on sensorimotor spatial dependencies and conscious access to virtual 3D objects. Includes raw and preprocessed behavioral data, R analysis scripts, and R Markdown reports.

Abstract Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models —which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states.

Abstract Quantifying the complexity of neural activity has provided fundamental insights into cognition, consciousness, and clinical conditions. However, the most widely used approach to estimate the complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that substantially restrict its domain of applicability. In this article we leverage the information-theoretic foundations of LZ to overcome these limitations by introducing a complexity estimator based on state-space models —which we dub Complexity via State-space Entropy Rate (CSER). While having a performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial advantages: 1) CSER offers a principled decomposition into spectral components, which allows us to rigorously investigate the relationship between complexity and spectral power; and 2) CSER provides a temporal resolution two orders of magnitude better than LZ, which allows complexity analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver of complexity changes across states of consciousness; and reveals early entropy increases that precede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms. Overall, by overcoming the main limitations of LZ and substantially extending its range of applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal structure of the signal complexity associated with cognitive processes and conscious states.

Abstract This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.

This paper considers the phenomenology of depersonalisation disorder, in relation to predictive processing and its associated pathophysiology. To do this, we first establish a few mechanistic tenets of predictive processing that are necessary to talk about phenomenal transparency, mental action, and self as subject. We briefly review the important role of 'predicting precision' and how this affords mental action and the loss of phenomenal transparency. We then turn to sensory attenuation and the phenomenal consequences of (pathophysiological) failures to attenuate or modulate sensory precision. We then consider this failure in the context of depersonalisation disorder. The key idea here is that depersonalisation disorder reflects the remarkable capacity to explain perceptual engagement with the world via the hypothesis that "I am an embodied perceiver, but I am not in control of my perception". We suggest that individuals with depersonalisation may believe that 'another agent' is controlling their thoughts, perceptions or actions, while maintaining full insight that the 'other agent' is 'me' (the self). Finally, we rehearse the predictions of this formal analysis, with a special focus on the psychophysical and physiological abnormalities that may underwrite the phenomenology of depersonalisation.

The extent to which high-level, complex functions can proceed unconsciously has been a topic of considerable debate. While unconscious processing has been demonstrated for a range of low-level processes, from feature integration to simple forms of conditioning and learning, theoretical contributions suggest that increasing complexity requires conscious access. Here, we focus our attention on instrumental conditioning, which has been previously shown to proceed without stimulus awareness. Yet, instrumental conditioning also involves integrating information over a large temporal scale and distinct modalities in order to deploy selective action, constituting a process of substantial complexity. With this in mind, we revisit the question of feasibility of instrumental conditioning in the unconscious domain. Firstly, we address the theoretical and practical considerations relevant to unconscious learning in general. Secondly, we aim to replicate the first study to show instrumental conditioning in the absence of stimulus awareness (Pessiglione et al., 2008), following the original design and supplementing the original crucial analyses with a Bayesian approach (Experiment 1). We found that apparent unconscious learning took place when replicating the original methods directly and according to the tests of awareness used. However, we could not establish that the full sample was unaware in a separate awareness check. We therefore attempted to replicate the effect yet again with improved methods to address the issues related to sensitivity and immediacy (Experiment 2), including an individual threshold-setting task and a trial-by-trial awareness check permitting exclusion of individual aware trials. Here, we found evidence for absence of unconscious learning. This result provides evidence that instrumental conditioning did not occur without stimulus awareness in this paradigm, supporting the view that complex forms of learning may rely on conscious access. Our results provides support for the proposal that perceptual consciousness may be necessary for complex, flexible processes, especially where selective action and behavioural adaptation are required.

Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.

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Bruineberg and colleagues helpfully distinguish between instrumental and ontological interpretations of Markov blankets, exposing the dangers of using the former to make claims about the latter. However, proposing a sharp distinction neglects the value of recognising a continuum spanning from instrumental to ontological. This value extends to the related distinction between "being" and "having" a model.

Abstract The major theories of consciousness that distinguish conscious from unconscious states can be grouped into two main classes, either higher-order or integration theories. There is evidence that different types of mental states can be unconscious, though that conclusion depends on the theory of consciousness assumed. Unconscious memory (in the sense of the influence of a prior event not recollected) can shape perception and liking and control our behavior. Subliminal perception can produce semantic priming and guide attention and decision making; and optical variables that a person describes incorrectly can guide action. Implicit learning can shape judgments and choices in complex environments. Unconscious intentions can allow people to respond appropriately in goal-directed ways while the person experiences the actions as involuntary. Unconscious attitudes are no more or less plausible than any other mental state being unconscious, but it has been hard to obtain evidence for unconscious attitudes as distinct from gut reactions one does not agree with.

We report the results of an academic survey into the theoretical and methodological foundations, common assumptions, and the current state of the field of consciousness research. The survey consisted of 22 questions and was distributed on two different occasions of the annual meeting of the Association of the Scientific Study of Consciousness (2018 and 2019). We examined responses from 166 consciousness researchers with different backgrounds (e.g. philosophy, neuroscience, psychology, and computer science) and at various stages of their careers (e.g. junior/senior faculty and graduate/undergraduate students). The results reveal that there remains considerable discussion and debate between the surveyed researchers about the definition of consciousness and the way it should be studied. To highlight a few observations, a majority of respondents believe that machines could have consciousness, that consciousness is a gradual phenomenon in the animal kingdom, and that unconscious processing is extensive, encompassing both low-level and high-level cognitive functions. Further, we show which theories of consciousness are currently considered most promising by respondents and how supposedly different theories cluster together, which dependent measures are considered best to index the presence or absence of consciousness, and which neural measures are thought to be the most likely signatures of consciousness. These findings provide us with a snapshot of the current views of researchers in the field and may therefore help prioritize research and theoretical approaches to foster progress.

Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here, we summarize, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system's temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Abstract Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.

Human perception and experience of time are strongly influenced by ongoing stimulation, memory of past experiences, and required task context. When paying attention to time, time experience seems to expand; when distracted, it seems to contract. When considering time based on memory, the experience may be different than what is in the moment, exemplified by sayings like "time flies when you're having fun." Experience of time also depends on the content of perceptual experience-rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions among attention, memory, and perceptual stimulation is a likely reason that an overarching theory of time perception has been difficult to achieve. Here, we introduce a model of perceptual processing and episodic memory that makes use of hierarchical predictive coding, short-term plasticity, spatiotemporal attention, and episodic memory formation and recall, and apply this model to the problem of human time perception. In an experiment with approximately 13,000 human participants, we investigated the effects of memory, cognitive load, and stimulus content on duration reports of dynamic natural scenes up to about 1 minute long. Using our model to generate duration estimates, we compared human and model performance. Model-based estimates replicated key qualitative biases, including differences by cognitive load (attention), scene type (stimulation), and whether the judgment was made based on current or remembered experience (memory). Our work provides a comprehensive model of human time perception and a foundation for exploring the computational basis of episodic memory within a hierarchical predictive coding framework.

Human experience of time exhibits systematic, context-dependent deviations from clock time; for example, time is experienced differently at work than on holiday. Here we test the proposal that differences from clock time in subjective experience of time arise because time estimates are constructed by accumulating the same quantity that guides perception: salient events. Healthy human participants watched naturalistic, silent videos of up to 24 seconds in duration and estimated their duration while fMRI was acquired. We were able to reconstruct trial-by-trial biases in participants' duration reports, which reflect subjective experience of duration, purely from salient events in their visual cortex BOLD activity. By contrast, salient events in neither of two control regions-auditory and somatosensory cortex-were predictive of duration biases. These results held despite being able to (trivially) predict clock time from all three brain areas. Our results reveal that the information arising during perceptual processing of a dynamic environment provides a sufficient basis for reconstructing human subjective time duration.

The major theories of consciousness that distinguish conscious from unconscious states can be grouped into two main classes, either higher order or integration theories. There is evidence that different types of mental states can be unconscious, though that conclusion depends on the theory of consciousness assumed. Unconscious memory (in the sense of the influence of a prior event not recollected) can shape perception, liking, and control our behaviour. Subliminal perception can produce semantic priming, guide attention and decision making; and optical variables a person describes incorrectly can guide action. Implicit learning can shape judgments and choices in complex environments. Unconscious intentions can allow people to respond appropriately in goal directed ways, while the person experiences the actions as involuntary. Unconscious attitudes are no more or less plausible than any other mental state being unconscious, but it has been hard to obtain evidence for unconscious attitudes, as distinct from gut reactions one does not agree with.

The experience of time passing is fundamental to human experience. Considerable experimental data has found that perceptual judgments of duration show systematic distortions away from physical ‘clock’ time. The majority of neurocognitive explanations of duration perception invoke some form of ‘inner clock’, or pacemaker. Systematic distortions can then be accounted for by alterations in this pacemaker mechanism. Here, we summarise recent work exploring a different approach, according to which experiences of duration are based on activity within perceptual classification networks. Specifically, we propose that subjective time is constructed from accumulated salient changes within hierarchical perceptual networks and substantiate this proposal by (i) building an artificial neural network based model which is able to predict human subjective time judgements, including a number of its biases; (ii) using model-based neuroimaging to show that human subjective time can be predicted from activity in human perceptual cortex, as suggested by our model, and (iii) locating the model within a larger ‘predictive processing’ framework which enables connections between time perception and episodic memory to be elaborated. Altogether, we provide a new mechanistic framework for understanding human time perception in terms of inference about information arising during perceptual processing.

We investigate the degree to which experiences in three experimental paradigms may arise from phenomenological control, i.e. from the way people can control their experience to meet expectancies and goals. In the experimental situation, expectancies and goals arise from demand characteristics (cues which communicate beliefs about experimental aims to participants). Trait phenomenological control has been shown to substantially predict experimental measures of changes in embodiment experience in which demand characteristics are not controlled (e.g., mirror touch and pain, and experiences of ownership of a fake hand). Here we report substantial relationships between scores on the Phenomenological Control Scale (PCS; a test of direct imaginative suggestion) and vEAR scores (reports of auditory experience for silent videos), ASMR scores but not the Müller-Lyer illusion. These findings indicate that vEAR and ASMR experiences may be implicit imaginative suggestion effects, but the Müller-Lyer illusion is unrelated to phenomenological control. This study demonstrates that relationships of trait phenomenological control with subjective reports about experience are not limited to embodiment and may confound a wide range of (but not all) measures in psychological science.

Synaesthesia is a condition defined by additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. The associative nature of synaesthesia has motivated attempts to induce synaesthesia by means of associative learning. Two recent studies of this kind highlighted the potential for perceptual plasticity even in adulthood, by demonstrating that extensive associative training can generate not only behavioural and neurophysiological markers of synaesthesia, but also synaesthesia-like phenomenology. However, while the results of these studies provided tantalising evidence that a learning component may be involved in the development of synesthetic phenomenology, they only provided superficial descriptions regarding the training-related changes in induced synaesthesia-like (ISL) experience. Therefore, it was not possible to assess how closely the phenomenology of ISL and naturally occurring grapheme-colour synaesthesia (NOS) overlap. Here, we addressed this question by providing a new extended qualitative analysis of the phenomenological changes associated with learning new perceptual phenomenology (ISL group) and comparing the descriptive similarities in colour experience to equivalent qualitative data acquired from a new group of NOS participants. Using this approach, we were able to directly compare associated colour experiences between the ISL and NOS group to assess how closely these two types of novel perceptual experience align. Our results reveal that induced and synaesthetic experience are remarkably similar, displaying a high degree of phenomenological overlap across multiple experiential categories, including: stability of experience, location of colour experience, shape of co-occurring colour experience, relative strength of colour experience and automaticity of colour experience. Our results exemplify the benefits of qualitative methods by providing new evidence that intensive training of letter-colour associations can alter conscious perceptual experiences in non-synaesthetes, and that such alterations produce synaesthesia-like phenomenology, which substantially resembles experiences described in natural grapheme-colour synaesthesia. Our results have implications for the plasticity of visual perception and the role of learning and development in establishing perceptual traits.

Bruineberg and colleagues helpfully distinguish between instrumental and ontological interpretations of Markov blankets, exposing the dangers of using the former to make claims about the latter. However, proposing a sharp distinction neglects the value of recognising a continuum spanning from instrumental to ontological. This value extends to the related distinction between ‘being’ and ‘having’ a model.

Extended Reality (XR), encompassing various forms of virtual reality (VR) and augmented reality (AR), has become a powerful experimental tool in consciousness research due to its capability to create holistic and immersive experiences of oneself and surrounding environments through simulation. One hallmark of a successful XR experience is when it elicits a strong sense of presence, which can be thought of as a subjective sense of reality of the self and the world. Although XR research has shed light on many factors that may influence presence (or its absence) in XR environments, there remains much to be discovered about the detailed and diverse phenomenology of presence, and the neurocognitive mechanisms that underlie it. In this chapter, we analyse the concept of presence and relate it to the way in which humans may generate and maintain a stable sense of reality during both natural perception and virtual experiences. We start by reviewing the concept of presence as developed in XR research, covering both factors that may influence presence and potential ways of measuring presence. We then discuss the phenomenological characteristics of presence in human consciousness, drawing on clinical examples where presence is disturbed. Next, we describe two experiments using XR that investigated the effects of sensorimotor contingency and affordances on a specific form of presence related to the sense of objects as really existing in the world, referred to as ‘objecthood’. We then go beyond perceptual presence to discuss the concept of 'conviction about reality', which corresponds to people's beliefs about the reality status of their perceptual experiences. We finish by exploring how the novel XR method of ‘Substitutional Reality’ can allow experimental investigation of these topics, opening new experimental directions for studying presence beyond the ‘as-if’ experience of fully simulated environments.

Accounts of predictive processing propose that conscious experience is influenced not only by passive predictions about the world, but also by predictions encompassing how the world changes in relation to our actions-that is, on predictions about sensorimotor contingencies. We tested whether valid sensorimotor predictions, in particular learned associations between stimuli and actions, shape reports about conscious visual experience. Two experiments used instrumental conditioning to build sensorimotor predictions linking different stimuli with distinct actions. Conditioning was followed by a breaking continuous flash suppression task, measuring the speed of reported breakthrough for different pairings between the stimuli and prepared actions, comparing those congruent and incongruent with the trained sensorimotor predictions. In Experiment 1, counterbalancing of the response actions within the breaking continuous flash suppression task was achieved by repeating the same action within each block but having them differ across the two blocks. Experiment 2 sought to increase the predictive salience of the actions by avoiding the repetition within blocks. In Experiment 1, breakthrough times were numerically shorter for congruent than incongruent pairings, but Bayesian analysis supported the null hypothesis of no influence from the sensorimotor predictions. In Experiment 2, reported conscious perception was significantly faster for congruent than for incongruent pairings. A meta-analytic Bayes factor combining the two experiments confirmed this effect. Altogether, we provide evidence for a key implication of the action-oriented predictive processing approach to conscious perception, namely that sensorimotor predictions shape our conscious experience of the world.

Reports of changes in experiences of body location and ownership following synchronous tactile and visual stimulation of fake and real hands (rubber hand (RH) effects) are widely attributed to multisensory integration mechanisms. However, existing control methods for subjective report measures (asynchronous stroking and control statements) are confounded by participant hypothesis awareness; the report may reflect response to demand characteristics. Subjective report is often accompanied by indirect (also called ‘objective’ or ‘implicit’) measures. Here, we report tests of expectancies for synchronous ‘illusion’ and asynchronous ‘control’ conditions across two pre-registered studies ( n = 140 and n = 45) for two indirect measures: proprioceptive drift (a change in perceived hand location) and skin conductance response (a measure of physiological arousal). Expectancies for synchronous condition measures were greater than for asynchronous conditions in both studies. Differences between synchronous and asynchronous control condition measures are therefore confounded by hypothesis awareness. This means indirect measures of RH effects may reflect compliance, bias and phenomenological control in response to demand characteristics, just as for subjective measures. Valid control measures are required to support claims of a role of multisensory integration for both direct and indirect measures of RH effects.

Abstract The adaptive regulation of bodily and interoceptive parameters, such as body temperature, thirst and hunger is a central problem for any biological organism. Here, we present a series of simulations using the framework of Active Inference to formally characterize interoceptive control and some of its dysfunctions. We start from the premise that the goal of interoceptive control is to minimize a discrepancy between expected and actual interoceptive sensations (i.e., a prediction error or free energy). Importantly, living organisms can achieve this goal by using various forms of interoceptive control: homeostatic, allostatic and goal-directed. We provide a computationally-guided analysis of these different forms of interoceptive control, by showing that they correspond to distinct generative models within Active Inference. Furthermore, we illustrate how these generative models may support empirical research, by predicting physiological and brain signals that may accompany both adaptive and maladaptive interoceptive control. Highlights We use Active Inference to provide formal models of interoceptive control We model homeostatic, allostatic and goal-directed forms of interoceptive control Our simulations illustrate both adaptive interoceptive control and its dysfunctions We discuss how the models can aid empirical research on interoception

Phenomenological control is the ability to generate experiences to meet expectancies. There are stable trait differences in this ability, as shown by responses to imaginative suggestions of, for example, paralysis, amnesia, and auditory, visual, gustatory and tactile hallucinations. Phenomenological control has primarily been studied within the context of hypnosis, in which suggestions are delivered following a hypnotic induction. Reports of substantial relationships between phenomenological control in a hypnotic context (hypnotizability) and experimental measures (e.g., the rubber hand illusion) suggest the need for a broad investigation of the influence of phenomenological control in psychological experiments. However, hypnosis is not required for successful response to imaginative suggestion. Because misconceptions about the hypnotic context may influence hypnotizability scores, a non-hypnotic scale which better matches the contextual expectancies of other experiments and avoids the hypnotic context is potentially better suited for such investigation. We present norms for the Phenomenological Control Scale (PCS), an adaptation of the Sussex Waterloo Scale of Hypnotizability (SWASH) which is free of the hypnotic context. Mean scores for the PCS are higher than for SWASH, and the subjective scales of PCS and SWASH show similar reliability. The PCS subjective scale is a reliable tool for measuring trait response to imaginative suggestion (i.e., phenomenological control) outside the context of hypnosis.

Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here, we summarize, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system's temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

In daily life, repeated experiences with a task (e.g. driving) will generally result in the development of a belief about one’s ability (“I am a good driver”). Here we ask how such beliefs, termed self-efficacy, interact with metacognitive confidence judgements. Across three pre-registered experiments, participants performed a perceptual discrimination task and reported their decision confidence. We induced contextual beliefs about performance (our operationalisation of self-efficacy) by manipulating the prior probability of an easy or hard trial occurring in each block. In Experiment 1 easy and hard trials generated the same levels of performance (a “subjective difficulty” manipulation), whereas in Experiments 2 and 3 performance differed across difficulty conditions (an “objective difficulty” manipulation). Results showed that context (self-efficacy) and difficulty interacted multiplicatively, consistent with the notion that confidence judgements combine decision evidence with a prior (contextual) belief on being correct. This occurred despite context having no corresponding effect on performance. We reasoned that performing tasks in easy contexts may reduce cognitive “load”, and tested this, in Experiment 3, by instructing participants to perform two tasks concurrently. Consistent with a reduction in load, the effects of context transferred from influencing confidence on our primary task to improving performance on the secondary task. Taken together, these studies reveal that contextual beliefs about performance facilitate multitasking, potentially by reducing the load of tasks believed to be easy, and they extend psychophysical investigations of perceptual decision-making by incorporating ‘higher-order’ beliefs about difficulty context, corresponding to intuitive notions of self-efficacy.

We report the results of an academic survey into the theoretical and methodological foundations, common assumptions, and the current state of the field of consciousness research. The survey consisted of 22 questions and was distributed on two different occasions of the annual meeting of the Association of the Scientific Study of Consciousness (ASSC, 2018 and 2019). We examined responses from 166 consciousness researchers with different backgrounds (e.g., philosophy, neuroscience, psychology, computer science) and at various stages of their careers (e.g., junior/senior faculty, graduate/undergraduate students). The results reveal that there remains considerable discussion and debate between the surveyed researchers about the definition of consciousness and the way it should be studied. To highlight a few observations, a majority of respondents believe that machines could have consciousness, that consciousness is a gradual phenomenon in the animal kingdom and that unconscious processing is extensive, encompassing both low-level as well as high-level cognitive functions. Further, we show which theories of consciousness are currently considered most promising by respondents and how supposedly different theories cluster together, which dependent measures are considered best to index the presence or absence of consciousness, and which neural measures are thought to be the most likely signatures of consciousness. These findings provide us with a snapshot of the current views of researchers in the field and may therefore help prioritise research and theoretical approaches to foster progress.

The rubber hand illusion (RHI) is a cornerstone of the scientific literature on embodiment. We have recently published a series of studies investigating the RHI, in particular its relationship to hypnotic (imaginative) suggestibility, and the validity of commonly used control conditions. These studies have generated substantial discussion regarding our claims, how they should be interpreted, and what all this means for past and future experimental studies of embodiment experiences [see, for example, (Makin, Scientific American, 2020)]. To clarify these issues, here we first summarise our main points (there is of course much more in the papers) and then offer responses to some frequently asked questions.

This paper considers the phenomenology of depersonalisation disorder, in relation to predictive processing and its associated pathophysiology. To do this, we first establish a few mechanistic tenets of predictive processing that are necessary to talk about phenomenal transparency, mental action, and self as subject. We briefly review the important role of ‘predicting precision’ and how this affords mental action and the loss of phenomenal transparency. We then turn to sensory attenuation and the phenomenal consequences of (pathophysiological) failures to attenuate or modulate sensory precision. We then consider this failure in the context of depersonalisation disorder. The key idea here is that depersonalisation disorder reflects the remarkable capacity to explain perceptual engagement with the world via the hypothesis that “I am an embodied perceiver, but I am not in control of my perception”. We suggest that individuals with depersonalisation may believe that ‘another agent’ is controlling their thoughts, perceptions or actions, while maintaining full insight that the ‘another agent’ is ‘me’ (the self). Finally, we rehearse the predictions of this formal analysis, with a special focus on the psychophysical and physiological abnormalities that may underwrite the phenomenology of depersonalisation.

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. We call this approach computational phenomenology because it applies methods originally developed in computational modelling to phenomenology. The first section presents a brief review of the project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project, and situates our project with respect to these projects. The third section reviews the generative modelling framework. The following section presents our new approach to neurophenomenology based on generative modelling. We then discuss how this application of generative modelling differs from previous attempts to use it to explain consciousness. In summary, generative modelling allows us to construct a computational model of the inferential or interpretive process that best explain this or that kind of lived experience.

In a previous paper, we reported substantial relationships between phenomenological control (trait response to imaginative suggestion) and responses to the rubber hand illusion, vicarious pain, and mirror synaesthesia. We argued that these responses may reflect phenomenological control rather than, or in addition to, other mechanisms. Ehrsson et al disagree with our claims regarding the rubber hand illusion. They provide analyses which replicate and extend our results, but which they argue undermine our claims. Here, we explain why our claims remain justified, drawing attention to the fact that comparing synchronous and asynchronous stroking in the rubber hand illusion is confounded by demand characteristics. Altogether, reported experience of ownership in the rubber hand illusion may be entirely attributable to phenomenological control, compliance and bias effects. Future experiments, with adequate control conditions, are needed to establish whether other mechanisms are involved.

Reports of changes in experiences of body location and ownership following synchronous tactile and visual stimulation of fake and real hands (rubber hand effects) are widely attributed to multisensory integration mechanisms. However, existing control methods for subjective report measures (asynchronous stroking and control statements) are confounded by participant hypothesis awareness; report may reflect response to demand characteristics. Subjective report is often accompanied by indirect (also called ‘objective’ or ‘implicit’) measures. Here we report tests of expectancies for synchronous ‘illusion’ and asynchronous ‘control’ conditions across two pre-registered studies (n = 140 and n = 45) for two indirect measures: proprioceptive drift (a change in perceived hand location) and skin conductance response (SCR; a measure of physiological arousal). Expectancies for synchronous condition measures were greater than for asynchronous condition in both studies. Differences between synchronous and asynchronous control condition measures are therefore confounded by hypothesis awareness. Indirect measures of rubber hand effects may reflect compliance, bias and phenomenological control in response to demand characteristics. Valid control measures are required to support claims of a role of multisensory integration for both direct and indirect measures of rubber hand effects.

Up to 40% of people report visually evoked auditory responses (vEARs; for example, ‘hearing’ sounds in response to watching silent videos). We investigate the degree to which vEAR experiences may arise from phenomenological control, i.e. from the way people can control their experience to meet expectancies arising from imaginative suggestion. In the experimental situation, expectancies arise from demand characteristics (cues which communicate beliefs about experimental aims to participants). Trait phenomenological control has been shown to substantially predict experimental measures of changes in ‘embodiment’ experience in which demand characteristics are not controlled (e.g., mirror touch and pain, and experiences of ownership of a fake hand). Here we report substantial relationship between scores on the Phenomenological Control Scale (PCS; a test of direct imaginative suggestion) and vEAR scores (reports of auditory experience for silent videos) which indicate that vEAR experience may be an implicit imaginative suggestion effect. This study demonstrates that relationships of trait phenomenological control with subjective reports about experience are not limited to embodiment and may confound a wide range of measures in psychological science.

Panpsychism is the notion that consciousness is fundamental and ubiquitous in the natural world. Defenders of panpsychism appeal, at least in part, to the apparent implausibility of materialist accounts of consciousness. However, materialist accounts are more resourceful than often assumed by proponents of panpsychism, while panpsychism has insurmountable problems of its own. The real problem with panpsychism is not that it seems crazy. It is that it explains nothing and does not generate testable predictions.

Phenomenological control is the ability to generate experiences to meet expectancies. There are stable trait differences in this ability, as shown by responses to imaginative suggestions of, for example, paralysis, amnesia, and auditory, visual, gustatory and tactile hallucinations. Phenomenological control has primarily been studied within the context of hypnosis, in which suggestions are delivered following a hypnotic induction. Reports of substantial relationships between phenomenological control in a hypnotic context (hypnotisability) and experimental measures (e.g., the rubber hand illusion) suggest the need for a broad investigation of the influence of phenomenological control in psychological experiments. However, hypnosis is not required for successful response to imaginative suggestion. Because misconceptions about the hypnotic context may influence hypnotisability scores, a non-hypnotic scale which better matches the contextual expectancies of other experiments and avoids the hypnotic context is potentially better suited for such investigation. We present norms for the Phenomenological Control Scale (PCS), an adaptation of the Sussex Waterloo Scale of Hypnotisability (SWASH) which is free of the hypnotic context. Mean scores for the PCS are higher than for SWASH, and the subjective scales of PCS and SWASH show similar reliability. The PCS subjective scale is a reliable tool for measuring trait response to imaginative suggestion (i.e., phenomenological control) outside the context of hypnosis.

The Kalman filter is a fundamental filtering algorithm that fuses noisy sensory data, a previous state estimate, and a dynamics model to produce a principled estimate of the current state. It assumes, and is optimal for, linear models and white Gaussian noise. Due to its relative simplicity and general effectiveness, the Kalman filter is widely used in engineering applications. Since many sensory problems the brain faces are, at their core, filtering problems, it is possible that the brain possesses neural circuitry that implements equivalent computations to the Kalman filter. The standard approach to Kalman filtering requires complex matrix computations that are unlikely to be directly implementable in neural circuits. In this paper, we show that a gradient-descent approximation to the Kalman filter requires only local computations with variance weighted prediction errors. Moreover, we show that it is possible under the same scheme to adaptively learn the dynamics model with a learning rule that corresponds directly to Hebbian plasticity. We demonstrate the performance of our method on a simple Kalman filtering task, and propose a neural implementation of the required equations.

We introduce a notion of emergence for coarse-grained macroscopic variables associated with highly-multivariate microscopic dynamical processes, in the context of a coupled dynamical environment. Dynamical independence instantiates the intuition of an emergent macroscopic process as one possessing the characteristics of a dynamical system "in its own right", with its own dynamical laws distinct from those of the underlying microscopic dynamics. We quantify (departure from) dynamical independence by a transformation-invariant Shannon information-based measure of dynamical dependence. We emphasise the data-driven discovery of dynamically-independent macroscopic variables, and introduce the idea of a multiscale "emergence portrait" for complex systems. We show how dynamical dependence may be computed explicitly for linear systems via state-space modelling, in both time and frequency domains, facilitating discovery of emergent phenomena at all spatiotemporal scales. We discuss application of the state-space operationalisation to inference of the emergence portrait for neural systems from neurophysiological time-series data. We also examine dynamical independence for discrete- and continuous-time deterministic dynamics, with potential application to Hamiltonian mechanics and classical complex systems such as flocking and cellular automata.

Predictive coding offers a potentially unifying account of cortical function -- postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world. The theory is closely related to the Bayesian brain framework and, over the last two decades, has gained substantial influence in the fields of theoretical and cognitive neuroscience. A large body of research has arisen based on both empirically testing improved and extended theoretical and mathematical models of predictive coding, as well as in evaluating their potential biological plausibility for implementation in the brain and the concrete neurophysiological and psychological predictions made by the theory. Despite this enduring popularity, however, no comprehensive review of predictive coding theory, and especially of recent developments in this field, exists. Here, we provide a comprehensive review both of the core mathematical structure and logic of predictive coding, thus complementing recent tutorials in the literature. We also review a wide range of classic and recent work within the framework, ranging from the neurobiologically realistic microcircuits that could implement predictive coding, to the close relationship between predictive coding and the widely-used backpropagation of error algorithm, as well as surveying the close relationships between predictive coding and modern machine learning techniques.

The Free-Energy-Principle (FEP) is an influential and controversial theory which postulates a deep and powerful connection between the stochastic thermodynamics of self-organization and learning through variational inference. Specifically, it claims that any self-organizing system which can be statistically separated from its environment, and which maintains itself at a non-equilibrium steady state, can be construed as minimizing an information-theoretic functional -- the variational free energy -- and thus performing variational Bayesian inference to infer the hidden state of its environment. This principle has also been applied extensively in neuroscience, and is beginning to make inroads in machine learning by spurring the construction of novel and powerful algorithms by which action, perception, and learning can all be unified under a single objective. While its expansive and often grandiose claims have spurred significant debates in both philosophy and theoretical neuroscience, the mathematical depth and lack of accessible introductions and tutorials for the core claims of the theory have often precluded a deep understanding within the literature. Here, we aim to provide a mathematically detailed, yet intuitive walk-through of the formulation and central claims of the FEP while also providing a discussion of the assumptions necessary and potential limitations of the theory. Additionally, since the FEP is a still a living theory, subject to internal controversy, change, and revision, we also present a detailed appendix highlighting and condensing current perspectives as well as controversies about the nature, applicability, and the mathematical assumptions and formalisms underlying the FEP.

Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than what is readily explainable in terms of the `sum of the parts.' Our ability to understand and control these systems remains limited, one reason being that we still don't know how best to describe -- and quantify -- the higher-order dynamical interactions that characterise their complexity. To address this limitation, we combine principles from the theories of Information Decomposition and Integrated Information into what we call Integrated Information Decomposition, or $Φ$ID. $Φ$ID provides a comprehensive framework to reason about, evaluate, and understand the information dynamics of complex multivariate systems. $Φ$ID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer and dynamical complexity as aggregates of these modes. Via computational and empirical examples, we demonstrate that $Φ$ID extends our explanatory power beyond traditional causal discovery methods -- with profound implications for the study of complex systems across disciplines.

Emergence is a profound subject that straddles many scientific disciplines,\nincluding the formation of galaxies and how consciousness arises from the\ncollective activity of neurons. Despite the broad interest that exists on this\nconcept, the study of emergence has suffered from a lack of formalisms that\ncould be used to guide discussions and advance theories. Here we summarise,\nelaborate on, and extend a recent formal theory of causal emergence based on\ninformation decomposition, which is quantifiable and amenable to empirical\ntesting. This theory relates emergence with information about a system's\ntemporal evolution that cannot be obtained from the parts of the system\nseparately. This article provides an accessible but rigorous introduction to\nthe framework, discussing the merits of the approach in various scenarios of\ninterest. We also discuss several interpretation issues and potential\nmisunderstandings, while highlighting the distinctive benefits of this\nformalism.\n

Panpsychism is the notion that consciousness is fundamental and ubiquitous. Defenders of panpsychism appeal, at least in part, to the apparent implausibility of materialist accounts of consciousness. However, materialist accounts are more resourceful than often assumed by proponents of panpsychism, while panpsychism has insurmountable problems of its own. The real problem with panpsychism is not that it seems crazy. It is that it explains nothing and does not generate testable predictions.

RATIONALE: Conscious perception is thought to depend on global amplification of sensory input. In recent years, striatal dopamine has been proposed to be involved in gating information and conscious access, due to its modulatory influence on thalamocortical connectivity. OBJECTIVES: Since much of the evidence that implicates striatal dopamine is correlational, we conducted a double-blind crossover pharmacological study in which we administered cabergoline-a dopamine D2 agonist-and placebo to 30 healthy participants. Under both conditions, we subjected participants to several well-established experimental conscious-perception paradigms, such as backward masking and the attentional blink task. RESULTS: We found no evidence in support of an effect of cabergoline on conscious perception: key behavioral and event-related potential (ERP) findings associated with each of these tasks were unaffected by cabergoline. CONCLUSIONS: Our results cast doubt on a causal role for dopamine in visual perception. It remains an open possibility that dopamine has causal effects in other tasks, perhaps where perceptual uncertainty is more prominent.

Recently, the discussion regarding the consequences of cutting the corpus callosum ("split-brain") has regained momentum (Corballis, Corballis, Berlucchi, & Marzi, Brain, 141(6), e46, 2018; Pinto et al., Brain, 140(5), 1231-1237, 2017a; Pinto, Lamme, & de Haan, Brain, 140(11), e68, 2017; Volz & Gazzaniga, Brain, 140(7), 2051-2060, 2017; Volz, Hillyard, Miller, & Gazzaniga, Brain, 141(3), e15, 2018). This collective review paper aims to summarize the empirical common ground, to delineate the different interpretations, and to identify the remaining questions. In short, callosotomy leads to a broad breakdown of functional integration ranging from perception to attention. However, the breakdown is not absolute as several processes, such as action control, seem to remain unified. Disagreement exists about the responsible mechanisms for this remaining unity. The main issue concerns the first-person perspective of a split-brain patient. Does a split-brain harbor a split consciousness or is consciousness unified? The current consensus is that the body of evidence is insufficient to answer this question, and different suggestions are made with respect to how future studies might address this paucity. In addition, it is suggested that the answers might not be a simple yes or no but that intermediate conceptualizations need to be considered.

We first review recent work from our laboratory, which construes hypnotizability as an example of a more general trait of capacity for phenomenological control, which people can use to create subjective experiences in many nonhypnotic contexts where those experiences fulfill people's goals. Second, we review recent work, which construes phenomenological control as a specifically metacognitive process, where intentional cognitive and motor action occurs without awareness of specific intentions (cold control theory). In terms of the reach of phenomenological control, we argue that various laboratory phenomena, namely vicarious pain, mirror-touch synesthesia, and the rubber hand illusion are to an unknown degree a construction of phenomenological control. The argument can of course be extended in principle to other findings. In terms of the reach of cold control, we present a new theory of intentional binding and show how intentional binding can measure the absence of conscious intentions in the hypnotic context. We obtain no evidence that cold control confers abilities beyond the changes in the metacognitive monitoring it postulates, and we explore the negative correlation between mindfulness and cold control viewed as a lack of mindfulness of intentions.

In hypnotic responding, expectancies arising from imaginative suggestion drive striking experiential changes (e.g., hallucinations) - which are experienced as involuntary - according to a normally distributed and stable trait ability (hypnotisability). Such experiences can be triggered by implicit suggestion and occur outside the hypnotic context. In large sample studies (of 156, 404 and 353 participants), we report substantial relationships between hypnotisability and experimental measures of experiential change in mirror-sensory synaesthesia and the rubber hand illusion comparable to relationships between hypnotisability and individual hypnosis scale items. The control of phenomenology to meet expectancies arising from perceived task requirements can account for experiential change in psychological experiments.

The theories of consciousness discussed by Doerig and colleagues tend to monolithically identify consciousness with some other phenomenon, process, or mechanism. But by treating consciousness as singular explanatory target, such theories will struggle to account for the diverse properties that conscious experiences exhibit. We propose that progress in consciousness science will best be achieved by elaborating systematic mappings between physical and biological mechanisms, and the functional and (crucially) phenomenological properties of consciousness. This means we need theories for consciousness science, perhaps more so than theories of consciousness. From this perspective, ‘predictive processing’ emerges as a highly promising candidate.

Abstract Human perception and experience of time is strongly influenced by ongoing stimulation, memory of past experiences, and required task context. When paying attention to time, time experience seems to expand; when distracted, it seems to contract. When considering time based on memory, the experience may be different than in the moment, exemplified by sayings like “time flies when you’re having fun”. Experience of time also depends on the content of perceptual experience – rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions between attention, memory, and perceptual stimulation is a likely reason that an overarching theory of time perception has been difficult to achieve. Here, we introduce a model of perceptual processing and episodic memory that makes use of hierarchical predictive coding, short-term plasticity, spatio-temporal attention, and episodic memory formation and recall, and apply this model to the problem of human time perception. In an experiment with ~ 13, 000 human participants we investigated the effects of memory, cognitive load, and stimulus content on duration reports of dynamic natural scenes up to ~ 1 minute long. Using our model to generate duration estimates, we compared human and model performance. Model-based estimates replicated key qualitative biases, including differences by cognitive load (attention), scene type (stimulation), and whether the judgement was made based on current or remembered experience (memory). Our work provides a comprehensive model of human time perception and a foundation for exploring the computational basis of episodic memory within a hierarchical predictive coding framework. Author summary Experience of the duration of present or past events is a central aspect of human experience, the underlying mechanisms of which are not yet fully understood. In this work, we combine insights from machine learning and neuroscience to propose a combination of mathematical models that replicate human perceptual processing, long-term memory, attention, and duration perception. Our computational implementation of this framework can process information from video clips of ordinary life scenes, record and recall important events, and report the duration of these clips. To assess the validity of our proposal, we conducted an experiment with ~ 13, 000 human participants. Each was shown a video between 1-64 seconds long and reported how long they believed it was. Reports of duration by our computational model qualitatively matched these human reports, made about the exact same videos. This was true regardless of the video content, whether time was actively judged or based on memory of the video, or whether the participants focused on a single task or were distracted - all factors known to influence human time perception. Our work provides the first model of human duration perception to incorporate these diverse and complex factors and provides a basis to probe the deep links between memory and time in human experience.

Abstract Rationale Conscious perception is thought to depend on global amplification of sensory input. In recent years, striatal dopamine has been proposed to be involved in gating information and conscious access, due to its modulatory influence on thalamocortical connectivity. Objectives Since much of the evidence that implicates striatal dopamine is correlational, we conducted a double-blind crossover pharmacological study in which we administered cabergoline – a dopamine D2 agonist – and placebo to 30 healthy participants. Under both conditions, we subjected participants to several well-established experimental conscious-perception paradigms, such as backward masking and the attentional blink task. Results We found no evidence in support of an effect of cabergoline on conscious perception: key behavioral and event-related potential (ERP) findings associated with each of these tasks were unaffected by cabergoline. Conclusions Our results cast doubt on a causal role for dopamine in visual perception. It remains an open possibility that dopamine has causal effects in other tasks, perhaps where perceptual uncertainty is more prominent.

Abstract Grapheme-colour synaesthesia (GCS) is defined by additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. The associative nature of GCS has motivated attempts to induce synaesthesia by means of associative learning. Two recent studies have shown that extensive associative training can generate not only behavioural (consistency and automaticity) and neurophysiological markers of GCS, but also synaesthesia-like phenomenology [1,2]. However, these studies provided only superficial descriptions regarding the training-related changes in subjective experience: they did not directly assess how closely induced synaesthetic experiences mirror those found in natural GCS. Here we report an extended qualitative analysis of the transcripts of the semi-structured interviews obtained following the completion of the associative training protocol used by [2]. In addition, we performed a comparable analysis of responses to an interview with a new population of natural occurring grapheme-colour synaesthetes (NOS), allowing us to directly compare the phenomenological dimensions of induced and naturally occurring synaesthetic experience. Our results provide an extensive addition to the description of the phenomenology of NOS experience, revealing a high degree of heterogeneity both within and across all experiential categories. Capitalising on this unique level of detail, we identified a number of shared experiential categories between NOS and induced synaesthesia-like (ISL) groups, including: stability of experience, location of colour experience, shape of co-occurring colour experience, relative strength of colour experience and automaticity of colour experience . Only the automaticity of colour experience differed significantly between the two groups: NOS experience was reported as being mostly automatic, whereas induced ISL were mostly described as being ‘wilful’. We observed three additional experiential categories relating to the automaticity of synaesthetic experience within the NOS group: contextually varied experience, semi-automatic experience and reflective association , which suggests that, as with other experiential categories, the automaticity of synaesthetic experience is also highly heterogeneous. Our results provide new evidence that that intensive training of letter-colour associations can alter conscious perceptual experiences in non-synaesthetes, and that such alterations produce synaesthesia-like phenomenology which substantially resembles similarities to natural grapheme-colour synaesthesia.

Recent findings have shown that psychedelics reliably enhance brain entropy (understood as neural signal diversity), and this effect has been associated with both acute and long-term psychological outcomes such as personality changes. These findings are particularly intriguing given that a decrease of brain entropy is a robust indicator of loss of consciousness (e.g. from wakefulness to sleep). However, little is known about how context impacts the entropy-enhancing effect of psychedelics, which carries important implications for how it can be exploited in, for example, psychedelic psychotherapy. This article investigates how brain entropy is modulated by stimulus manipulation during a psychedelic experience, by studying participants under the effects of LSD or placebo, either with gross state changes (eyes closed vs. open) or different stimulus (no stimulus vs. music vs. video). Results show that while brain entropy increases with LSD in all the experimental conditions, it exhibits largest changes when subjects have their eyes closed. Furthermore, brain entropy changes are consistently associated with subjective ratings of the psychedelic experience, but this relationship is disrupted when participants are viewing video — potentially due to a “competition” between external stimuli and endogenous LSD-induced imagery. Taken together, our findings provide strong quantitative evidence for the role of context in modulating neural dynamics during a psychedelic experience, underlining the importance of performing psychedelic psychotherapy in a suitable environment. Additionally, our findings put into question simplistic interpretations of brain entropy as a direct neural correlate of conscious level. Significance Statement The effects of psychedelic substances on conscious experience can be substantially affected by contextual factors, which play a critical role in the outcomes of psychedelic therapy. This study shows how context can modulate not only psychological, but also neurophysiological phenomena during a psychedelic experience. Our findings reveal distinctive effects of having eyes closed after taking LSD, including a more pronounced change on the neural dynamics, and a closer correspondence between brain activity and subjective ratings. Furthermore, our results suggest a competition between external stimuli and internal psychedelic-induced imagery, which supports the practice of carrying out psychedelic therapy with patients having their eyes closed.

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. `Active inference' is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of how biological agents achieve this. On this framework, inference, learning and action emerge from a single imperative to maximize the Bayesian evidence for a niched model of the world. However, implementations of this process have thus far been restricted to low-dimensional and idealized situations. Here, we present a working implementation of active inference that applies to high-dimensional tasks, with proof-of-principle results demonstrating efficient exploration and an order of magnitude increase in sample efficiency over strong model-free baselines. Our results demonstrate the feasibility of applying active inference at scale and highlight the operational homologies between active inference and current model-based approaches to RL.

Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

&lt;p&gt;In this appendix, we present results for an additional experiment where we compare learning under epistemic and random action strategies in a high-dimensional state space.&lt;/p&gt; &lt;p&gt;(PDF)&lt;/p&gt;

The broad concept of emergence is instrumental in various of the most challenging open scientific questions-yet, few quantitative theories of what constitutes emergent phenomena have been proposed. This article introduces a formal theory of causal emergence in multivariate systems, which studies the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour-which we refer to as causal decoupling. Moreover, the theory allows practical criteria that can be efficiently calculated in large systems, making our framework applicable in a range of scenarios of practical interest. We illustrate our findings in a number of case studies, including Conway's Game of Life, Reynolds' flocking model, and neural activity as measured by electrocorticography.

Accounts of predictive processing propose that conscious experience is influenced not only by passive predictions about the world, but also by predictions encompassing how the world changes in relation to our actions – that is, on predictions about sensorimotor contingencies. We tested whether valid sensorimotor predictions, in particular learned associations between stimuli and actions, shape reports about conscious visual experience. Two experiments used instrumental conditioning to build sensorimotor predictions linking different stimuli with distinct actions. Conditioning was followed by a breaking continuous flash suppression (b-CFS) task, measuring the speed of reported breakthrough for different pairings between the stimuli and prepared actions, comparing those congruent and incongruent with the trained sensorimotor predictions. In Experiment 1, counterbalancing of the response actions within the b-CFS task was achieved by repeating the same action within each block but having them differ across the two blocks. Experiment 2 sought to increase the predictive salience of the actions by avoiding the repetition within blocks. In Experiment 1, breakthrough times were numerically shorter for congruent than incongruent pairings, but Bayesian analysis supported the null hypothesis of no influence from the sensorimotor predictions. In Experiment 2, reported conscious perception was significantly faster for congruent than for incongruent pairings. A meta-analytic Bayes factor combining the two experiments confirmed this effect. Altogether, we provide evidence for a key implication of the action-oriented predictive processing approach to conscious perception, namely that sensorimotor predictions shape our conscious experience of the world.

The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to finding the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.

The extent to which high-level, complex functions can proceed unconsciously has been a topic of considerable debate. While unconscious processing has been demonstrated for a range of low-level processes, from feature integration to simple forms of conditioning and learning, theoretical contributions suggest that increasing complexity requires conscious access. Here, we focus our attention on instrumental conditioning, which has been previously shown to proceed without stimulus awareness. Yet, instrumental conditioning also involves integrating information over a large temporal scale and distinct modalities in order to deploy selective action, constituting a process of substantial complexity. With this in mind, we revisit the question of feasibility of instrumental conditioning in the unconscious domain. Firstly, we address the theoretical and practical considerations relevant to unconscious learning in general. Secondly, we aim to replicate the first study to show instrumental conditioning in the absence of stimulus awareness (Pessiglione et al., 2008), following the original design and supplementing the original crucial analyses with a Bayesian approach (Experiment 1). We found that apparent unconscious learning took place when replicating the original methods directly and according to the tests of awareness used. However, we could not establish that the full sample was unaware in a separate awareness check. We therefore attempted to replicate the effect yet again with improved methods to address the issues related to sensitivity and immediacy (Experiment 2), including an individual threshold-setting task and a trial-by-trial awareness check permitting exclusion of individual aware trials. Here, we found evidence for absence of unconscious learning. This result provides evidence that instrumental conditioning did not occur without stimulus awareness in this paradigm, supporting the view that complex forms of learning may rely on conscious access. Our results provides support for the proposal that perceptual consciousness may be necessary for complex, flexible processes, especially where selective action and behavioural adaptation are required.

Prior knowledge has been shown to facilitate the incorporation of visual stimuli into awareness. We adopted an individual differences approach to explore whether a tendency to ‘see the expected’ is general or method-specific. We administered a binocular rivalry task and manipulated selective attention, as well as induced expectations via predictive context, self-generated imagery, expectancy cues, and perceptual priming. Most prior manipulations led to a facilitated awareness of the biased percept in binocular rivalry, whereas strong signal primes led to a suppressed awareness, i.e., adaptation. Correlations and factor analysis revealed that the facilitatory effect of priors on visual awareness is closely related to attentional control. We also investigated whether expectation-based biases predict perceptual abilities. Adaptation to strong primes predicted improved naturalistic change detection and the facilitatory effect of weak primes predicted the experience of perceptual anomalies. Taken together, our results indicate that the facilitatory effect of priors may be underpinned by an attentional mechanism but the tendency to ‘see the expected’ is method-specific.

This short letter is a response to a recent Forum article in Trends in Cognitive Sciences, by Sun and Firestone, which reprises the so-called 'Dark Room Problem' as a challenge to the explanatory value of predictive processing and free-energy-minimisation frameworks for cognitive science. Among many possible responses to Sun and Firestone, we explain how minimisatin of prediction error in the long term (expected free energy) naturally prescribes the sort of epistemic, curious actions that lead to escaping from dark rooms.

The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.

Researchers often adjudicate between models of memory according to the models' ability to explain impaired patterns of performance (e.g., in amnesia). In contrast, evidence from special groups with enhanced memory is very rarely considered. Here, we explored how people with unusual perceptual experiences (synaesthesia) perform on various measures of memory and test how computational models of memory may account for their enhanced performance. We contrasted direct and indirect measures of memory (i.e., recognition memory, repetition priming, and fluency) in grapheme-colour synaesthetes and controls using a continuous identification with recognition (CID-R) paradigm. Synaesthetes outperformed controls on recognition memory and showed a different reaction-time pattern for identification. The data were most parsimoniously accounted for by a single-system computational model of the relationship between recognition and identification. Overall, the findings speak in favour of enhanced processing as an explanation for the memory advantage in synaesthesia. In general, our results show how synaesthesia can be used as an effective tool to study how individual differences in perception affect cognitive functions.

There are several ways to categorise reinforcement learning (RL) algorithms, such as either model-based or model-free, policy-based or planning-based, on-policy or off-policy, and online or offline. Broad classification schemes such as these help provide a unified perspective on disparate techniques and can contextualise and guide the development of new algorithms. In this paper, we utilise the control as inference framework to outline a novel classification scheme based on amortised and iterative inference. We demonstrate that a wide range of algorithms can be classified in this manner providing a fresh perspective and highlighting a range of existing similarities. Moreover, we show that taking this perspective allows us to identify parts of the algorithmic design space which have been relatively unexplored, suggesting new routes to innovative RL algorithms.

The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formulated in a manner suitable for implementation in neural circuitry. The primary challenge is to ensure that any candidate formulation uses only local information, rather than relying on global signals as in standard backprop. Recently several algorithms for approximating backprop using only local signals have been proposed. However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes, or multiple sequential backwards phases with information being stored across phases. Here, we propose a novel algorithm, Activation Relaxation (AR), which is motivated by constructing the backpropagation gradient as the equilibrium point of a dynamical system. Our algorithm converges rapidly and robustly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single parallel backwards relaxation phase, and can operate on arbitrary computation graphs. We illustrate these properties by training deep neural networks on visual classification tasks, and describe simplifications to the algorithm which remove further obstacles to neurobiological implementation (for example, the weight-transport problem, and the use of nonlinear derivatives), while preserving performance.

Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors. While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. Moreover, under certain conditions, predictive coding has been shown to approximate the backpropagation of error algorithm, and thus provides a relatively biologically plausible credit-assignment mechanism for training deep networks. However, standard implementations of the algorithm still involve potentially neurally implausible features such as identical forward and backward weights, backward nonlinear derivatives, and 1-1 error unit connectivity. In this paper, we show that these features are not integral to the algorithm and can be removed either directly or through learning additional sets of parameters with Hebbian update rules without noticeable harm to learning performance. Our work thus relaxes current constraints on potential microcircuit designs and hopefully opens up new regions of the design-space for neuromorphic implementations of predictive coding.

The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning rules. Unlike competing schemes, it converges to the exact backpropagation gradients, and utilises only a single type of computational unit and a single backwards relaxation phase. We have previously shown that the algorithm can be further simplified and made more biologically plausible by (i) introducing a learnable set of backwards weights, which overcomes the weight-transport problem, and (ii) avoiding the computation of nonlinear derivatives at each neuron. However, tthe efficacy of these simplifications has, so far, only been tested on simple multi-layer-perceptron (MLP) networks. Here, we show that these simplifications still maintain performance using more complex CNN architectures and challenging datasets, which have proven difficult for other biologically-plausible schemes to scale to. We also investigate whether another biologically implausible assumption of the original AR algorithm -- the frozen feedforward pass -- can be relaxed without damaging performance.

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. ‘Active inference’ is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of how biological agents achieve this. On this framework, inference, learning and action emerge from a single imperative to maximize the Bayesian evidence for a niched model of the world. However, implementations of this process have thus far been restricted to low-dimensional and idealized situations. Here, we present a working implementation of active inference that applies to high-dimensional tasks, with proof-of-principle results demonstrating efficient exploration and an order of magnitude increase in sample efficiency over strong model-free baselines. Our results demonstrate the feasibility of applying active inference at scale and highlight the operational homologies between active inference and current model-based approaches to RL.

Neuroimaging studies of the psychedelic state offer a unique window onto the neural basis of conscious perception and selfhood. Despite well understood pharmacological mechanisms of action, the large-scale changes in neural dynamics induced by psychedelic compounds remain poorly understood. Using source-localised, steady-state MEG recordings, we describe changes in functional connectivity following the controlled administration of LSD, psilocybin and low-dose ketamine, as well as, for comparison, the (non-psychedelic) anticonvulsant drug tiagabine. We compare both undirected and directed measures of functional connectivity between placebo and drug conditions. We observe a general decrease in directed functional connectivity for all three psychedelics, as measured by Granger causality, throughout the brain. These data support the view that the psychedelic state involves a breakdown in patterns of functional organisation or information flow in the brain. In the case of LSD, the decrease in directed functional connectivity is coupled with an increase in undirected functional connectivity, which we measure using correlation and coherence. This surprising opposite movement of directed and undirected measures is of more general interest for functional connectivity analyses, which we interpret using analytical modelling. Overall, our results uncover the neural dynamics of information flow in the psychedelic state, and highlight the importance of comparing multiple measures of functional connectivity when analysing time-resolved neuroimaging data.

Awareness may persist in fully disconnected cortical islands.We identify both natural and artificial examples of potential islands of awareness.Detecting islands of awareness poses difficult but often addressable challenges.The possibility of islands of awareness raises important ethical and legal issues.The discovery of islands of awareness would have important implications for debates about the nature of consciousness. Ordinary human experience is embedded in a web of causal relations that link the brain to the body and the wider environment. However, there might be conditions in which brain activity supports consciousness even when that activity is fully causally isolated from the body and its environment. Such cases would involve what we call islands of awareness: conscious states that are neither shaped by sensory input nor able to be expressed by motor output. This Opinion paper considers conditions in which such islands might occur, including ex cranio brains, hemispherotomy, and in cerebral organoids. We examine possible methods for detecting islands of awareness, and consider their implications for ethics and for the nature of consciousness. Ordinary human experience is embedded in a web of causal relations that link the brain to the body and the wider environment. However, there might be conditions in which brain activity supports consciousness even when that activity is fully causally isolated from the body and its environment. Such cases would involve what we call islands of awareness: conscious states that are neither shaped by sensory input nor able to be expressed by motor output. This Opinion paper considers conditions in which such islands might occur, including ex cranio brains, hemispherotomy, and in cerebral organoids. We examine possible methods for detecting islands of awareness, and consider their implications for ethics and for the nature of consciousness. Consciousness is first and foremost a property of living organisms – organisms that are embodied and embedded in environments. The contents of consciousness are shaped by the sensory stimuli received by the brain, and those contents in turn give rise to behaviours that prompt us to attribute consciousness to an organism. However, there are conscious states in which the transfer of information between the world and the brain is massively reduced, with the result that the brain (or parts thereof) becomes disconnected from its environment. In some conditions, disconnection is partial, so that some form of either input and/or output is retained. In other conditions, the disconnection is complete, so that the brain (or parts thereof) becomes fully isolated from its environment. What happens to consciousness when the brain becomes disconnected from its environment? Can it support islands of awareness (see Glossary), or does consciousness require the presence of (high bandwidth?) interaction between the brain and its environment? This question has long fascinated philosophers, but recent developments in neuroscience, neurosurgery, and neuroengineering now extend the scope of this discussion beyond the philosopher’s armchair and out into the laboratory and clinic. We address three issues raised by the possibility of islands of awareness. The first concerns their nature and distribution. Under what conditions might such islands arise? What forms might they take? How common might they be? A second issue concerns the detection of islands of awareness. Might current methods for detecting consciousness be applicable to islands of awareness, or will we need new tools for identifying consciousness in disconnected brains? A third issue concerns the implications of islands of awareness. What ethical implications might such islands have, and what might they tell us about the nature of consciousness? We address these issues by considering three conditions in which islands of awareness might be thought to occur: ex cranio brains; the neurosurgical procedure of hemispherotomy; and cerebral organoids. Although these three cases are by no means the only cases that could be considered here – for example, one might also consider whether islands of awareness could occur in utero [1Lagercrantz H. Changeux J.P. The emergence of human consciousness: from fetal to neonatal life.Pediatr. Res. 2009; 65: 255-260Crossref PubMed Scopus (116) Google Scholar] – we focus on them here because they highlight the issues raised by islands of awareness with particular force and urgency. Before we turn to genuine islands of awareness, we begin with cases of merely partial disconnection. Clinical neurology offers a rich repertoire of cases to consider here, for structural lesions can cause the brain to become disconnected on either the input or the output side without loss of consciousness. Starting from the input side, we know that consciousness can be preserved in the absence of afferent activity from peripheral receptors and nerves. For example, acquired blindness is a condition in which patients lose sight but retain the capacity for imagery, visual dreaming, and vivid hallucination [2Menon G.J. et al.Complex visual hallucinations in the visually impaired: the Charles Bonnet Syndrome.Surv. 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Science and art have long recognized that perceptual experience depends on the involvement of the experiencer. In art history, this idea is captured by Ernst Gombrich’s ‘beholder’s share’. In neuroscience, it traces to Helmholtz’s concept of ‘perception as inference’, which is enjoying renewed prominence in the guise of ‘prediction error minimization’ (PEM) or the ‘Bayesian brain’. The shared idea is that our perceptual experience – whether of the world, of ourselves, or of an artwork – depends on the active ‘top-down’ interpretation of sensory input. Perception becomes a generative act, in which perceptual, cognitive, affective, and sociocultural expectations conspire to shape the brain’s ‘best guess’ of the causes of sensory signals. In this article, I explore the parallels between the Bayesian brain and the beholders’ share, illustrated, somewhat informally, with examples from Impressionist, Expressionist, and Cubist art. By connecting phenomenological insights from these traditions with the cognitive neuroscience of predictive perception, I outline a reciprocal relationship in which art reveals phenomenological targets for neurocognitive accounts of subjectivity, while the concepts of predictive perception may in turn help make mechanistic sense of the beholder’s share. This is not standard neuroaesthetics – the attempt to discover the brain basis of aesthetic experience – nor is it any kind of neuro-fangled ‘theory of art’. It is instead an examination of one way in which art and brain science can be equal partners in revealing deep truths about human experience.

We investigated differences in intentional binding in high and low hypnotizable groups to explore two questions relating to (a) trait differences in the availability of motor intentions to metacognitive processes and (b) a proposed cue combination model of binding. An experience of involuntariness is central to hypnotic responding and may arise from strategically being unaware of one's intentions. Trait differences in the ability to respond to hypnotic suggestion may reflect differing levels of access to motor intentions. Intentional binding refers to the subjective compression of the time between an action and its outcome, indicated by a forward shift in the judged time of an action toward its outcome (action binding) and the backward shift of an outcome toward a causal action (outcome binding). Intentional binding is sensitive to intentional action without requiring explicit reflection upon agency. One way of explaining the sensitivity of intentional binding is to see it as a simple case of multisensory cue combination in which awareness of intentions increases knowledge of the timing of actions. Here we present results consistent with such a mechanism. In a contingent presentation of action and outcome events, low hypnotizable had more precise timing judgments of actions and also showed weaker action binding than highs. These results support the theory that trait hypnotizability is related to access to information related to motor intentions, and that intentional binding reflects the Bayesian combination of cross-modal cues. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. Here, we provide a novel explanation for how human time perception might be accomplished, based on non-temporal perceptual classification processes. To demonstrate this proposal, we build an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing. In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Estimates produced by this system match human reports made about the same videos, replicating key qualitative biases, including differentiating between scenes of walking around a busy city or sitting in a cafe or office. Our approach provides a working model of duration perception from stimulus to estimation and presents a new direction for examining the foundations of this central aspect of human experience.

The phenomenon of change blindness reveals that people are surprisingly poor at detecting unexpected visual changes; however, research on individual differences in detection ability is scarce. Predictive processing accounts of visual perception suggest that better change detection may be linked to assigning greater weight to prediction error signals, as indexed by an increased alternation rate in perceptual rivalry or greater sensitivity to low-level visual signals. Alternatively, superior detection ability may be associated with robust visual predictions against which sensory changes can be more effectively registered, suggesting an association with high-level mechanisms of visual short-term memory (VSTM) and attention. We administered a battery of 10 measures to explore these predictions and to determine, for the first time, the test-retest reliability of commonly used change detection measures. Change detection performance was stable over time and generalized from displays of static scenes to video clips. An exploratory factor analysis revealed two factors explaining performance across the battery, that we identify as visual stability (loading on change detection, attention measures, VSTM and perceptual rivalry) and visual ability (loading on iconic memory, temporal order judgments and contrast sensitivity). These results highlight the importance of strong, stable representations and the ability to resist distraction, in order to successfully incorporate unexpected changes into the contents of visual awareness.

All of us involved in the mind and brain sciences, in whatever capacity, are increasingly aware of the importance of ensuring the credibility of our research. This credibility-which for experimental work turns on its reliability-is particularly salient for consciousness research, given the at-times precarious perception of consciousness science within the wider landscapes of psychology and neuroscience We are therefore very pleased to introduce a number of 'open science' initiatives that have recently been implemented in Neuroscience of Consciousness.

Abstract Throughout his career Andy Clark has shaped how scientists and philosophers think about the role of representation in action, perception, and cognition. In the latest iteration of this debate he has foregrounded the influential perspective of predictive processing, which sees perception as a process of action-oriented “best guessing” (inference) about the causes of noisy and ambiguous sensory signals and which involves the brain-inducing “generative” models of how hidden causes mediate the effects of actions on sensory signals. This chapter develops this position in the context of interoception (the sense of the body from within) and physiological regulation. A key idea here, which recalls twentieth-century cybernetic theory, is that interoceptive inference is targeted towards maintaining physiological homeostasis rather than inducing complete and accurate internal models of an external state of affairs. The chapter explores how this perspective helps connect control-oriented interoceptive inference to phenomenological properties of embodied selfhood and subjectivity. The upshot echoes (or perhaps subverts) a classic philosophical trope of the Enlightenment philosopher Julien de La Mettrie: to find the origins of our conscious selves in our nature as beast machines.

People with synaesthesia have additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. Synaesthesia therefore offers a unique window into the neurocognitive mechanisms underlying conscious perception. A long-standing question in synaesthesia research is whether it is possible to artificially induce non-synaesthetic individuals to have synaesthesia-like experiences. Although synaesthesia is widely considered a congenital condition, increasing evidence points to the potential of a variety of approaches to induce synaesthesia-like experiences, even in adulthood. Here, we summarize a range of methods for artificially inducing synaesthesia-like experiences, comparing the resulting experiences to the key hallmarks of natural synaesthesia which include consistency, automaticity and a lack of 'perceptual presence'. We conclude that a number of aspects of synaesthesia can be artificially induced in non-synaesthetes. These data suggest the involvement of developmental and/or learning components in the acquisition of synaesthesia, and they extend previous reports of perceptual plasticity leading to dramatic changes in perceptual phenomenology in adults. This article is part of a discussion meeting issue 'Bridging senses: novel insights from synaesthesia'.

Abstract What are the global neuronal signatures of altered states of consciousness (ASC)? Recently, increases in neural signal diversity, compared to those found in wakeful rest, have been reported during psychedelic states. Neural signal diversity has previously been identified as a robust signature of the state of consciousness, showing lower scores during sleep or anaesthesia compared to wakeful rest. The increased neural signal diversity during psychedelic states raises the additional possibility that it may also reflect the increased diversity of subjective experiences associated with these states. However, psychedelic states involve widespread neuropsychopharmacological changes, only some of which may be associated with altered phenomenology. Therefore, we used stroboscopic stimulation to induce non-pharmacological altered states of consciousness while measuring the diversity of EEG signals. Stroboscopic stimulation caused substantial increases in the intensity and range of subjective experiences, with reports of both simple and complex visual hallucinations. These experiences were accompanied by increases in EEG signal diversity scores (measured using Lempel-Ziv complexity) exceeding those associated with wakeful rest, in line with studies of the psychedelic state. Our findings support the proposal that EEG signal diversity reflects the diversity of subjective experience that is associated with different states of consciousness.

Abstract Neuroimaging studies of the psychedelic state offer a unique window onto the neural basis of conscious perception and selfhood. Despite well understood pharmacological mechanisms of action, the large-scale changes in neural dynamics induced by psychedelic compounds remain poorly understood. Using source-localised, steady-state MEG recordings, we describe changes in functional connectivity following the controlled administration of LSD, psilocybin and low-dose ketamine, as well as, for comparison, the (non-psychedelic) anticonvulsant drug tiagabine. We compare both undirected and directed measures of functional connectivity between placebo and drug conditions. We observe a general decrease in directed functional connectivity for all three psychedelics, as measured by Granger causality, throughout the brain. These data support the view that the psychedelic state involves a breakdown in patterns of functional organisation or information flow in the brain. In the case of LSD, the decrease in directed functional connectivity is coupled with an increase in undirected functional connectivity, which we measure using correlation and coherence. This surprising opposite movement of directed and undirected measures is of more general interest for functional connectivity analyses, which we interpret using analytical modelling. Overall, our results uncover the neural dynamics of information flow in the psychedelic state, and highlight the importance of comparing multiple measures of functional connectivity when analysing time-resolved neuroimaging data.

Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

Neural processes in the brain operate at a range of temporal scales. Granger causality, the most widely-used neuroscientific tool for inference of directed functional connectivity from neurophsyiological data, is traditionally deployed in the form of one-step-ahead prediction regardless of the data sampling rate, and as such yields only limited insight into the temporal structure of the underlying neural processes. We introduce Granger causality variants based on multi-step, infinite-future and single-lag prediction, which facilitate a more detailed and systematic temporal analysis of information flow in the brain.

To investigate how embodied sensorimotor interactions shape subjective visual experience, we developed a novel naturalistic Virtual Reality setting combined with motion tracking that allow object i...

The experience of authorship over one's actions and their consequences-sense of agency-is a fundamental aspect of conscious experience. In recent years, it has become common to use intentional binding as an implicit measure of the sense of agency. However, it remains contentious whether reported intentional-binding effects indicate the role of intention-related information in perception or merely represent a strong case of multisensory causal binding. Here, we used a novel virtual-reality setup to demonstrate identical magnitude-binding effects in both the presence and complete absence of intentional action, when perceptual stimuli were matched for temporal and spatial information. Our results demonstrate that intentional-binding-like effects are most simply accounted for by multisensory causal binding without necessarily being related to intention or agency. Future studies that relate binding effects to agency must provide evidence for effects beyond that expected for multisensory causal binding by itself.

The neural basis of time perception remains unknown. A prominent account is the pacemaker-accumulator model, wherein regular ticks of some physiological or neural pacemaker are read out as time. Putative candidates for the pacemaker have been suggested in physiological processes (heartbeat), or dopaminergic mid-brain neurons, whose activity has been associated with spontaneous blinking. However, such proposals have difficulty accounting for observations that time perception varies systematically with perceptual content. We examined physiological influences on human duration estimates for naturalistic videos between 1–64 seconds using cardiac and eye recordings. Duration estimates were biased by the amount of change in scene content. Contrary to previous claims, heart rate, and blinking were not related to duration estimates. Our results support a recent proposal that tracking change in perceptual classification networks provides a basis for human time perception, and suggest that previous assertions of the importance of physiological factors should be tempered.

[Published in Nature Communications as Trait phenomenological control predicts experience of mirror synaesthesia and the rubber hand illusion] The control of top down processes to generate experience has been studied within the context of hypnosis since the birth of psychological science. In hypnotic responding, expectancies arising from imaginative suggestion drive striking experiential changes (e.g., hallucinations) – which are experienced as involuntary – according to a normally distributed and stable trait ability (hypnotisability). Such experiences can be triggered by implicit suggestion and occur outside the hypnotic context. The possibility that they account for experiential change in psychological studies has been overlooked. In large sample studies (of 156, 404 and 353 participants) we report substantial relationships between hypnotisability and experimental measures of experiential change (mirror-sensory synaesthesia and the rubber hand illusion) comparable to relationships between hypnotisability and individual hypnosis scale items. The control of phenomenology to meet expectancies arising from perceived task requirements can account for experiential change in psychological experiments.

Tourette syndrome is a hyperkinetic movement disorder. Characteristic features include tics, recurrent movements that are experienced as compulsive and "unwilled"; uncomfortable premonitory sensations that resolve through tic release; and often, the ability to suppress tics temporarily. We demonstrate how these symptoms and features can be understood in terms of aberrant predictive (Bayesian) processing in hierarchical neural systems, explaining specifically: why tics arise, their "unvoluntary" nature, how premonitory sensations emerge, and why tic suppression works-sometimes. In our model, premonitory sensations and tics are generated through over-precise priors for sensation and action within somatomotor regions of the striatum. Abnormally high precision of priors arises through the dysfunctional synaptic integration of cortical inputs. These priors for sensation and action are projected into primary sensory and motor areas, triggering premonitory sensations and tics, which in turn elicit prediction errors for unexpected feelings and movements. We propose experimental paradigms to validate this Bayesian account of tics. Our model integrates behavioural, neuroimaging, and computational approaches to provide mechanistic insight into the pathophysiological basis of Tourette syndrome.

Neural processes in the brain operate at a range of temporal scales. Granger\ncausality, the most widely-used neuroscientific tool for inference of directed\nfunctional connectivity from neurophsyiological data, is traditionally deployed\nin the form of one-step-ahead prediction regardless of the data sampling rate,\nand as such yields only limited insight into the temporal structure of the\nunderlying neural processes. We introduce Granger causality variants based on\nmulti-step, infinite-future and single-lag prediction, which facilitate a more\ndetailed and systematic temporal analysis of information flow in the brain.\n

Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them. However, analyses that depict interdependencies as directed graphs fail to discriminate the rich variety of modes of information flow that can coexist within a system. This, in turn, creates problems with attempts to operationalise the concepts of 'dynamical complexity' or `integrated information.' To address this shortcoming, we combine concepts of partial information decomposition and integrated information, and obtain what we call Integrated Information Decomposition, or $Φ$ID. We show how $Φ$ID paves the way for more detailed analyses of interdependencies in multivariate time series, and sheds light on collective modes of information dynamics that have not been reported before. Additionally, $Φ$ID reveals that what is typically referred to as 'integration' is actually an aggregate of several heterogeneous phenomena. Furthermore, $Φ$ID can be used to formulate new, tailored measures of integrated information, as well as to understand and alleviate the limitations of existing measures.

La realidad que percibimos no es un reflejo directo del mundo exterior objetivo. Bien al contrario, es el producto de las predicciones que el cerebro hace sobre las causas de los estimulos sensoriales recibidos. El sentido de realidad que acompana a nuestras percepciones serviria para orientar nuestro comportamiento y responder asi adecuadamente a la fuente de los estimulos.

Does disruption of prefrontal cortical activity using transcranial magnetic stimulation (TMS) impair visual metacognition? An initial study supporting this idea (Rounis, Maniscalco, Rothwell, Passingham, & Lau, 2010) motivated an attempted replication and extension (Bor, Schwartzman, Barrett, & Seth, 2017). Bor et al. failed to replicate the initial study, concluding that there was not good evidence that TMS to dorsolateral prefrontal cortex impairs visual metacognition. This failed replication has recently been critiqued by some of the authors of the initial study (Ruby, Maniscalco, & Peters, 2018). Here we argue that these criticisms are misplaced. In our response, we encounter some more general issues concerning good practice in replication of cognitive neuroscience studies, and in setting criteria for excluding data when employing statistical analyses like signal detection theory. We look forward to further studies investigating the role of prefrontal cortex in metacognition, with increasingly refined methodologies, motivated by the discussions in this series of papers.

The metacognitive ability to introspect about self-performance varies substantially across individuals. Given that effective monitoring of performance is deemed important for effective behavioral control, intervening to improve metacognition may have widespread benefits, for example in educational and clinical settings. However, it is unknown whether and how metacognition can be systematically improved through training independently of task performance, or whether metacognitive improvements generalize across different task domains. Across 8 sessions, here we provided feedback to two groups of participants in a perceptual discrimination task: an experimental group (n = 29) received feedback on their metacognitive judgments, while an active control group (n = 32) received feedback on their decision performance only. Relative to the control group, adaptive training led to increases in metacognitive calibration (as assessed by Brier scores), which generalized both to untrained stimuli and an untrained task (recognition memory). Leveraging signal detection modeling we found that metacognitive improvements were driven both by changes in metacognitive efficiency (meta-d'/d') and confidence level, and that later increases in metacognitive efficiency were positively mediated by earlier shifts in confidence. Our results reveal a striking malleability of introspection and indicate the potential for a domain-general enhancement of metacognitive abilities. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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INTRODUCTION: Metacognition, or "thinking about thinking", is a higher-order thought process that allows for the evaluation of perceptual processes for accuracy. Metacognitive accuracy is associated with the grey matter volume (GMV) in the prefrontal cortex (PFC), an area also impacted in schizophrenia. The present study set out to investigate whether deficits in metacognitive accuracy are present in the early stages of psychosis. METHODS: Metacognitive accuracy in first-episode psychosis (FEP) was assessed on a perceptual decision-making task and their performance compared to matched healthy control participants (N = 18). A novel signal detection theory approach was used to model metacognitive sensitivity independently from objective perceptual performance. A voxel-based morphometry investigation was also conducted on GMV. RESULTS: We found that the FEP group demonstrated significantly worse metacognitive accuracy compared to controls (p = .039). Importantly, GMV deficits were also observed in the superior frontal gyrus. The findings suggest a specific deficit in this processing domain to exist at first episode; however, no relationship was found between GMV and metacognitive accuracy. CONCLUSIONS: Our findings support the notion that an inability to accurately scrutinise perception may underpin functional deficits observed in later schizophrenia; however, the exact neural basis of metacognitive deficits in FEP remains elusive.

Tourette syndrome is a neurodevelopmental disorder, characterized by motor and phonic tics. Tics are typically experienced as avolitional, compulsive, and associated with premonitory urges. They are exacerbated by stress and can be triggered by external stimuli, including social cues like the actions and facial expressions of others. Importantly, emotional social stimuli, with angry facial stimuli potentially the most potent social threat cue, also trigger behavioural reactions in healthy individuals, suggesting that such mechanisms may be particularly sensitive in people with Tourette syndrome. Twenty-one participants with Tourette syndrome and 21 healthy controls underwent functional MRI while viewing faces wearing either neutral or angry expressions to quantify group differences in neural activity associated with processing social information. Simultaneous video recordings of participants during neuroimaging enabled us to model confounding effects of tics on task-related responses to the processing of faces. In both Tourette syndrome and control participants, face stimuli evoked enhanced activation within canonical face perception regions, including the occipital face area and fusiform face area. However, the Tourette syndrome group showed additional responses within the anterior insula to both neutral and angry faces. Functional connectivity during face viewing was then examined in a series of psychophysiological interactions. In participants with Tourette syndrome, the insula showed functional connectivity with a set of cortical regions previously implicated in tic generation: the presupplementary motor area, premotor cortex, primary motor cortex, and the putamen. Furthermore, insula functional connectivity with the globus pallidus and thalamus varied in proportion to tic severity, while supplementary motor area connectivity varied in proportion to premonitory sensations, with insula connectivity to these regions increasing to a greater extent in patients with worse symptom severity. In addition, the occipital face area showed increased functional connectivity in Tourette syndrome participants with posterior cortical regions, including primary somatosensory cortex, and occipital face area connectivity with primary somatosensory and primary motor cortices varied in proportion to tic severity. There were no significant psychophysiological interactions in controls. These findings highlight a potential mechanism in Tourette syndrome through which heightened representation within insular cortex of embodied affective social information may impact the reactivity of subcortical motor pathways, supporting programmed motor actions that are causally implicated in tic generation. Medicinal and psychological therapies that focus on reducing insular hyper-reactivity to social stimuli may have potential benefit for tic reduction in people with Tourette syndrome.

Consciousness is perhaps the most familiar aspect of our existence, yet we still do not know its biological basis. This chapter outlines a biomimetic approach to consciousness science, identifying three principles linking properties of conscious experience to potential biological mechanisms. First, conscious experiences generate large quantities of information in virtue of being simultaneously integrated and differentiated. Second, the brain continuously generates predictions about the world and self, which account for the specific content of conscious scenes. Third, the conscious self depends on active inference of self-related signals at multiple levels. Research following these principles helps move from establishing correlations between brain responses and consciousness towards explanations which account for phenomenological properties—addressing what can be called the “real problem” of consciousness. The picture that emerges is one in which consciousness, mind, and life, are tightly bound together—with implications for any possible future “conscious machines.”

Dissociative experiences, including depersonalization and derealisation, represent perturbations of consciousness and selfhood, and are commonly reported by patients during early stages of a psychotic illness. The continuity and integrity of a conscious sense of self is proposed to be grounded upon the control of internal physiological state and its predictive representation through interoception, i.e. the sensing of internal bodily changes. We tested the hypothesized relationship between dissociation and interoceptive deficits in patients with first episode psychosis (FEP), combining behavioural testing with functional neuroimaging. Individuals with first episode psychosis (N=41) and matched community control participants (N=21) performed an interoceptive task (heart-tone synchrony judgments) during functional magnetic resonance imaging (fMRI). Trial-by-trial confidence ratings indexed subjective performance, and measures of metacognitive interoceptive awareness (insight) were derived from confidence-accuracy correspondence. We tested for regional brain activity relating to dissociative symptom scores and objective, subjective and metacognitive aspects of interoception. In patients with FEP, metacognitive impairments in interoception predicted magnitude of dissociative symptoms, accompanied by hypoactivation of right insula cortex. Other dimensions of interoception, and accuracy, confidence and metacognitive insight on an exteroceptive task were unrelated to dissociative symptoms and there were no group differences between FEP patient and control groups. Our findings suggest that symptoms of disturbed conscious integrity and selfhood in early psychosis arise through selective disruption of higher-order metacognitive representations of interoceptive signals. Brain systems supporting the conscious integration of bodily feelings may represent a target for interventions to enhance functioning and, speculatively, mitigate illness progression in psychosis.

ABSTRACT Visual experience appears richly detailed despite the poor resolution of the majority of the visual field, thanks to foveal-peripheral integration. The recently described Uniformity Illusion (UI), in which peripheral elements of a pattern seem to take on the properties of foveal elements, may shed light on this integration. We examined the basis of UI by generating adaptation to a pattern of Gabors suitable for producing UI on orientation. After removing the pattern, participants reported the tilt of a single peripheral Gabor. The tilt after-effect (TAE) followed the physical adapting orientation rather than the global orientation perceived under UI, even when the illusion had been reported for a long time. Conversely, a control experiment replacing illusory for physical uniformity for the same durations did produce an after-effect to the global orientation. Our results indicate that the UI is not associated with changes in sensory encoding, but likely depends on high-level processes.

Abstract How sure are we about what we know? Confidence, measured via self-report, is often interpreted as a subjective probabilistic estimate on having made a correct judgement. The neurocognitive mechanisms underlying the construction of confidence and the information incorporated into these judgements are of increasing interest. Investigating these mechanisms requires principled and practically applicable measures of confidence and metacognition. Unfortunately, current measures of confidence are subject to distortions from decision biases and task performance. Motivated by a recent signal-detection theoretic behavioural measure of metacognitive sensitivity, known as meta- ď , here we present a quantitative behavioural measure of confidence that is invariant to decision bias and task performance. This measure, which we call m - distance , captures in a principled way the propensity to report decisions with high (or low) confidence. Computational simulations demonstrate the robustness of m - distance to decision bias and task performance, as well as its behaviour under conditions of high and low metacognitive sensitivity and under dual-channel and hierarchical models of metacognition. The introduction of the m - distance measure will enhance systematic quantitative studies of the behavioural expression and neurocognitive basis of subjective confidence.

Abstract The metacognitive ability to introspect about self-performance varies substantially across individuals. Given that effective monitoring of performance is deemed important for effective behavioural control, intervening to improve metacognition may have widespread benefits, for example in educational and clinical settings. However, it is unknown whether and how metacognition can be systematically improved through training independently of task performance, or whether metacognitive improvements generalize across different task domains. Across 8 sessions, here we provided feedback to two groups of participants in a perceptual discrimination task: an experimental group (N = 29) received feedback on their metacognitive judgments, while an active control group (N = 32) received feedback on their decision performance only. Relative to the control group, adaptive training led to increases in metacognitive calibration (as assessed by Brier scores) which generalized both to untrained stimuli and an untrained task (recognition memory). Leveraging signal detection modeling we found that metacognitive improvements were driven both by changes in metacognitive efficiency (meta- d’/d’ ) and confidence level, and that later increases in metacognitive efficiency were positively mediated by earlier shifts in confidence. Our results reveal a striking malleability of introspection and indicate the potential for a domain-general enhancement of metacognitive abilities.

Abstract Perception can be understood as an active process in which sensory samples are combined with prior expectations to shape perceptual content. A prominent example of the influence of priors on perception is that manually reproduced temporal durations are biased towards the mean of previously experienced durations. However, little is known about how prior expectations are acquired and maintained in environments in which multiple competing cues may indicate whether a given prior should be applied in that specific context. We tested whether human participants could acquire and maintain multiple priors for duration, dependent on the sensory signal in which the duration was presented. Human participants were presented with visual flashes or auditory tones, high or low pitch tones, or white noise versus pure tone audio. In each case, the presented duration on a given trial was drawn from a distribution that was, on average, shorter for tones than for flashes, or vice versa. Our participants’ timing reports were consistent with having acquired distinct duration priors dependent on the sensory signal in which the duration was presented (e.g. auditory or visual). Moreover, this was true whether signals differed across, or within, sensory modality. We account for our findings within a Bayesian framework in which duration priors are iteratively updated depending on determination of a common or distinct origin between successive events. Overall, these results show that the human brain can acquire and maintain multiple perceptual priors based on differences in stimulus properties both within and across the senses.

BACKGROUND: Corticospinal tract (CST) degeneration and cortical atrophy are consistent features of amyotrophic lateral sclerosis (ALS). We hypothesised that neurite orientation dispersion and density imaging (NODDI), a multicompartment model of diffusion MRI, would reveal microstructural changes associated with ALS within the CST and precentral gyrus (PCG) 'in vivo'. METHODS: 23 participants with sporadic ALS and 23 healthy controls underwent diffusion MRI. Neurite density index (NDI), orientation dispersion index (ODI) and free water fraction (isotropic compartment (ISO)) were derived. Whole brain voxel-wise analysis was performed to assess for group differences. Standard diffusion tensor imaging (DTI) parameters were computed for comparison. Subgroup analysis was performed to investigate for NODDI parameter differences relating to bulbar involvement. Correlation of NODDI parameters with clinical variables were also explored. The results were accepted as significant where p<0.05 after family-wise error correction at the cluster level, clusters formed with p<0.001. RESULTS: In the ALS group NDI was reduced in the extensive regions of the CST, the corpus callosum and the right PCG. ODI was reduced in the right anterior internal capsule and the right PCG. Significant differences in NDI were detected between subgroups stratified according to the presence or absence of bulbar involvement. ODI and ISO correlated with disease duration. CONCLUSIONS: NODDI demonstrates that axonal loss within the CST is a core feature of degeneration in ALS. This is the main factor contributing to the altered diffusivity profile detected using DTI. NODDI also identified dendritic alterations within the PCG, suggesting microstructural cortical dendritic changes occur together with CST axonal damage.

Simulating phenomenological aspects of altered states of consciousness provides an important experimental tool for consciousness science and psychiatry. Here we describe the Hallucination Machine, ...

The recent history of perceptual experience has been shown to influence subsequent perception. Classically, this dependence on perceptual history has been examined in sensory-adaptation paradigms, wherein prolonged exposure to a particular stimulus (e.g., a vertically oriented grating) produces changes in perception of subsequently presented stimuli (e.g., the tilt aftereffect). More recently, several studies have investigated the influence of shorter perceptual exposure with effects, referred to as serial dependence, being described for a variety of low- and high-level perceptual dimensions. In this study, we examined serial dependence in the processing of dispersion statistics, namely variance-a key descriptor of the environment and indicative of the precision and reliability of ensemble representations. We found two opposite serial dependences operating at different timescales, and likely originating at different processing levels: A positive, Bayesian-like bias was driven by the most recent exposures, dependent on feature-specific decision making and appearing only when high confidence was placed in that decision; and a longer lasting negative bias-akin to an adaptation aftereffect-becoming manifest as the positive bias declined. Both effects were independent of spatial presentation location and the similarity of other close traits, such as mean direction of the visual variance stimulus. These findings suggest that visual variance processing occurs in high-level areas but is also subject to a combination of multilevel mechanisms balancing perceptual stability and sensitivity, as with many different perceptual dimensions.

Visual experience appears richly detailed despite the poor resolution of the majority of the visual field, thanks to foveal-peripheral integration. The recently described uniformity illusion (UI), wherein peripheral elements of a pattern take on the appearance of foveal elements, may shed light on this integration. We examined the basis of UI by generating adaptation to a pattern of Gabors suitable for producing UI on orientation. After removing the pattern, participants reported the tilt of a single peripheral Gabor. The tilt aftereffect followed the physical adapting orientation rather than the global orientation perceived under UI, even when the illusion had been reported for a long time. Conversely, a control experiment replacing illusory uniformity with a physically uniform Gabor pattern for the same durations did produce an aftereffect to the global orientation. Results indicate that UI is not associated with changes in sensory encoding at V1 but likely depends on higher level processes.

The mind and brain sciences began with consciousness as a central concern. But for much of the 20th century, ideological and methodological concerns relegated its empirical study to the margins. Since the 1990s, studying consciousness has regained a legitimacy and momentum befitting its status as the primary feature of our mental lives. Nowadays, consciousness science encompasses a rich interdisciplinary mixture drawing together philosophical, theoretical, computational, experimental, and clinical perspectives, with neuroscience its central discipline. Researchers have learned a great deal about the neural mechanisms underlying global states of consciousness, distinctions between conscious and unconscious perception, and self-consciousness. Further progress will depend on specifying closer explanatory mappings between (first-person subjective) phenomenological descriptions and (third-person objective) descriptions of (embodied and embedded) neuronal mechanisms. Such progress will help reframe our understanding of our place in nature and accelerate clinical approaches to a wide range of psychiatric and neurological disorders.

Intentional binding refers to the subjective compression of the time between an action and its outcome, typically indicated by a forward shift in the judged time of an action toward its outcome (action binding) and the backward shift of an outcome toward the action that caused it (outcome binding). The effect is considered an implicit measure of the sense of agency as it is sensitive to intentional action without requiring explicit reflection upon agency. One way of explaining the sensitivity of intentional binding is to see it as a simple case of multisensory cue combination in which awareness of intentions increases knowledge of the timing of actions. Here we present results consistent with such a mechanism. An experience of involuntariness is central to hypnotic responding, and may arise from strategically being unaware of one’s intentions. Trait differences in the ability to respond to hypnotic suggestion may reflect differing levels of access to motor intentions, with highly hypnotisable people having relatively low access and low hypnotisable people greater access. In a contingent presentation of action and outcome events, low hypnotisables had more precise timing judgements of actions than highs, and showed weaker action binding than highs. These results support the theory that trait hypnotisability is related to access to information related to motor intentions, as increased availability of such information should support more precise judgements of the timing of an intentional action. Intentional binding may thus reflect the Bayesian combination of cross-modal cues.

Modern psychology has long focused on the body as the basis of the self. Recently, predictive processing accounts of interoception (perception of the body ‘from within’) have become influential in accounting for experiences of body ownership and emotion. Here, we describe embodied selfhood in terms of ‘instrumental interoceptive inference’ that emphasises allostatic regulation and physiological integrity. We apply this approach to the distinctive phenome- nology of embodied selfhood, accounting for its non-object-like character and subjective stability over time. Our perspective has implications for the develop- ment of selfhood and illuminates longstanding debates about relations between life and mind, implying, contrary to Descartes, that experiences of embodied selfhood arise because of, and not in spite of, our nature as ‘beast machines’.

Unravelling the mechanisms that trigger cognitive control is a central question in cognitive science. Cognitive conflict is closely associated with the activation of cognitive control, helping individuals to follow their own goals. In this study, we examined whether the association of conflict and control holds when people are not aware of their intentions (i.e., they experience involuntariness regarding their behaviours). To induce unconscious control, we employed a posthypnotic suggestion (word blindness: that words will appear as a meaningless foreign script) on highly suggestible participants, a manipulation which has previously been shown to halve the Stroop interference effect. To alter the amount of conflict, we manipulated the proportion of incongruent, congruent and neutral Stroop trials between blocks in two experiments. The analyses revealed that the Stroop effect was reduced by the suggestion in the high conflict conditions (conditions with 33% or more incongruent trials), and barely at all in the low conflict conditions (conditions with 10% incongruent trials) compared to no suggestion conditions, thus, supporting the idea that a certain amount of conflict is required to activate unconscious control. This finding can also be interpreted in light of the two competing accounts of the word blindness effect (de-automatisation of reading and response competition models). The results imply that conflict between the response options occurs even in the suggestion condition and so the word blindness suggestion does not influence semantic processing as the de-automatisation of reading account postulates, rather, it is more likely that the suggestion facilitates the reduction of response competition.

Interoception is the body-to-brain axis of sensation concerning the state of the internal body. Interoceptive signals from the heart can modulate emotional processing. Moreover, the degree to which people accurately detect their own heart beating at rest can predict their emotional responsivity. Here we determine how interoceptive signals can modulate the perception of faces by exploiting phasic cardiac activity within a binocular onset rivalry paradigm. We demonstrate that afferent cardiac signals facilitate the early processing of fearful face images in individuals with high interoceptive (heartbeat detection) accuracy: The contraction of heart at cardiac systole enhanced the detection of fearful faces (during initial image selection in rivalrous presentations), and shortened reaction times for subsequent emotional judgements. However, cardiac systole does not lead to an overall bias in increased fear categorisation. Our findings highlight interactive contributions of interoception in shaping perceptual and behavioural aspects of emotional processing. Our results thereby add to empirical data regarding mechanisms of body-brain integration underpinning emotional experience. (Preprint of manuscript under review)

The recent history of perceptual experience has been shown to influence subsequent perception. Classically, this dependence on perceptual history has been examined in sensory adaptation paradigms, wherein prolonged exposure to a particular stimulus (e.g. a vertically oriented grating) produces changes in perception of subsequently presented stimuli (e.g. the tilt aftereffect). More recently, several studies have investigated the influence of shorter perceptual exposure with effects, referred to as serial dependence, being described for a variety of low and high-level perceptual dimensions. In this study, we examined serial dependence in the processing of dispersion statistics, namely variance - a key descriptor of the environment and indicative of the precision and reliability of ensemble representations. We found two opposite serial dependencies operating at different timescales, and likely originating at different processing levels: A positive, Bayesian-like bias was driven by the most recent exposures, dependent on feature-specific decision-making and appearing only when high confidence was placed in that decision; and a longer-lasting negative bias - akin to an adaptation after-effect - becoming manifest as the positive bias declined. Both effects were independent of spatial presentation location and the similarity of other close traits, such as mean direction of the visual variance stimulus. These findings suggest that visual variance processing occurs in high-level areas, but is also subject to a combination of multi-level mechanisms balancing perceptual stability and sensitivity, as with many different perceptual dimensions.

Does disruption of prefrontal cortical activity using transcranial magnetic stimulation (TMS) impair visual metacognition? An initial study supporting this idea (Rounis, Maniscalco, Rothwell, Passingham, &amp;amp; Lau, 2010) motivated an attempted replication and extension (Bor, Schwartzman, Barrett, &amp;amp; Seth, 2017). Bor et al. failed to replicate the initial study, concluding that there was not good evidence that TMS to prefrontal cortex impairs visual metacognition. This failed replication has recently been critiqued by some of the authors of the initial study (Ruby, Maniscalco, Lau, &amp;amp; Peters, 2017). Here, we argue that these recent criticisms are misplaced. In responding to the criticisms of Ruby et al, we encounter some more general issues concerning good practice in replication of cognitive neuroscience studies, and in setting criteria for excluding data when employing statistical analyses like signal detection theory. We look forward to further studies of the role of prefrontal cortex in metacognition, with increasingly refined methodologies, motivated by the discussions in this series of papers.

The experience of authorship over one’s actions and their consequences - sense of agency - is a fundamental aspect of conscious experience. In recent years, it has become common to use intentional binding as an implicit measure of the sense of agency. However, it remains contentious whether binding effects indicate the role of intention-related information in perception or merely represent a strong case of multisensory causal binding. Here, we use a novel virtual reality setup to demonstrate identical magnitude binding effects both in the presence and complete absence of intentional action, when perceptual stimuli are matched for temporal and spatial information. Our results demonstrate that intentional binding-like effects are most simply accounted for by multisensory causal binding, without necessarily being related to intention or agency. Future studies which relate binding effects to agency must provide evidence for effects beyond that expected for multisensory causal binding by itself.

Throughout his career Andy Clark has shaped how scientists and philosophers think about the role of representation in action, perception, and cognition. In the latest iteration of this debate he has foregrounded the influential perspective of ‘predictive processing’, which sees perception as a process of action-oriented ‘best guessing’ (inference) about the causes of noisy and ambiguous sensory signals, and which involves the brain inducing ‘generative’ models of how hidden causes mediate the effects of actions on sensory signals. Here, I will develop this position in the context of interoception (the sense of the body from within) and physiological regulation. A key idea here, which recalls 20th century cybernetic theory, is that interoceptive inference is targeted towards maintaining physiological homeostasis rather than inducing complete and accurate internal models of an external state-of-affairs. I explore how this perspective helps connect control-oriented interoceptive inference to phenomenological properties of embodied selfhood and subjectivity. The upshot echoes (or perhaps subverts) a classic philosophical trope of the Enlightenment philosopher Julien La Mettrie: to find the origins of our conscious selves in our nature as beast machines.

To investigate how embodied sensorimotor interactions shape subjective visual experience, we developed a novel combination of Virtual Reality (VR) and Augmented Reality (AR) within an adapted breaking continuous flash suppression (bCFS) paradigm. In a first experiment, participants manipulated novel virtual 3D objects, viewed through a head-mounted display, using three interlocking cogs. This setup allowed us to manipulate the sensorimotor contingencies governing interactions with virtual objects, while characterising the effects on subjective visual experience by measuring breakthrough times from bCFS. We contrasted the effects of the congruency (veridical versus reversed sensorimotor coupling) and contingency (live versus replayed interactions) using a motion discrimination task. The results showed that the contingency but not congruency of sensorimotor coupling affected breakthrough times, with live interactions displaying faster breakthrough times. In a second experiment, we investigated how the contingency of sensorimotor interactions affected object category discrimination within a more naturalistic setting, using a motion tracker that allowed object interactions with increased degrees of freedom. We again found that breakthrough times were faster for live compared to replayed interactions (contingency effect). Together, these data demonstrate that bCFS breakthrough times for unfamiliar 3D virtual objects are modulated by the contingency of the dynamic causal coupling between actions and their visual consequences, in line with theories of perception that emphasise the influence of sensorimotor contingencies on visual experience. The combination of VR/AR and motion tracking technologies with bCFS provides a novel methodology extending the use of binocular suppression paradigms into more dynamic and realistic sensorimotor environments.

A major challenge for the successful naturalization of consciousness lies in locating its biological function, or functions. Although common sense suggests that conscious experience has many important functional roles in our lives, experiments and theoretical arguments challenge these everyday intuitions. Many human behaviors can occur in the absence of consciousness, and the natural world contains many creatures capable of engaging in complex behaviors, at least some of which may be doing so entirely without consciousness (e.g., mollusks, microorganisms). While consciousness is a real phenomenon whether functional or not, without any defensible function its scientific study is made even more difficult than it already is.We begin with some necessary conceptual ground-clearing, reviewing the possibility that consciousness does not have a function and dealing with ambiguities in the meaning of ‘function’ and of ‘consciousness’. We then consider in detail a range of putative functions for consciousness, covering both cognitive perspectives and proposals grounded in neural dynamics. We start with the intuitively appealing ideas that consciousness functions to initiate voluntary behavior and/or to mediate rational actions, before turning to more recent candidates, for example that consciousness functions to integrate information or to mediate planning and flexible behavior in response to novelty. Finally, we consider the possibility that consciousness, as a constellation concept,may have multiple functions. [The reference for this chapter is Seth, A.K. (2009). Functions of consciousness. In W.P. Banks (ed.) Elsevier Encyclopedia of Consciousness. Vol 1: 279-293.]

At the birth of psychology as a science, consciousness was its central problem. But throughout the twentieth century, ideological and methodological concerns pushed the explicit empirical study of consciousness to the sidelines. Since the 1990s, studying consciousness has regained a legitimacy and impetus befitting its status as the central feature of our mental lives. Nowadays consciousness science encompasses a rich interdisciplinary mixture drawing together philosophical, theoretical, computational, experimental, and clinical perspectives. While solving the metaphysically ‘hard’ problem of why consciousness is part of the universe may seem as intractable as ever, scientists have learned a great deal about the neural mechanisms underlying conscious states. Further progress will depend on specifying closer explanatory mappings between (first person subjective) phenomenological descriptions and (third person objective) descriptions of biological and physical processes. Such progress will help reframe our understanding of our place in nature, and may also accelerate clinical approaches to a wide range of psychiatric and neurological disorders.

The neural basis of time perception remains unknown. A prominent account is the pacemaker-accumulator model, wherein regular ticks of some physiological or neural pacemaker are read out as time. Putative candidates for the pacemaker have been suggested in physiological processes (heartbeat), or dopaminergic mid-brain neurons, whose activity has been associated with spontaneous blinking. However, such proposals have difficulty accounting for observations that time perception varies systematically with perceptual content. We examined physiological influences on human duration estimates for naturalistic videos between 1-64 seconds using cardiac and eye recordings. Duration estimates were biased by the amount of change in scene content. Contrary to previous claims, heart rate, and blinking were not related to duration estimates. Our results support a recent proposal that tracking change in perceptual classification networks provides a basis for human time perception, and suggest that previous assertions of the importance of physiological factors should be tempered.

Determining what constitutes practically relevant, statistically significant evidence for animal sentience, under the precautionary principle, could be enhanced through Bayesian statistics. A Bayesian approach allows incorporation of multiple evidence sources through prior probabilities, the tracking of changing evidence across time, and a principled means of adjusting evidentiary bars via Bayes factors.

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The ‘free energy principle’ (FEP) has been suggested to provide a unified theory of the brain, integrating data and theory relating to action, perception, and learning. The theory and implementation of the FEP combines insights from Helmholtzian ‘perception as inference’, machine learning theory, and statistical thermodynamics. Here, we provide a detailed mathematical evaluation of a suggested biologically plausible implementation of the FEP that has been widely used to develop the theory. Our objectives are (i) to describe within a single article the mathematical structure of this implementation of the FEP; (ii) provide a simple but complete agent-based model utilising the FEP and (iii) to disclose the assumption structure of this implementation of the FEP to help elucidate its significance for the brain sciences.

Is conscious visual perception limited to the locations that a person attends? The remarkable phenomenon of change blindness, which shows that people miss nearly all unattended changes in a visual scene, suggests the answer is yes. However, change blindness is found after visual interference (a mask or a new scene), so that subjects have to rely on working memory (WM), which has limited capacity, to detect the change. Before such interference, however, a much larger capacity store, called fragile memory (FM), which is easily overwritten by newly presented visual information, is present. Whether these different stores depend equally on spatial attention is central to the debate on the role of attention in conscious vision. In 2 experiments, we found that minimizing spatial attention almost entirely erases visual WM, as expected. Critically, FM remains largely intact. Moreover, minimally attended FM responses yield accurate metacognition, suggesting that conscious memory persists with limited spatial attention. Together, our findings help resolve the fundamental issue of how attention affects perception: Both visual consciousness and memory can be supported by only minimal attention. (PsycINFO Database Record

Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception. However, the phenomenological properties of these states are difficult to isolate experimentally from other, more general physiological and cognitive effects of psychoactive substances or psychopathological conditions. Thus, simulating phenomenological aspects of altered states in the absence of these other more general effects provides an important experimental tool for consciousness science and psychiatry. Here we describe such a tool, which we call the Hallucination Machine. It comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display (panoramic VR). By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. Two experiments illustrate potential applications of the Hallucination Machine. First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.

What is the level of consciousness of the psychedelic state? Empirically, measures of neural signal diversity such as entropy and Lempel-Ziv (LZ) complexity score higher for wakeful rest than for states with lower conscious level like propofol-induced anesthesia. Here we compute these measures for spontaneous magnetoencephalographic (MEG) signals from humans during altered states of consciousness induced by three psychedelic substances: psilocybin, ketamine and LSD. For all three, we find reliably higher spontaneous signal diversity, even when controlling for spectral changes. This increase is most pronounced for the single-channel LZ complexity measure, and hence for temporal, as opposed to spatial, signal diversity. We also uncover selective correlations between changes in signal diversity and phenomenological reports of the intensity of psychedelic experience. This is the first time that these measures have been applied to the psychedelic state and, crucially, that they have yielded values exceeding those of normal waking consciousness. These findings suggest that the sustained occurrence of psychedelic phenomenology constitutes an elevated level of consciousness - as measured by neural signal diversity.

Scientific Reports 6: Article number: 24350; published online: 15 April 2016; updated: 11 April 2017 In this Article, an additional affiliation for Paul F.M.J. Verschure was omitted. The correct affiliations for Paul F.M.J. Verschure are listed below: SPECS, Universitat Pompeu Fabra, Barcelona, Spain.

Key to understanding the neuronal basis of consciousness is the characterization of the neural signatures of changes in level of consciousness during sleep. Here we analysed three measures of dynamical complexity on spontaneous depth electrode recordings from 10 epilepsy patients during wakeful rest (WR) and different stages of sleep: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability over time of the set of channels active above a threshold; (iii) synchrony coalition entropy, which measures the variability over time of the set of synchronous channels. When computed across sets of channels that are broadly distributed across multiple brain regions, all three measures decreased substantially in all participants during early-night non-rapid eye movement (NREM) sleep. This decrease was partially reversed during late-night NREM sleep, while the measures scored similar to WR during rapid eye movement (REM) sleep. This global pattern was in almost all cases mirrored at the local level by groups of channels located in a single region. In testing for differences between regions, we found elevated signal complexity in the frontal lobe. These differences could not be attributed solely to changes in spectral power between conditions. Our results provide further evidence that the level of consciousness correlates with neural dynamical complexity.

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. Here, we provide a novel explanation for how human time perception might be accomplished, based on non-temporal perceptual clas-sification processes. To demonstrate this proposal, we built an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing. In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Estimates produced by this system match human reports made about the same videos, replicating key qualitative biases, including differentiating between scenes of walking around a busy city or sitting in a cafe or office. Our approach provides a working model of duration perception from stimulus to estimation and presents a new direction for examining the foundations of this central aspect of human experience.

Abstract Effective behaviour and cognition requires the ability to make predictions about the temporal properties of events, such as duration. It is well known that violations of temporal structure within sequences of stimuli lead to neurophysiological effects known as the (temporal) mismatch negativity (TMMN). However, previous studies investigating this phenomenon have typically presented successive stimulus intervals (i.e., durations) within a rhythmic structure, conflating the contributions of rhythmic temporal processing with those specific to duration. In a novel behavioural paradigm which extends the classic temporal oddball design, we examined the neurophysiological correlates of prediction violation under both rhythmically (isochronous) and arrhythmically (anisochronous) presented durations, in visual and auditory modalities. Using event-related potential (ERP), multivariate pattern analysis (MVPA), and temporal generalisation analysis (TGA) analyses, we found evidence for common, and distinct neurophysiological responses related to duration predictions and their violation, across isochronous and anisochronous conditions. Further, using TGA we could directly compare processes underlying duration prediction violation across different modalities, despite differences in processing latency of audition and vision. We discovered a common set of neurophysiological responses that are elicited whenever a duration prediction is violated, regardless of presentation modality, indicating the existence of a supramodal duration prediction mechanism. Altogether, our data show that the human brain encodes predictions specifically about duration, in addition to those from rhythmic structure, and that the neural underpinnings of these predictions generalize across modalities. These findings support the idea that time perception is based on similar principles of inference as characterize ‘predictive processing’ theories of perception.

Abstract Altered states of consciousness, such as psychotic or pharmacologically-induced hallucinations, provide a unique opportunity to examine the mechanisms underlying conscious perception. However, the phenomenological properties of these states are difficult to isolate experimentally from other, more general physiological and cognitive effects of psychoactive substances or psychopathological conditions. Thus, simulating phenomenological aspects of altered states in the absence of these other more general effects provides an important experimental tool for consciousness science and psychiatry. Here we describe such a tool, the Hallucination Machine . It comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display (panoramic VR). By doing this, we are able to simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way. Two experiments illustrate potential applications of the Hallucination Machine . First, we show that the system induces visual phenomenology qualitatively similar to classical psychedelics. In a second experiment, we find that simulated hallucinations do not evoke the temporal distortion commonly associated with altered states. Overall, the Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.

Neuroimaging studies commonly associate dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex with conscious perception. However, such studies only investigate correlation, rather than causation. In addition, many studies conflate objective performance with subjective awareness. In an influential recent paper, Rounis and colleagues addressed these issues by showing that continuous theta burst transcranial magnetic stimulation (cTBS) applied to the DLPFC impaired metacognitive (subjective) awareness for a perceptual task, while objective performance was kept constant. We attempted to replicate this finding, with minor modifications, including an active cTBS control site. Using a between-subjects design for both DLPFC and posterior parietal cortices, we found no evidence of a cTBS-induced metacognitive impairment. In a second experiment, we devised a highly rigorous within-subjects cTBS design for DLPFC, but again failed to find any evidence of metacognitive impairment. One crucial difference between our results and the Rounis study is our strict exclusion of data deemed unsuitable for a signal detection theory analysis. Indeed, when we included this unstable data, a significant, though invalid, metacognitive impairment was found. These results cast doubt on previous findings relating metacognitive awareness to DLPFC, and inform the current debate concerning whether or not prefrontal regions are preferentially implicated in conscious perception.

A novel neural signature of active visual processing has recently been described in the form of the “perceptual echo”, in which the cross-correlation between a sequence of randomly fluctuating luminance values and occipital electrophysiological signals exhibits a long-lasting periodic (∼100 ms cycle) reverberation of the input stimulus (VanRullen and Macdonald, 2012). As yet, however, the mechanisms underlying the perceptual echo and its function remain unknown. Reasoning that natural visual signals often contain temporally predictable, though nonperiodic features, we hypothesized that the perceptual echo may reflect a periodic process associated with regularity learning. To test this hypothesis, we presented subjects with successive repetitions of a rapid nonperiodic luminance sequence, and examined the effects on the perceptual echo, finding that echo amplitude linearly increased with the number of presentations of a given luminance sequence. These data suggest that the perceptual echo reflects a neural signature of regularity learning. Furthermore, when a set of repeated sequences was followed by a sequence with inverted luminance polarities, the echo amplitude decreased to the same level evoked by a novel stimulus sequence. Crucially, when the original stimulus sequence was re-presented, the echo amplitude returned to a level consistent with the number of presentations of this sequence, indicating that the visual system retained sequence-specific information, for many seconds, even in the presence of intervening visual input. Altogether, our results reveal a previously undiscovered regularity learning mechanism within the human visual system, reflected by the perceptual echo. <b>SIGNIFICANCE STATEMENT</b> How the brain encodes and learns fast-changing but nonperiodic visual input remains unknown, even though such visual input characterizes natural scenes. We investigated whether the phenomenon of “perceptual echo” might index such learning. The perceptual echo is a long-lasting reverberation between a rapidly changing visual input and evoked neural activity, apparent in cross-correlations between occipital EEG and stimulus sequences, peaking in the alpha (∼10 Hz) range. We indeed found that perceptual echo is enhanced by repeatedly presenting the same visual sequence, indicating that the human visual system can rapidly and automatically learn regularities embedded within fast-changing dynamic sequences. These results point to a previously undiscovered regularity learning mechanism, operating at a rate defined by the alpha frequency.

Science and art have long recognised that perceptual experience depends on the involvement of the experiencer. In art history, this idea is captured by Ernst Gombrich’s ‘beholder’s share’. In neuroscience, it traces to Helmholtz’s concept of ‘perception as inference’, which is enjoying renewed prominence in the guise of ‘prediction error minimization’ or the ‘Bayesian brain’. The shared idea is that our perceptual experience – whether of the world, of ourselves, or of an artwork – depends on the active ‘top-down’ interpretation of sensory input. Perception becomes a generative act, in which perceptual, cognitive, affective, and sociocultural expectations conspire to shape the brain’s ‘best guess’ of the causes of sensory signals. In this paper, I explore the parallels between the Bayesian brain and the beholders’ share, illustrated, somewhat informally, with examples from Impressionist, Expressionist, and Cubist art. By connecting phenomenological insights from these traditions with the cognitive neuroscience of predictive perception, I outline a reciprocal relationship in which art reveals phenomenological targets for neurocognitive accounts of subjectivity, while the concepts of predictive perception may in turn help make mechanistic sense of the beholder’s share. This is not standard neuroaesthetics – the attempt to discover the brain basis of aesthetic experience – nor is it any kind of neuro-fangled ‘theory of art’. It is instead an examination of one way in which art and brain science can be equal partners in revealing deep truths about human experience.

Science and art have long recognised that perceptual experience depends on the involvement of the experiencer. In art history, this idea is captured by Ernst Gombrich’s ‘beholder’s share’. In neuroscience, it traces to Helmholtz’s concept of ‘perception as inference’, which is enjoying renewed prominence in the guise of ‘prediction error minimization’ or the ‘Bayesian brain’. The shared idea is that our perceptual experience – whether of the world, of ourselves, or of an artwork – depends on the active ‘top-down’ interpretation of sensory input. Perception becomes a generative act, in which perceptual, cognitive, affective, and sociocultural expectations conspire to shape the brain’s ‘best guess’ of the causes of sensory signals. In this paper, I explore the parallels between the Bayesian brain and the beholders’ share, illustrated, somewhat informally, with examples from Impressionist, Expressionist, and Cubist art. By connecting phenomenological insights from these traditions with the cognitive neuroscience of predictive perception, I outline a reciprocal relationship in which art reveals phenomenological targets for neurocognitive accounts of subjectivity, while the concepts of predictive perception may in turn help make mechanistic sense of the beholder’s share. This is not standard neuroaesthetics – the attempt to discover the brain basis of aesthetic experience – nor is it any kind of neuro-fangled ‘theory of art’. It is instead an examination of one way in which art and brain science can be equal partners in revealing deep truths about human experience.

The 'free energy principle' (FEP) has been suggested to provide a unified theory of the brain, integrating data and theory relating to action, perception, and learning. The theory and implementation of the FEP combines insights from Helmholtzian 'perception as inference', machine learning theory, and statistical thermodynamics. Here, we provide a detailed mathematical evaluation of a suggested biologically plausible implementation of the FEP that has been widely used to develop the theory. Our objectives are (i) to describe within a single article the mathematical structure of this implementation of the FEP; (ii) provide a simple but complete agent-based model utilising the FEP; (iii) disclose the assumption structure of this implementation of the FEP to help elucidate its significance for the brain sciences.

The 'free energy principle' (FEP) has been suggested to provide a unified\ntheory of the brain, integrating data and theory relating to action,\nperception, and learning. The theory and implementation of the FEP combines\ninsights from Helmholtzian 'perception as inference', machine learning theory,\nand statistical thermodynamics. Here, we provide a detailed mathematical\nevaluation of a suggested biologically plausible implementation of the FEP that\nhas been widely used to develop the theory. Our objectives are (i) to describe\nwithin a single article the mathematical structure of this implementation of\nthe FEP; (ii) provide a simple but complete agent-based model utilising the\nFEP; (iii) disclose the assumption structure of this implementation of the FEP\nto help elucidate its significance for the brain sciences.\n

Granger-Geweke causality (GGC) is a powerful and popular method for identifying directed functional (`causal') connectivity in neuroscience. In a recent paper, Stokes and Purdon [1] raise several concerns about its use. They make two primary claims: (1) that GGC estimates may be severely biased or of high variance, and (2) that GGC fails to reveal the full structural/causal mechanisms of a system. However, these claims rest, respectively, on an incomplete evaluation of the literature, and a misconception about what GGC can be said to measure. Here we explain how existing approaches (as implemented, for example, in our popular MVGC software [2,3]) resolve the first issue, and discuss the frequently-misunderstood distinction between functional and effective neural connectivity which underlies Stokes and Purdon's second claim. [1] Patrick A. Stokes and Patrick. L. Purdon (2017), A study of problems encountered in Granger causality analysis from a neuroscience perspective, Proc. Natl. Acad. Sci. USA 114(34):7063-7072. [2] Lionel Barnett and Anil K. Seth (2012), The MVGC Multivariate Granger Causality Matlab toolbox, http://users.sussex.ac.uk/~lionelb/MVGC/ [3] Lionel Barnett and Anil K. Seth (2014), The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference, J. Neurosci. Methods 223:50-68

Granger-Geweke causality (GGC) is a powerful and popular method for\nidentifying directed functional (`causal') connectivity in neuroscience. In a\nrecent paper, Stokes and Purdon [1] raise several concerns about its use. They\nmake two primary claims: (1) that GGC estimates may be severely biased or of\nhigh variance, and (2) that GGC fails to reveal the full structural/causal\nmechanisms of a system. However, these claims rest, respectively, on an\nincomplete evaluation of the literature, and a misconception about what GGC can\nbe said to measure. Here we explain how existing approaches (as implemented,\nfor example, in our popular MVGC software [2,3]) resolve the first issue, and\ndiscuss the frequently-misunderstood distinction between functional and\neffective neural connectivity which underlies Stokes and Purdon's second claim.\n [1] Patrick A. Stokes and Patrick. L. Purdon (2017), A study of problems\nencountered in Granger causality analysis from a neuroscience perspective,\nProc. Natl. Acad. Sci. USA 114(34):7063-7072.\n [2] Lionel Barnett and Anil K. Seth (2012), The MVGC Multivariate Granger\nCausality Matlab toolbox, http://users.sussex.ac.uk/~lionelb/MVGC/\n [3] Lionel Barnett and Anil K. Seth (2014), The MVGC multivariate Granger\ncausality toolbox: A new approach to Granger-causal inference, J. Neurosci.\nMethods 223:50-68\n

Determining what constitutes practically relevant, statistically significant evidence for animal sentience, under the precautionary principle, could be enhanced through Bayesian statistics. A Bayesian approach allows the incorporation of multiple evidence sources through prior probabilities, the tracking of changing evidence across time, and a principled means of adjusting evidentiary bars via Bayes factors.

Stokes and Purdon [1] raise several concerns about the use of Granger-Geweke causality (GGC) analysis in neuroscience. They make two primary claims: (i) that GGC estimates may be severely biased or of high variance, and (ii) that GGC fails to reveal the full structural/causal mechanisms of a system. Unfortunately, these claims rest, respectively, on an incomplete evaluation of the literature and a misconception about what GGC can be said to measure. [1] Patrick A. Stokes and Patrick. L. Purdon (2017), A study of problems encountered in Granger causality analysis from a neuroscience perspective, Proc. Natl. Acad. Sci. USA, 114(34):7063-7072.

In grapheme-colour synaesthesia (GCS), the presentation of letters or numbers induces an additional 'concurrent' experience of colour. Early functional MRI (fMRI) investigations of GCS reported activation in colour-selective area V4 during the concurrent experience. However, others have failed to replicate this key finding. We reasoned that individual differences in synaesthetic phenomenology might explain this inconsistency in the literature. To test this hypothesis, we examined fMRI BOLD responses in a group of grapheme-colour synaesthetes (n=20) and matched controls (n=20) while characterising the individual phenomenology of the synaesthetes along dimensions of 'automaticity' and 'localisation'. We used an independent functional localiser to identify colour-selective areas in both groups. Activations in these areas were then assessed during achromatic synaesthesia-inducing, and non-inducing conditions; we also explored whole brain activations, where we sought to replicate the existing literature regarding synaesthesia effects. Controls showed no significant activations in the contrast of inducing > non-inducing synaesthetic stimuli, in colour-selective ROIs or at the whole brain level. In the synaesthete group, we correlated activation within colour-selective ROIs with individual differences in phenomenology using the Coloured Letters and Numbers (CLaN) questionnaire which measures, amongst other attributes, the subjective automaticity/attention in synaesthetic concurrents, and their spatial localisation. Supporting our hypothesis, we found significant correlations between individual measures of synaesthetic phenomenology and BOLD responses in colour-selective areas, when contrasting inducing against non-inducing stimuli. Specifically, left-hemisphere colour area responses were stronger for synaesthetes scoring high on phenomenological localisation and automaticity/attention, while right-hemisphere colour area responses showed a relationship with localisation only. In exploratory whole brain analyses, the BOLD response within several other areas was also correlated with these phenomenological factors, including the intra-parietal sulcus, insula, precentral and supplementary motor areas. Our findings reveal a network of regions underlying synaesthetic phenomenology and they help reconcile the diversity of previous results regarding colour-selective BOLD responses during synaesthesia, by establishing a bridge between neural responses and individual synaesthetic phenomenology.

Can consciousness be understood through an association with voluntary skeletomotor action selection? Although flexible and integrated action selection is a plausible function for consciousness, a narrow focus on skeletomotor control neglects the contributions to conscious selfhood and subjectivity that rest on interoception and autonomic regulation (internal "action"). I consider these issues from the perspective of predictive processing.

When visual input has conflicting interpretations, conscious perception can alternate spontaneously between these possible interpretations. This is called bistable perception. Previous neuroimaging studies have indicated the involvement of two right parietal areas in resolving perceptual ambiguity (ant-SPLr and post-SPLr). Transcranial magnetic stimulation (TMS) studies that selectively interfered with the normal function of these regions suggest that they play opposing roles in this type of perceptual switch. In the present study, we investigated this fractionation of parietal function by use of combined TMS with electroencephalography (EEG). Specifically, while participants viewed either a bistable stimulus, a replay stimulus, or resting-state fixation, we applied single pulse TMS to either location independently while simultaneously recording EEG. Combined with participant's individual structural magnetic resonance imaging (MRI) scans, this dataset allows for complex analyses of the effect of TMS on neural time series data, which may further elucidate the causal role of the parietal cortex in ambiguous perception.

Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error's extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictive models. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models.

We present a new 3D template atlas of the anatomical subdivisions of the macaque brain, which is based on and aligned to the magnetic resonance imaging (MRI) data set and histological sections of the Saleem and Logothetis atlas. We describe the creation and validation of the atlas that, when registered with macaque structural or functional MRI scans, provides a straightforward means to estimate the boundaries between architectonic areas, either in a 3D volume with different planes of sections, or on an inflated brain surface (cortical flat map). As such, this new template atlas is intended for use as a reference standard for macaque brain research. Atlases and templates are available as both volumes and surfaces in standard NIFTI and GIFTI formats.

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues, namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homoeostasis and allostasis, early cybernetic ideas of predictive control and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.

Abstract It is clear that prior expectations shape perceptual decision-making, yet their contribution to the construction of subjective decision confidence remains largely unexplored. We recorded fMRI data while participants made perceptual decisions and confidence judgements, controlling for potential confounds of attention. Results show that subjective confidence increases as perceptual prior expectations increasingly support the decision, and that this relationship is associated with BOLD activity in right inferior frontal gyrus (rIFG). Specifically, rIFG is sensitive to the discrepancy between expectation and decision (mismatch), and, crucially, higher mismatch responses are associated with lower decision confidence. Connectivity analyses revealed the source of the expectancy information to be bilateral orbitofrontal cortex (OFC) and the source of sensory signals to be intracalcarine sulcus. Altogether, our results indicate that predictive information is integrated into subjective confidence in rIFG, and reveal an occipital-frontal network that constructs confidence from top-down and bottom-up signals. This interpretation was further supported by exploratory findings that the white matter density of intracalcarine sulcus and OFC negatively predicted their respective contributions to the construction of confidence. Our findings advance our understanding of the neural basis of subjective perceptual processes by revealing an occipito-frontal functional network that integrates prior beliefs into the construction of confidence.

Abstract Neuroimaging studies commonly associate dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex with conscious perception. However, such studies only investigate correlation, rather than causation. In addition, many studies conflate objective performance with subjective awareness. In an influential recent paper, Rounis and colleagues addressed these issues by showing that theta burst transcranial magnetic stimulation (tbs-TMS) applied to the DLPFC impaired metacognitive (subjective) awareness for a perceptual task, while objective performance was kept constant. We attempted to replicate this finding, with minor modifications, including an active tbs-TMS control site. Using a between-subjects design for both DLPFC and posterior parietal cortices, we found no evidence of a tbs-TMS-induced metacognitive impairment. In a second experiment, we devised a highly rigorous within-subjects tbs-TMS design for DLPFC, but again failed to find any evidence of metacognitive impairment. One crucial difference between our results and the Rounis study is our strict exclusion of data deemed unsuitable for a signal detection theory analysis. Indeed, when we included this unstable data, a significant, though invalid, metacognitive impairment was found. These results cast doubt on previous findings relating metacognitive awareness to DLPFC, and inform the current debate concerning whether or not prefrontal regions are preferentially implicated in conscious perception.

Prior expectations have a powerful influence on perception, biasing both decision and confidence. However, how this occurs at the neural level remains unclear. It has been suggested that spontaneous alpha-band neural oscillations represent rhythms of the perceptual system that periodically modulate perceptual judgments. We hypothesized that these oscillations instantiate the effects of expectations. While collecting scalp EEG, participants performed a detection task that orthogonally manipulated perceptual expectations and attention. Trial-by-trial retrospective confidence judgments were also collected. Results showed that, independent of attention, prestimulus occipital alpha phase predicted the weighting of expectations on yes/no decisions. Moreover, phase predicted the influence of expectations on confidence. Thus, expectations periodically bias objective and subjective perceptual decision-making together before stimulus onset. Our results suggest that alpha-band neural oscillations periodically transmit prior evidence to visual cortex, changing the baseline from which evidence accumulation begins. In turn, our results inform accounts of how expectations shape early visual processing.

Vision in the fovea, the center of the visual field, is much more accurate and detailed than vision in the periphery. This is not in line with the rich phenomenology of peripheral vision. Here, we investigated a visual illusion that shows that detailed peripheral visual experience is partially based on a reconstruction of reality. Participants fixated on the center of a visual display in which central stimuli differed from peripheral stimuli. Over time, participants perceived that the peripheral stimuli changed to match the central stimuli, so that the display seemed uniform. We showed that a wide range of visual features, including shape, orientation, motion, luminance, pattern, and identity, are susceptible to this uniformity illusion. We argue that the uniformity illusion is the result of a reconstruction of sparse visual information (from the periphery) based on more readily available detailed visual information (from the fovea), which gives rise to a rich, but illusory, experience of peripheral vision.

It is clear that prior expectations shape perceptual decision-making, yet their contribution to the construction of subjective decision confidence remains largely unexplored. We recorded fMRI data while participants made perceptual decisions and confidence judgments, manipulating perceptual prior expectations while controlling for potential confounds of attention. Results show that subjective confidence increases as expectations increasingly support the decision, and that this relationship is associated with BOLD activity in right inferior frontal gyrus (rIFG). Specifically, rIFG is sensitive to the discrepancy between expectation and decision (mismatch), and higher mismatch responses are associated with lower decision confidence. Connectivity analyses revealed expectancy information to be represented in bilateral orbitofrontal cortex and sensory signals to be represented in intracalcarine sulcus. Together, our results indicate that predictive information is integrated into subjective confidence in rIFG, and reveal an occipital-frontal network that constructs confidence from top-down and bottom-up signals. This interpretation was further supported by exploratory findings that the white matter density of right orbitofrontal cortex negatively predicted its respective contribution to the construction of confidence. Our findings advance our understanding of the neural basis of subjective perceptual processes by revealing an occipitofrontal functional network that integrates prior beliefs into the construction of confidence. SIGNIFICANCE STATEMENT: Perceptual decision-making is typically conceived as an integration of bottom-up and top-down influences. However, perceptual decisions are accompanied by a sense of confidence. Confidence is an important facet of perceptual consciousness yet remains poorly understood. Here we implicate right inferior frontal gyrus in constructing confidence from the discrepancy between perceptual judgment and its prior probability. Furthermore, we place right inferior frontal gyrus within an occipitofrontal network, consisting of orbitofrontal cortex and intracalcarine sulcus, which represents and communicates relevant top-down and bottom-up signals. Together, our data reveal a role of frontal regions in the top-down processes enabling perceptual decisions to become available for conscious report.

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

Volitional action and self-control-feelings of acting according to one's own intentions and in being control of one's own actions-are fundamental aspects of human conscious experience. However, it is unknown whether high-level cognitive control mechanisms are affected by socially salient but nonconscious emotional cues. In this study, we manipulated free choice decisions to act or withhold an action by subliminally presenting emotional faces: In a novel version of the Go/NoGo paradigm, participants made speeded button-press responses to Go targets, withheld responses to NoGo targets, and made spontaneous, free choices to execute or withhold the response for Choice targets. Before each target, we presented emotional faces, backwards masked to render them nonconscious. In Intentional trials, subliminal angry faces made participants more likely to voluntarily withhold the action, whereas fearful and happy faces had no effects. In a second experiment, the faces were made supraliminal, which eliminated the effects of angry faces on volitional choices. A third experiment measured neural correlates of the effects of subliminal angry faces on intentional choice using EEG. After replicating the behavioural results found in Experiment 1, we identified a frontal-midline theta component-associated with cognitive control processes-which is present for volitional decisions, and is modulated by subliminal angry faces. This suggests a mechanism whereby subliminally presented "threat" stimuli affect conscious control processes. In summary, nonconscious perception of angry faces increases choices to inhibit, and subliminal influences on volitional action are deep seated and ecologically embedded.

Granger causality is well established within the neurosciences for inference\nof directed functional connectivity from neurophysiological data. These data\nusually consist of time series which subsample a continuous-time\nbiophysiological process. While it is well-known that subsampling can lead to\nimputation of spurious causal connections where none exist, here we address the\nequally important issue of the effects of subsampling on the ability to\nreliably detect causal connections which do exist.\n Neurophysiological processes typically feature signal propagation delays on\nmultiple time scales; accordingly, we base our analysis on a distributed-lag,\ncontinuous-time stochastic model, and consider Granger causality in continuous\ntime at finite prediction horizons. Via exact analytical solutions, we identify\nrelationships among sampling frequency, underlying causal time scales and\ndetectability of causalities. Our analysis reveals complex interactions between\nthe time scale(s) of neural signal propagation and sampling frequency: we\ndemonstrate that Granger causality decays exponentially as the sample time\ninterval increases beyond causal delay times, identify detectability "black\nspots" and "sweet spots", and show that subsampling may sometimes improve\ndetectability. We also demonstrate that the invariance of Granger causality\nunder causal, invertible filtering fails at finite prediction horizons. We\ndiscuss the implications of our results for inference of Granger causality at\nthe neural level from various neurophysiological recording modes, and emphasise\nthat sampling rates for causal analysis of neurophysiological time series\nshould be informed by domain-specific time scales.\n

When visual input has conflicting interpretations, conscious perception can alternate spontaneously between these possible interpretations. This is called bistable perception. Previous neuroimaging studies have indicated the involvement of two right parietal areas in resolving perceptual ambiguity (ant-SPLr and post-SPLr). Transcranial magnetic stimulation (TMS) studies that selectively interfered with the normal function of these regions suggest that they play opposing roles in this type of perceptual switch. In the present study, we investigated this fractionation of parietal function by use of combined TMS with electroencephalography (EEG). Specifically, while participants viewed either a bistable stimulus, a replay stimulus, or resting-state fixation, we applied single pulse TMS to either location independently while simultaneously recording EEG. Combined with participant’s individual structural magnetic resonance imaging (MRI) scans, this dataset allows for complex analyses of the effect of TMS on neural time series data, which may further elucidate the causal role of the parietal cortex in ambiguous perception.

Do fish consciously feel pain? Addressing this question, Key (2016) asks whether the neural mechanisms underlying conscious pain reports in humans can be identified in fish. This strategy fails in three ways. First, non-mammalian consciousness — if it exists — may depend on different mechanisms. Second, accumulating neurophysiological and behavioural evidence, evolutionary considerations, and emerging Bayesian brain theories suggest that if fish can feel at all, they can feel pain. Finally, the qualitative nature of pain and suffering obliges us, via the precautionary principle, to accommodate the possibility of its existence where doubt remains.

Abstract The action-oriented approach in cognitive science emphasizes the role of action in shaping, or constituting, perception, cognition, and consciousness. This chapter summarizes a week-long discussion on how the action-oriented approach changes our understanding of consciousness and the structure of experience, combining the viewpoints of philosophers, neuroscientists, psychologists, and clinicians. This is exciting territory, since much of the resurgent activity in consciousness science has so far focused on the neural, cognitive, and behavioral correlates of perception, independent of action. A wide-ranging discussion included questions such as how actions shape consciousness, and what determines consciousness of actions. The specific context of self-experience, from its bodily aspects to its social expression were considered. The discussions were related to specific theoretical frameworks, which emphasize the role of action in cognition, and identified an emerging empirical agenda including action-based experiments in both normal subjects and clinical populations. An intensive consideration of action is likely to have a lasting impact on how we conceive of the phenomenology and mechanisms of consciousness, and on the ways in which consciousness science will unfold in the years ahead.

Synesthesia is associated with unusual additional perceptual experiences, which are automatically and consistently triggered by specific inducing stimuli. Synesthesia is also accompanied by more general sensory and cortical changes, such as enhanced modality-specific cortical excitability. Extensive cognitive training has been shown to generate synesthesia-like phenomenology but whether these experiences are accompanied by neurophysiological changes characteristic of synesthesia remains unknown. Addressing this question provides a unique opportunity to elucidate the neural basis of perceptual plasticity relevant to conscious experiences. In a series of experiments we investigate whether extensive training of grapheme-color associations leads not only to synesthetic experiences, but also to changes in cortical excitability. Using a structured interview, we confirm that overtraining synesthetic associations results in synesthetic phenomenology. Using electroencephalography and transcranial magnetic stimulation before and after training, we find enhanced visual evoked potentials (in response to untrained patterns) and lower phosphene thresholds, demonstrating specific cortical changes. Behavioral tasks before and after training further reveal synesthesia-like performance. Control studies confirmed these results were due specifically to grapheme-color training. A passive control without training, confirmed that these results were not due to repeated testing. An active control with an analogous training regime associating abstract symbols with graphemes led to similar behavioral changes but, crucially, not neural changes or widespread phenomenological changes characteristic of synesthesia. In summary, we demonstrate cortical changes following training that are characteristic of genuine synesthesia. Collectively, our data reveal dramatic plasticity in human visual perception, expressed through a coordinated set of behavioral, neurophysiological, and phenomenological changes.

In vivo tractography based on diffusion magnetic resonance imaging (dMRI) has opened new doors to study structure-function relationships in the human brain. Initially developed to map the trajectory of major white matter tracts, dMRI is used increasingly to infer long-range anatomical connections of the cortex. Because axonal projections originate and terminate in the gray matter but travel mainly through the deep white matter, the success of tractography hinges on the capacity to follow fibers across this transition. Here we demonstrate that the complex arrangement of white matter fibers residing just under the cortical sheet poses severe challenges for long-range tractography over roughly half of the brain. We investigate this issue by comparing dMRI from very-high-resolution ex vivo macaque brain specimens with histological analysis of the same tissue. Using probabilistic tracking from pure gray and white matter seeds, we found that ∼50% of the cortical surface was effectively inaccessible for long-range diffusion tracking because of dense white matter zones just beneath the infragranular layers of the cortex. Analysis of the corresponding myelin-stained sections revealed that these zones colocalized with dense and uniform sheets of axons running mostly parallel to the cortical surface, most often in sulcal regions but also in many gyral crowns. Tracer injection into the sulcal cortex demonstrated that at least some axonal fibers pass directly through these fiber systems. Current and future high-resolution dMRI studies of the human brain will need to develop methods to overcome the challenges posed by superficial white matter systems to determine long-range anatomical connections accurately.

Can perceptual presence be explained by counterfactually-rich predictive models linking perception and action? Considering an unusually rich range of responses to this idea has led me to (1) re-emphasize the core conceptual commitment of "predictive processing of sensorimotor contingencies" (PPSMC) to predictive model-based perception, (2) reconsider the relationship between presence and objecthood, and (3) refine the phenomenological target by differentiating between perceptual presence and the phenomenology of absence-of-presence, or "phenomenal unreality." It turns out that this requires blue-sky thinking.

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations-commonplace in application domains as diverse as climate science, econometrics, and the neurosciences-induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

How do expectations influence transitions between unconscious and conscious perceptual processing? According to the influential predictive processing framework, perceptual content is determined by predictive models of the causes of sensory signals. On one interpretation, conscious contents arise when predictive models are verified by matching sensory input (minimizing prediction error). On another, conscious contents arise when surprising events falsify current perceptual predictions. Finally, the cognitive impenetrability account posits that conscious perception is not affected by such higher level factors. To discriminate these positions, we combined predictive cueing with continuous flash suppression (CFS) in which the relative contrast of a target image gradually increases over time. In four experiments we established that expected stimuli enter consciousness faster than neutral or unexpected stimuli. These effects are difficult to account for in terms of response priming, pre-existing stimulus associations, or the attentional mechanisms that cause asynchronous temporal order judgments (of simultaneously presented stimuli). Our results further suggest that top-down expectations play a larger role when bottom-up input is ambiguous, in line with predictive processing accounts of perception. Taken together, our findings support the hypothesis that conscious access depends on verification of perceptual predictions.

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct 'flavours' of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.

A lot of what our brains process never enters our consciousness, even if it may be of potential value to us. So just what are we wasting by letting our brains process stimuli we don't even notice or attend to? This is one of the areas being explored in the 16-partner CEEDs project (ceeds-project.eu). Funded by the European Commission's Future and Emerging Technologies programme, CEEDs (the Collective Experience of Empathic Data systems) has developed new sensors and technologies to unobtrusively measure people's implicit reactions to multimodal presentations of very large data sets. The idea is that monitoring these reactions may reveal when you are surprised, satisfied, interested or engaged by a part of the data, even if you're not aware of being so. Applications of CEEDs technology are relevant to a broad range of disciplines -spanning science, education, design, and archaeology, all the way through to connected retail. This chapter provides a formalisation of the CEEDs approach and its applications and in so doing explains how the CEEDs project has broken new ground in the nascent domain of human computer confluence.

### Introduction A key challenge in neuroscience and, in particular, neuroimaging, is to move beyond identification of regional activations toward the characterization of functional circuits underpinning perception, cognition, behavior, and consciousness. Granger causality (G-causality) analysis

Is there a single principle by which neural operations can account for perception, cognition, action, and even consciousness? A strong candidate is now taking shape in the form of “predictive processing”. On this theory, brains engage in predictive inference on the causes of sensory inputs by continuous minimization of prediction errors or informational “free energy”. Predictive processing can account, supposedly, not only for perception, but also for action and for the essential contribution of the body and environment in structuring sensorimotor interactions. In this paper I draw together some recent developments within predictive processing that involve predictive modelling of internal physiological states (interoceptive inference), and integration with “enactive” and “embodied” approaches to cognitive science (predictive perception of sensorimotor contingencies). The upshot is a development of predictive processing that originates, not in Helmholtzian perception-as-inference, but rather in 20th-century cybernetic principles that emphasized homeostasis and predictive control. This way of thinking leads to (i) a new view of emotion as active interoceptive inference; (ii) a common predictive framework linking experiences of body ownership, emotion, and exteroceptive perception; (iii) distinct interpretations of active inference as involving disruptive and disambiguatory—not just confirmatory—actions to test perceptual hypotheses; (iv) a neurocognitive operationalization of the “mastery of sensorimotor contingencies” (where sensorimotor contingencies reflect the rules governing sensory changes produced by various actions); and (v) an account of the sense of subjective reality of perceptual contents (“perceptual presence”) in terms of the extent to which predictive models encode potential sensorimotor relations (this being “counterfactual richness”). This is rich and varied territory, and surveying its landmarks emphasizes the need for experimental tests of its key contributions.

Responding to Wanja Wiese’s incisive commentary, I first develop the analogy between predictive processing and scientific discovery. Active inference in the Bayesian brain turns out to be well characterized by abduction (inference to the best explanation), rather than by deduction or induction. Furthermore, the emphasis on control highlighted by cybernetics suggests that active inference can be a process of “inference to the best prediction”, leading to a distinction between “epistemic” and “instrumental” active inference. Secondly, on the relationship between perceptual presence and objecthood, I recognize a distinction between the “world revealing” presence of phenomenological objecthood, and the experience of “absence of presence” or “phenomenal unreality”. Here I propose that world-revealing presence (objecthood) depends on counterfactually rich predictive models that are necessarily hierarchically deep, whereas phenomenal unreality arises when active inference fails to unmix causes “in the world” from those that depend on the perceiver. Finally, I return to control-oriented active inference in the setting of interoception, where cybernetics and predictive processing are most closely connected.

Professor Anil Seth of Sussex University discusses the hard problem of understanding consciousness with Jim Al-Khalili, in front of an audience at the Hay Festival.

Interoception refers to the sensing of internal bodily changes. Interoception interacts with cognition and emotion, making measurement of individual differences in interoceptive ability broadly relevant to neuropsychology. However, inconsistency in how interoception is defined and quantified led to a three-dimensional model. Here, we provide empirical support for dissociation between dimensions of: (1) interoceptive accuracy (performance on objective behavioural tests of heartbeat detection), (2) interoceptive sensibility (self-evaluated assessment of subjective interoception, gauged using interviews/questionnaires) and (3) interoceptive awareness (metacognitive awareness of interoceptive accuracy, e.g. confidence-accuracy correspondence). In a normative sample (N=80), all three dimensions were distinct and dissociable. Interoceptive accuracy was only partly predicted by interoceptive awareness and interoceptive sensibility. Significant correspondence between dimensions emerged only within the sub-group of individuals with greatest interoceptive accuracy. These findings set the context for defining how the relative balance of accuracy, sensibility and awareness dimensions explain cognitive, emotional and clinical associations of interoceptive ability.

Synesthesia is a condition where presentation of one perceptual class consistently evokes additional experiences in different perceptual categories. Synesthesia is widely considered a congenital condition, although an alternative view is that it is underpinned by repeated exposure to combined perceptual features at key developmental stages. Here we explore the potential for repeated associative learning to shape and engender synesthetic experiences. Non-synesthetic adult participants engaged in an extensive training regime that involved adaptive memory and reading tasks, designed to reinforce 13 specific letter-color associations. Following training, subjects exhibited a range of standard behavioral and physiological markers for grapheme-color synesthesia; crucially, most also described perceiving color experiences for achromatic letters, inside and outside the lab, where such experiences are usually considered the hallmark of genuine synesthetes. Collectively our results are consistent with developmental accounts of synesthesia and illuminate a previously unsuspected potential for new learning to shape perceptual experience, even in adulthood.

Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of "perceptual presence" has motivated "sensorimotor theories" which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative "predictive processing" theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These "counterfactually-rich" generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception.

The capacity to attend to multiple objects in the visual field is limited. However, introspectively, people feel that they see the whole visual world at once. Some scholars suggest that this introspective feeling is based on short-lived sensory memory representations, whereas others argue that the feeling of seeing more than can be attended to is illusory. Here, we investigated this phenomenon by combining objective memory performance with subjective confidence ratings during a change-detection task. This allowed us to compute a measure of metacognition--the degree of knowledge that subjects have about the correctness of their decisions--for different stages of memory. We show that subjects store more objects in sensory memory than they can attend to but, at the same time, have similar metacognition for sensory memory and working memory representations. This suggests that these subjective impressions are not an illusion but accurate reflections of the richness of visual perception.

Blindsight and other examples of unconscious knowledge and perception demonstrate dissociations between judgment accuracy and metacognition: Studies reveal that participants' judgment accuracy can be above chance while their confidence ratings fail to discriminate right from wrong answers. Here, we demonstrated the opposite dissociation: a reliable relationship between confidence and judgment accuracy (demonstrating metacognition) despite judgment accuracy being no better than chance. We evaluated the judgments of 450 participants who completed an AGL task. For each trial, participants decided whether a stimulus conformed to a given set of rules and rated their confidence in that judgment. We identified participants who performed at chance on the discrimination task, utilizing a subset of their responses, and then assessed the accuracy and the confidence-accuracy relationship of their remaining responses. Analyses revealed above-chance metacognition among participants who did not exhibit decision accuracy. This important new phenomenon, which we term blind insight, poses critical challenges to prevailing models of metacognition grounded in signal detection theory.

In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained ('random'), connection length preserving ('spatial'), and connection length optimised ('reduced') surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain.

Cognitions and emotions can be influenced by bodily physiology. Here, we investigated whether the processing of brief fear stimuli is selectively gated by their timing in relation to individual heartbeats. Emotional and neutral faces were presented to human volunteers at cardiac systole, when ejection of blood from the heart causes arterial baroreceptors to signal centrally the strength and timing of each heartbeat, and at diastole, the period between heartbeats when baroreceptors are quiescent. Participants performed behavioral and neuroimaging tasks to determine whether these interoceptive signals influence the detection of emotional stimuli at the threshold of conscious awareness and alter judgments of emotionality of fearful and neutral faces. Our results show that fearful faces were detected more easily and were rated as more intense at systole than at diastole. Correspondingly, amygdala responses were greater to fearful faces presented at systole relative to diastole. These novel findings highlight a major channel by which short-term interoceptive fluctuations enhance perceptual and evaluative processes specifically related to the processing of fear and threat and counter the view that baroreceptor afferent signaling is always inhibitory to sensory perception.

Grapheme-color synesthesia is a perceptual experience where graphemes, letters or words evoke a specific color, which are experienced either as spatially coincident with the grapheme inducer (projector sub-type) or elsewhere, perhaps without a definite spatial location (associator sub-type). Here, we address the question of whether synesthesia can be rapidly produced using a hypnotic color suggestion to examine the possibility of "hypnotic synesthesia", i.e., subjectively experienced color hallucinations similar to those experienced by projector synesthetes. We assess the efficacy of this intervention using an "embedded figures" test, in which participants are required to detect a shape (e.g., a square) composed of local graphemic elements. For grapheme-color synesthetes, better performance on the task has been linked to a higher proportion of graphemes perceived as colored. We found no performance benefits on this test when using a hypnotic suggestion, as compared to a no-suggestion control condition. The same result was found when participants were separated according to the degree to which they were susceptible to the suggestion (number of colored trials perceived). However, we found a relationship between accuracy and subjective reports of color in those participants who reported a large proportion of colored trials: trials in which the embedded figure was accurately recognized (relative to trials in which it was not) were associated with reports of more intense colors occupying a greater spatial extent. Collectively, this implies that hypnotic color was only perceived after shape detection rather than aiding in shape detection via color-based perceptual grouping. The results suggest that hypnotically induced colors are not directly comparable to synesthetic ones.

Investigation of synesthesia phenomenology in adults is needed to constrain accounts of developmental trajectories of this trait. We report an extended phenomenological investigation of sequence-space synesthesia in a single case (AB). We used the Elicitation Interview (EI) method to facilitate repeated exploration of AB's synesthetic experience. During an EI the subject's attention is selectively guided by the interviewer in order to reveal precise details about the experience. Detailed analysis of the resulting 9 h of interview transcripts provided a comprehensive description of AB's synesthetic experience, including several novel observations. For example, we describe a specific spatial reference frame (a "mental room") in which AB's concurrents occur, and which overlays his perception of the real world (the "physical room"). AB is able to switch his attention voluntarily between this mental room and the physical room. Exemplifying the EI method, some of our observations were previously unknown even to AB. For example, AB initially reported to experience concurrents following visual presentation, yet we determined that in the majority of cases the concurrent followed an internal verbalization of the inducer, indicating an auditory component to sequence-space synesthesia. This finding is congruent with typical rehearsal of inducer sequences during development, implicating cross-modal interactions between auditory and visual systems in the genesis of this synesthetic form. To our knowledge, this paper describes the first application of an EI to synesthesia, and the first systematic longitudinal investigation of the first-person experience of synesthesia since the re-emergence of interest in this topic in the 1980's. These descriptions move beyond rudimentary graphical or spatial representations of the synesthetic spatial form, thereby providing new targets for neurobehavioral analysis.

OPINION article Front. Psychol., 14 August 2014Sec. Consciousness Research https://doi.org/10.3389/fpsyg.2014.00896

Abstract: Low frequency noise performance is the key indicator in determining the signal to noise ratio of a capacitively coupled sensor when used to acquire electroencephalogram signals. For this reason, a prototype Electric Potential Sensor device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10 Hz or less. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and have diameters of 12 mm or 18 mm. In both cases, the sensors are housed in inert stainless steel machined housings with the electronics fabricated in surface mount components on a printed circuit board compatible with epoxy potting compounds. Potted sensors are designed to be immersed in alcohol for sterilization purposes. A comparative study was conducted with a commercial wet gel electrode system. These studies comprised measurements of both free running electroencephalogram and Event Related Potentials. Quality of the recorded electroencephalogram was assessed using three methods of inspection of raw signal, comparing signal to noise ratios, and Event Related Potentials noise analysis. A strictly comparable signal to noise ratio was observed and the overall conclusion from these comparative studies is that

Identifying with a body is central to being a conscious self. The now classic "rubber hand illusion" demonstrates that the experience of body-ownership can be modulated by manipulating the timing of exteroceptive (visual and tactile) body-related feedback. Moreover, the strength of this modulation is related to individual differences in sensitivity to internal bodily signals (interoception). However the interaction of exteroceptive and interoceptive signals in determining the experience of body-ownership within an individual remains poorly understood. Here, we demonstrate that this depends on the online integration of exteroceptive and interoceptive signals by implementing an innovative "cardiac rubber hand illusion" that combined computer-generated augmented-reality with feedback of interoceptive (cardiac) information. We show that both subjective and objective measures of virtual-hand ownership are enhanced by cardio-visual feedback in-time with the actual heartbeat, as compared to asynchronous feedback. We further show that these measures correlate with individual differences in interoceptive sensitivity, and are also modulated by the integration of proprioceptive signals instantiated using real-time visual remapping of finger movements to the virtual hand. Our results demonstrate that interoceptive signals directly influence the experience of body ownership via multisensory integration, and they lend support to models of conscious selfhood based on interoceptive predictive coding.

The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Bayesian brain and predictive coding approaches within cognitive science. To date, this perspective has been applied primarily to exteroceptive perception (e.g., vision, audition), and action. Here, I describe a predictive, inferential perspective on interoception: 'interoceptive inference' conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents. The model generalizes 'appraisal' theories that view emotions as emerging from cognitive evaluations of physiological changes, and it sheds new light on the neurocognitive mechanisms that underlie the experience of body ownership and conscious selfhood in health and in neuropsychiatric illness.

The Bayesian brain hypothesis provides an attractive unifying framework for perception, cognition, and action. We argue that the framework can also usefully integrate interoception, the sense of the internal physiological condition of the body. Our model of "interoceptive predictive coding" entails a new view of emotion as interoceptive inference and may account for a range of psychiatric disorders of selfhood.

Analyzing metacognition, specifically knowledge of accuracy of internal perceptual, memorial, or other knowledge states, is vital for many strands of psychology, including determining the accuracy of feelings of knowing and discriminating conscious from unconscious cognition. Quantifying metacognitive sensitivity is however more challenging than quantifying basic stimulus sensitivity. Under popular signal-detection theory (SDT) models for stimulus classification tasks, approaches based on Type II receiver-operating characteristic (ROC) curves or Type II d-prime risk confounding metacognition with response biases in either the Type I (classification) or Type II (metacognitive) tasks. A new approach introduces meta-d': The Type I d-prime that would have led to the observed Type II data had the subject used all the Type I information. Here, we (a) further establish the inconsistency of the Type II d-prime and ROC approaches with new explicit analyses of the standard SDT model and (b) analyze, for the first time, the behavior of meta-d' under nontrivial scenarios, such as when metacognitive judgments utilize enhanced or degraded versions of the Type I evidence. Analytically, meta-d' values typically reflect the underlying model well and are stable under changes in decision criteria; however, in relatively extreme cases, meta-d' can become unstable. We explore bias and variance of in-sample measurements of meta-d' and supply MATLAB code for estimation in general cases. Our results support meta-d' as a useful measure of metacognition and provide rigorous methodology for its application. Our recommendations are useful for any researchers interested in assessing metacognitive accuracy.

There is growing evidence that for a range of dynamical systems featuring complex interactions between large ensembles of interacting elements, mutual information peaks at order-disorder phase transitions. We conjecture that, by contrast, information flow in such systems will generally peak strictly on the disordered side of a phase transition. This conjecture is verified for a ferromagnetic 2D lattice Ising model with Glauber dynamics and a transfer entropy-based measure of systemwide information flow. Implications of the conjecture are considered, in particular, that for a complex dynamical system in the process of transitioning from disordered to ordered dynamics (a mechanism implicated, for example, in financial market crashes and the onset of some types of epileptic seizures); information dynamics may be able to predict an imminent transition.

Mental functions are influenced by states of physiological arousal. Afferent neural activity from arterial baroreceptors at systole conveys the strength and timing of individual heartbeats to the brain. We presented words under limited attentional resources time-locked to different phases of the cardiac cycle, to test a hypothesis that natural baroreceptor stimulation influences detection and subsequent memory of words. We show memory for words presented around systole was decreased relative to words at diastole. The deleterious memory effect of systole was greater for words detected with low confidence and amplified in individuals with low interoceptive sensitivity, as indexed using a heartbeat counting task. Our observations highlight an important cardiovascular channel through which autonomic arousal impacts a cognitive function, an effect mitigated by metacognition (perceptual confidence) and interoceptive sensitivity.

A key step toward understanding how the brain works is the reliable characterization of directed functional (i.e., causal) connectivity between brain regions, generally during various states of wake and sleep, as well as during task performance. Here we perform a critical analysis of the potential of Granger causality (GC) [1] to confront this challenge. We describe new methodology for rigorously applying GC to steady-state data, validated against analytical calculations, and for the first time, simulated spiking neuron local field potential data. We apply our methods to scalp and intracranial electroencephalographic (EEG) recordings. The concept of GC is to quantify the extent to which the past of one signal Y predicts the future of another signal X. For 'pairwise' GC, one compares predictions based on the past of just × and on the past of both × and Y. For 'conditional' GC, one quantifies the extent to which the past of Y assists in predicting the future of × beyond the extent to which the pasts of × and all other variables excluding Y predict the future of X. These approaches lead to distinct functional connectivity maps. The conditional approach potentially describes better how each system component processes and communicates distinct information; however it is practically more awkward because it requires estimating more parameters. We review the elegant mathematical properties of the linear autoregressive approach to GC that make it an attractive connectivity measure, e.g. its links to information theory and its insensitivity to amplification level [2,3]. We then discuss challenges in the application to electrophysiology data, such as non-stationarity and statistical bias. We present ways to overcome these challenges, involving data segmentation and permutation analysis [4], and modifications to the basic linear autoregression model. We validate our methods in two ways: first using simulated data from models for which true GC values can be analytically derived; second using simulated local field potential data from a large-scale spiking neuron simulator. The latter allows us to confirm, for the first time, that GC analysis can indeed correctly reproduce the underlying causal structure in realistic neural dynamics. We compare and contrast the ability of pairwise and conditional GC approaches to detect significant changes in functional connectivity between wake, sleep and anaesthesia states across recorded scalp and intracranial EEG variables. Analyses are performed in both the time and frequency domains, and we make use of the first ever code for computing conditional GC in the frequency domain. As predicted, pairwise GC is more sensitive to changes in brain state than full conditional GC; however conditional GC performed on just a few simultaneous variables at once can also give meaningful results. In summary, we demonstrate rigorous methodology and new code for GC analysis of steady-state data, and illustrate the utility of GC in exposing the functional neural interactions underlying different brain states.

This joint article reflects the authors' personal views regarding noteworthy advances in the neuroscience of consciousness in the last 10 years, and suggests what we feel may be promising future directions. It is based on a small conference at the Samoset Resort in Rockport, Maine, USA, in July of 2012, organized by the Mind Science Foundation of San Antonio, Texas. Here, we summarize recent advances in our understanding of subjectivity in humans and other animals, including empirical, applied, technical, and conceptual insights. These include the evidence for the importance of fronto-parietal connectivity and of "top-down" processes, both of which enable information to travel across distant cortical areas effectively, as well as numerous dissociations between consciousness and cognitive functions, such as attention, in humans. In addition, we describe the development of mental imagery paradigms, which made it possible to identify covert awareness in non-responsive subjects. Non-human animal consciousness research has also witnessed substantial advances on the specific role of cortical areas and higher order thalamus for consciousness, thanks to important technological enhancements. In addition, much progress has been made in the understanding of non-vertebrate cognition relevant to possible conscious states. Finally, major advances have been made in theories of consciousness, and also in their comparison with the available evidence. Along with reviewing these findings, each author suggests future avenues for research in their field of investigation.

The clinical assessment of non-communicative brain damaged patients is extremely difficult and there is a need for paraclinical diagnostic markers of the level of consciousness. In the last few years, progress within neuroimaging has led to a growing body of studies investigating vegetative state and minimally conscious state patients, which can be classified in two main approaches. Active neuroimaging paradigms search for a response to command without requiring a motor response. Passive neuroimaging paradigms investigate spontaneous brain activity and brain responses to external stimuli and aim at identifying neural correlates of consciousness. Other passive paradigms eschew neuroimaging in favour of behavioural markers which reliably distinguish conscious and unconscious conditions in healthy controls. In order to furnish accurate diagnostic criteria, a mechanistic explanation of how the brain gives rise to consciousness seems desirable. Mechanistic and theoretical approaches could also ultimately lead to a unification of passive and active paradigms in a coherent diagnostic approach. In this paper, we survey current passive and active paradigms available for diagnosis of residual consciousness in vegetative state and minimally conscious patients. We then review the current main theories of consciousness and see how they can apply in this context. Finally, we discuss some avenues for future research in this domain.

This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.

Watching someone scratch himself can induce feelings of itchiness in the perceiver. This provides a unique opportunity to characterize the neural basis of subjective experiences of itch, independent of changes in peripheral inputs. In this study, we first established that the social contagion of itch is essentially a normative response (experienced by most people), and that the degree of contagion is related to trait differences in neuroticism (i.e., the tendency to experience negative emotions), but not to empathy. Watching video clips of someone scratching (relative to control videos of tapping) activated, as indicated by functional neuroimaging, many of the neural regions linked to the physical perception of itch, including anterior insular, primary somatosensory, and prefrontal (BA44) and premotor cortices. Moreover, activity in the left BA44, BA6, and primary somatosensory cortex was correlated with subjective ratings of itchiness, and the responsivity of the left BA44 reflected individual differences in neuroticism. Our findings highlight the central neural generation of the subjective experience of somatosensory perception in the absence of somatosensory stimulation. We speculate that the habitual activation of this central "itch matrix" may give rise to psychogenic itch disorders.

Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information' and 'causal density'. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.

Oscillating neuronal circuits, known as central pattern generators (CPGs), are responsible for generating rhythmic behaviours such as walking, breathing and chewing. The CPG model alone however does not account for the ability of animals to adapt their future behaviour to changes in the sensory environment that signal reward. Here, using multi-electrode array (MEA) recording in an established experimental model of centrally generated rhythmic behaviour we show that the feeding CPG of Lymnaea stagnalis is itself associated with another, and hitherto unidentified, oscillating neuronal population. This extra-CPG oscillator is characterised by high population-wide activity alternating with population-wide quiescence. During the quiescent periods the CPG is refractory to activation by food-associated stimuli. Furthermore, the duration of the refractory period predicts the timing of the next activation of the CPG, which may be minutes into the future. Rewarding food stimuli and dopamine accelerate the frequency of the extra-CPG oscillator and reduce the duration of its quiescent periods. These findings indicate that dopamine adapts future feeding behaviour to the availability of food by significantly reducing the refractory period of the brain's feeding circuitry.

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness.

Consciousness has of late become a "hot topic" in neuroscience. Empirical work has centered on identifying potential neural correlates of consciousness (NCCs), with a converging view that the prefrontal parietal network (PPN) is closely associated with this process. Theoretical work has primarily sought to explain how informational properties of this cortical network could account for phenomenal properties of consciousness. However, both empirical and theoretical research has given less focus to the psychological features that may account for the NCCs. The PPN has also been heavily linked with cognitive processes, such as attention. We describe how this literature is under-appreciated in consciousness science, in part due to the increasingly entrenched assumption of a strong dissociation between attention and consciousness. We argue instead that there is more common ground between attention and consciousness than is usually emphasized: although objects can under certain circumstances be attended to in the absence of conscious access, attention as a content selection and boosting mechanism is an important and necessary aspect of consciousness. Like attention, working memory and executive control involve the interlinking of multiple mental objects and have also been closely associated with the PPN. We propose that this set of cognitive functions, in concert with attention, make up the core psychological components of consciousness. One related process, chunking, exploits logical or mnemonic redundancies in a dataset so that it can be recoded and a given task optimized. Chunking has been shown to activate PPN particularly robustly, even compared with other cognitively demanding tasks, such as working memory or mental arithmetic. It is therefore possible that chunking, as a tool to detect useful patterns within an integrated set of intensely processed (attended) information, has a central role to play in consciousness. Following on from this, we suggest that a key evolutionary purpose of consciousness may be to provide innovative solutions to complex or novel problems.

Action selection is the task of doing the right thing at the right time. It requires the assessment of available alternatives, executing those most appropriate, and resolving conflicts among competing goals and possibilities. Using advanced computational modelling, this book explores cutting-edge research into action selection in nature from a wide range of disciplines, from neuroscience to behavioural ecology, and even political science. It delivers new insights into both detailed and systems-level attributes of natural intelligence and demonstrates advances in methodological practice. Contributions from leading researchers cover issues including whether biological action selection is optimal, neural substrates for action selection in the vertebrate brain, perceptual selection in decision making, and interactions between group and individual action selection. This first integrated review of action selection in nature contains a balance of review and original research material, consolidating current knowledge into a valuable reference for researchers while illustrating potential paths for future studies.

Parts of this book were originally published as an issue of the Philosophical Transactions of the Royal Society B: Biological Sciences (Volume 362; Issue 1485) but have been modified and updated. Anil Seth's contribution to the book was supported by EPSRC Leadership Fellowship EP/G007543/1 and by a donation from the Dr Mortimer and Theresa Sackler Foundation. Tony Prescott's contribution was supported by the EU 7th Framework Programme via the projects BIOTACT (ICT-215910) and EFAA (ICT-270490). Joanna Bryson's effort was funded in part by the US Air Force Office of Scientific Research, Air Force Material Command, USAF, under grant number FA8655-10-1-3050.

Action selection is the task of deciding ‘what to do next’. As a general problem facing all autonomous entities – animals and artificial agents – it has exercised both the sciences concerned with understanding the biological bases of behaviour (e.g., ethology, neurobiology, psychology) and those concerned with building artefacts (e.g., artificial intelligence, artificial life, and robotics). The problem has two parts: what constitutes an action, and how are actions selected?

When an animal does one thing rather than another, it is natural to ask ‘why?’ A common explanation is that the action is optimal with respect to some goal. For example, when observing the foraging behaviour of a shorebird, one may ask whether the intake of food is being maximised. This ‘normative’ view, a direct extension of Darwinian principles, has its more recent roots in behavioural ecology (Krebs and Davies, 1997) and optimal foraging theory (Stephens and Krebs, 1986). Adopting a normative perspective on action selection can be very useful in placing constraints on possible underlying mechanisms, for developing and comparing theoretical frameworks relating behaviour to mechanism, and for explaining instances of apparently irrational or suboptimal behaviour. The seven chapters within this section present new insights and modelling results relevant to each of these issues. They also connect with the other parts of this book in important ways. The constraints on underlying mechanisms are thoroughly explored by the computational neuroscience models described in Part II, and patterns of both rational and irrational social behaviour are encountered in a variety of forms in Part III.

The problem of action selection has two components: what is selected? How is it selected? To understand what is selected, it is necessary to recognise that animals do not choose among behaviours per se; rather, behaviour reflects observed interactions among brains, bodies, and environments (embeddedness). To understand what guides selection, it is useful to take a normative, functional perspective that evaluates behaviour in terms of a fitness metric. This perspective can be especially useful for understanding apparently irrational action selection. Bringing together these issues therefore requires integrating function and mechanism in models of action selection. This chapter describes 'optimised agent-based modelling', a methodology that integrates functional and mechanistic perspectives in the context of embedded agent–environment interactions. Using this methodology, I demonstrate that successful action selection can arise from the joint activity of parallel, loosely coupled sensorimotor processes, and I show how an instance of apparently suboptimal decision making (the matching law) can be accounted for by adaptation to competitive foraging environments.

A central and largely unsolved problem in the brain sciences is to understand the functional architecture of the vertebrate nervous system. Many questions about this architecture revolve around the issue of action selection. Because it is a fundamental property of neurons to be selective with regard to the patterns of input activity to which they respond, claims that particular brain subsystems are specifically or preferentially involved in the selection of action, as distinct to other aspects of control, must meet more stringent requirements (see below). It is also by no means inevitable that the functional decomposition of the brain will contain specialist action-selection mechanisms (see Seth, this volume). Appropriate behavioural switching could be a global property of nervous system function, and of its embedding in a body and environment, that cannot be attributed to specific subcomponents of brain architecture. In other words, it is plausible that an animal may ‘flip’ from one integrated pattern of behavioural output to another without some identifiable internal ‘switch’ being thrown. On the other hand, theoretical arguments can be presented, based for instance on the benefits that accrue from modularity (Bryson, 2005; Prescott et al., 1999; Wagner and Altenberg, 1996), to suggest that biological control systems may include specialised action-selection components. Hence, one important debate in this field is whether there are specialised mechanisms for action selection in animal nervous systems, and, if so, where these might be found (see also Prescott, 2007, for an evolutionary perspective on this question).

In nature, action selection is rarely purely an individual matter; rather, adaptive action selection usually involves a social context. As Seth demonstrates in Part I, the apparently irrational behaviour of an individual viewed in isolation can, in fact, be optimal when considered in a semi-social context that includes competing conspecifics. Importantly, the agents in Seth's model express no explicitly social behaviour – there are no direct costs or benefits associated with social interactions. Rather, agents in the model interact indirectly via their effects on resource distribution in the environment. This demonstrates the ubiquity of social phenomena in nature, which of course will, in general, lead to selective pressure for social adaptations.

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A range of studies in the past decade and a half indicate significant impacts of tool use on body image. In cases of intentional action, contractions of near space or experienced extensions of limbs have been shown when using tools such as rakes. It remains unclear whether the changes in body image are effected by the tool enabling perception at a distance or action/manipulation of the environment at a distance. We studied this issue using a new research tool, the Enactive Torch, a sensory substitution device specifically designed for research into perception and bodily action. The Enactive Torch allows perception at a distance without the capacity for distal action. We report a first experiment indicating that its use on a navigation task has no effect on body image.

An outstanding challenge in neuroscience is to develop theoretically grounded and practically applicable quantitative measures that are sensitive to conscious level. Such measures should be high for vivid alert conscious wakefulness, and low for unconscious states such as dreamless sleep, coma and general anaesthesia. Here, we describe recent progress in the development of measures of dynamical complexity, in particular causal density and integrated information. These and similar measures capture in different ways the extent to which a system's dynamics are simultaneously differentiated and integrated. Because conscious scenes are distinguished by the same dynamical features, these measures are therefore good candidates for reflecting conscious level. After reviewing the theoretical background, we present new simulation results demonstrating similarities and differences between the measures, and we discuss remaining challenges in the practical application of the measures to empirically obtained data.

The cognitive sciences are increasingly coming to terms with the embodied, embedded, extended, and experiential aspects of the mind. Exemplifying this shift, the enactive approach points to an essential role of goal-directed bodily activity in the generation of meaningful perceptual experience, i.e., sense-making. Here, building on recent insights into the transformative effects of practical tool-use, we make use of the enactive approach in order to provide a definition of an enactive interface in terms of augmented sense-making. We introduce such a custom-built interface, the Enactive Torch, and present a study of its experiential effects. The results demonstrate that the user experience is not adequately captured by any standardly assumed perceptual modality; rather, it is a new feeling that is mediated by the design of the device and shaped by the overall situation of the task. Taken together these findings show that there is much to be gained by synergies between engineering and the cognitive sciences in the creation of new experience-centered technology. We suggest that the guiding principle should be the design of interfaces that serve as a transparent medium for augmenting our natural skills of interaction with the world, instead of requiring conscious attention to the interface as an opaque object in the world.

A recent measure of 'integrated information', Φ(DM), quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, Φ(DM) is defined only for discrete Markov systems, which are unusual in biology; as a result, Φ(DM) can rarely be measured in practice. Here, we describe two new measures, Φ(E) and Φ(AR), that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities.

Dopaminergic neurons in the mammalian substantia nigra display characteristic phasic responses to stimuli which reliably predict the receipt of primary rewards. These responses have been suggested to encode reward prediction-errors similar to those used in reinforcement learning. Here, we propose a model of dopaminergic activity in which prediction-error signals are generated by the joint action of short-latency excitation and long-latency inhibition, in a network undergoing dopaminergic neuromodulation of both spike-timing dependent synaptic plasticity and neuronal excitability. In contrast to previous models, sensitivity to recent events is maintained by the selective modification of specific striatal synapses, efferent to cortical neurons exhibiting stimulus-specific, temporally extended activity patterns. Our model shows, in the presence of significant background activity, (i) a shift in dopaminergic response from reward to reward-predicting stimuli, (ii) preservation of a response to unexpected rewards, and (iii) a precisely timed below-baseline dip in activity observed when expected rewards are omitted.

This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologically inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neuroscience. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans.

This chapter reviews the use of neural systems in robotics, with particular emphasis on strongly biologically inspired neural networks and methods. As well as describing work at the research frontiers, the paper provides some historical background in order to clarify the motivations and scope of work in this field. There are two major sections that make up the bulk of the chapter: one surveying the application of artificial neural systems to robot control, and one describing the use of robots as tools in neuroscience. The former concentrates on biologically derived neural architectures and methods used to drive robot behaviours, and the latter introduces a closely related area of research where robotic models are used as tools to study neural mechanisms underlying the generation of adaptive behaviour in animals and humans.Request access from your librarian to read this chapter's full text.

After more than a century of neglect, the last two decades have seen a significant amount of progress in the science of consciousness (Seth, 2010). This resurgence of interest has been largely driven by the availability of increasingly sophisticated neuroscientific methods. However,

Abstract A satisfying neuroscience of consciousness must account for phenomenological properties in terms of neural properties. While pursuing this project may challenge our intuitions about what we are conscious of, evidence from behavior and introspection should not be discounted. All three lines of evidence need to be integrated in order to naturalize phenomenal experience.

The past decade has seen an explosion of interest in the neural mechanisms underlying consciousness. Experimental approaches from cognitive neuroscience that emphasize converging evidence from multiple methodologies have changed our understanding of how conscious mental states are associated with patterns of brain activity. In this special issue of Cognitive Neuroscience, we bring together five new empirical contributions to this literature plus a new theoretical discussion paper and associated peer commentaries.

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.

Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness.

OPINION article Front. Psychol., 10 March 2010Sec. Consciousness Research Volume 1 - 2010 | https://doi.org/10.3389/fpsyg.2010.00005

For people with synaesthesia letters and numbers may evoke experiences of colour. It has been previously demonstrated that these synaesthetes may be better at detecting a triangle made of 2s among a background of 5s if they perceive 5 and 2 as having different synaesthetic colours. However, other studies using this task (or tasks based on the same principle) have failed to replicate the effect or have suggested alternative explanations of the effect. In this study, we repeat the original study on a larger group of synaesthetes (n = 36) and include, for the first time, an assessment of their self-reported colour experiences. We show that synaesthetes do have a general advantage over controls on this task. However, many synaesthetes report no colour experiences at all during the task. Synaesthetes who do report colour typically experience around one third of the graphemes in the display as coloured. This is more consistent with theories of synaesthesia in which spatial attention needs to be deployed to graphemes for conscious colour experiences to emerge than the interpretation based on 'pop-out'.

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

Machine (artificial) consciousness can be interpreted in both strong and weak forms, as an instantiation or as a simulation. Here, I argue in favor of weak artificial consciousness, proposing that synthetic models of neural mechanisms potentially underlying consciousness can shed new light on how these mechanisms give rise to the phenomena they do. The approach I advocate involves using synthetic models to develop "explanatory correlates" that can causally account for deep, structural properties of conscious experience. In contrast, the project of strong artificial consciousness — while not impossible in principle — has yet to be credibly illustrated, and is in any case less likely to deliver advances in our understanding of the biological basis of consciousness. This is because of the inherent circularity involved in using models both as instantiations and as cognitive prostheses for exposing general principles, and because treating models as instantiations can indefinitely postpone comparisons with empirical data.

Webb’s lucid critique of the animat modeling approach in adaptive behavior (AB) is very welcome. It provides an excellent opportunity to clarify how models in the field can achieve greater biological relevance. Webb makes a strong case for the importance of targeting specific animal systems, as opposed to analyzing made-up (animat) models. There are, however, many roads to biological relevance, including but not limited to the close targeting of models to specific animals. Moreover, models which deliberately depart from close targeting can have biological relevance precisely because of their departures.

This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspective.Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS) tradition and dynamical systems are treated in a state space modeling framework.The nature of the fMRI signal is reviewed with emphasis on the involved neuronal, physiological and physical processes and their modeling as dynamical systems.In this context, two streams of development in modeling causal brain connectivity using fMRI are discussed: time series approaches to causality in a discrete time tradition and dynamic systems and control theory approaches in a continuous time tradition.This review closes with discussion of ongoing work and future perspectives on the integration of the two approaches.

The concept of emergence is central to artificial life and complexity science, yet quantitative, intuitive, and easy-to-apply measures of emergence are surprisingly lacking. Here, I introduce a just such a measure, G-emergence, which operationalizes the notion that an emergent process is both dependent upon and autonomous from its underlying causal factors. G-emergence is based on a nonlinear time series analysis adapted from ‘Granger causality’ and it provides a measure not only of emergence but also of apparent ‘downward causation’. I illustrate the measure by application to a canonical example of emergence, an agent-based simulation of bird flocking, and I discuss its potential impact on perhaps the most challenging of all scientific problems involving emergence: consciousness.

We analyzed the pre-S and S region of hepatitis B virus by polymerase chain reaction in 60 chronic carriers. In 10 hepatitis B e antigen-positive chronic carriers, a single DNA band was detected at the expected size, whereas additional shorter DNA bands were observed in some cases with chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. The smaller-sized DNA bands from chronic hepatitis patients had the deletion of 183 bp in the pre-S gene, and the deletion of 492 bp in S gene. The reasons of the appearance of these mutants are unknown, but it may be interesting to study in relationship to the mechanism of host immune response.

This commentary considers Merker's mesodiencephalic proposal in relation to quantitative measures of neural dynamics suggested to be relevant to consciousness. I suggest that even if critical neural mechanisms turn out to be subcortical, the functional utility of consciousness will depend on the rich conscious contents generated by continuous interaction of such mechanisms with a thalamocortical envelope.

Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.

The problem of action selection has two components: what is selected and how is it selected? To understand what is selected, it is necessary to distinguish between behavioural and mechanistic levels of description. Animals do not choose between behaviours per se; rather, behaviour reflects interactions among brains, bodies and environments. To understand what guides selection, it is useful to take a normative perspective that evaluates behaviour in terms of a fitness metric. This perspective, rooted in behavioural ecology, can be especially useful for understanding apparently irrational choice behaviour. This paper describes a series of models that use artificial life (AL) techniques to address the above issues. We show that successful action selection can arise from the joint activity of parallel, loosely coupled sensorimotor processes. We define a class of AL models that help to bridge the ecological approaches of normative modelling and agent- or individual-based modelling (IBM). Finally, we show how an instance of apparently suboptimal decision making, the matching law, can be accounted for by adaptation to competitive foraging environments.

We describe a theoretical network analysis that can distinguish statistically causal interactions in population neural activity leading to a specific output. We introduce the concept of a causal core to refer to the set of neuronal interactions that are causally significant for the output, as assessed by Granger causality. Because our approach requires extensive knowledge of neuronal connectivity and dynamics, an illustrative example is provided by analysis of Darwin X, a brain-based device that allows precise recording of the activity of neuronal units during behavior. In Darwin X, a simulated neuronal model of the hippocampus and surrounding cortical areas supports learning of a spatial navigation task in a real environment. Analysis of Darwin X reveals that large repertoires of neuronal interactions contain comparatively small causal cores and that these causal cores become smaller during learning, a finding that may reflect the selection of specific causal pathways from diverse neuronal repertoires.

A model of consciousness is a theoretical description that relates brain properties of consciousness (e.g., fast irregular electrical activity, widespread brain activation) to phenomenal properties of consciousness (e.g., qualia, a first-person-perspective, the unity of a conscious scene). Because of the diverse nature of these properties (Seth et al. 2005), useful models can be either mathematical/logical or verbal/conceptual.

A recent theoretical emphasis on complex interactions within neural systems underlying consciousness has been accompanied by proposals for the quantitative characterization of these interactions. In this article, we distinguish key aspects of consciousness that are amenable to quantitative measurement from those that are not. We carry out a formal analysis of the strengths and limitations of three quantitative measures of dynamical complexity in the neural systems underlying consciousness: neural complexity, information integration, and causal density. We find that no single measure fully captures the multidimensional complexity of these systems, and all of these measures have practical limitations. Our analysis suggests guidelines for the specification of alternative measures which, in combination, may improve the quantitative characterization of conscious neural systems. Given that some aspects of consciousness are likely to resist quantification altogether, we conclude that a satisfactory theory is likely to be one that combines both qualitative and quantitative elements.

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.

Most primate societies are characterised by hierarchical dominance structures. Males are usually dominant over females, but in periods of sexual attraction (during females period of tumescence) male ‘tolerance ’ towards females rises. (Hemelrijk, 2002) shows in a model that this ‘tolerance’ is created as a side effect due to the rise of female dominance during periods of sexual attraction. This rise is in turn the consequence of the more frequent approaches of males towards females during these periods. In Hemelrijk’s model the males gain no benefit from ‘tolerating ’ females and they only do so at high aggression levels as a kind of ‘respectful timidity’, because some of the females have become dominant over them. This paper replicates and examines the results of Hemelrijk’s study. We have found that some of Hemelrijk’s results are highly reliant on aspects of the model that are not well supported by the current primate literature. We analyse the mechanisms underlying her results, and suggest data that should be sought from observation logs of real primate colonies that would support or overturn the model. 1

Abstract: The metacognitive stance of Smith et al. (2003) risks ignoring sensory consciousness. Although Smith et al. rightly caution against the tendency to preserve the uniqueness of the human mind at all costs, their reasoned stance is undermined by a selective association of consciousness with high-level cognitive operations. Neurobiological evidence may offer a more general, and hence more inclusive, basis for the systematic study of animal consciousness.

Effective visual object recognition requires mechanisms to bind object features (e.g. color, shape and motion) while distinguishing distinct objects. Synchronously active neuronal circuits among reentrantly connected cortical areas may provide a basis for visual binding. To assess the potential of this mechanism, we have constructed a mobile brain-based device, Darwin VIII, which is guided by simulated analogues of cortical and sub-cortical areas required for visual processing, decision-making, reward and motor responses. These simulated areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. Darwin VIII learns to discriminate among multiple objects with shared visual features and associates 'target' objects with innately preferred auditory cues. We observed the co-activation of globally distributed neuronal circuits that corresponded to distinct objects in Darwin VIII's visual field. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, are necessary for successful discrimination. By situating Darwin VIII in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated movement, and by recording its behavioral and neuronal responses in detail, we were able to show that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects.

Whiskers are widely used by many animal species for navigation and texture discrimination. This paper describes Darwin IX, a mobile physical device equipped with artificial whiskers, the behavior of which is controlled by a neural simulation based on the rat somatosensory system. During its autonomous behavior, Darwin IX is able to discriminate among textures in its environment and learns to avoid textures that are paired with aversive events.

How does environmental structure influence the dynamics of adaptive behavior and its underlying mechanisms? By analyzing the neural controller of a simulated head/eye system, we show that a specific measure—“neural complexity”—can be selectively sensitive to neural dynamics underlying rich adaptive behavior. Evolutionary algorithms were used to generate neural network controllers able to support target fixation in environmental and phenotypic conditions of qualitatively different complexity. Networks that evolved in rich conditions showed higher behavioral flexibility and robustness, and higher neural complexity, than networks that evolved in simple conditions. The magnitude of neural complexity, which reflects a balance between dynamical integration and dynamical segregation, depended on properties of both the environment and the head/eye phenotype. These results show that neurally complex dynamics can accompany adaptive behavior in rich environmental and phenotypic conditions; they are consistent with the proposal that neural complexity may represent a common property of the functional organization of adaptive neural systems.

We describe the construction and performance of `brain-based devices¿ (BBDs), physical devices whose behaviour is controlled by simulated nervous systems modelled on vertebrate neuroanatomy and neurophysiology, that carry out perceptual categorization and selective conditioning to visual and textural stimuli. BBDs take input from the environment through on-board sensors including cameras, microphones and artificial whiskers, and take action based on experiential learning. BBDs have a large-scale neural simulation, a phenotype, a body plan, and the means to learn through autonomous exploration. Key neural mechanisms in the present BBDs include synaptic plasticity, reward or value systems, reentrant connectivity, the dynamic synchronization of neuronal activity, and neuronal units with spatiotemporal response properties. With our BBDs, as with animals, it is the interaction of these neural mechanisms with the sensorimotor correlations generated by active sensing and self motion that is responsible for adaptive behaviour. BBDs permit analysis of activity at all levels of the nervous system during behaviour, and as such they provide a rich source of heuristics for generating hypotheses regarding brain function. Moreover, by taking inspiration from systems neuroscience, BBDs provide a novel architecture for the design of neuromorphic systems.

Sensory signals from whiskers are typically rich in both spatial and temporal structure, and are used by many animals to guide a variety of adaptive behaviors. To explore possible neural mechanisms underlying whisker-guided behavior, we constructed Darwin IX, a mobile physical device equipped with artificial whiskers and a neural simulation based on the rat somatosensory (whisker) system. We show that neuronal units with time-lagged response properties, together with the selective modulation of neural connection strengths, provide a plausible neural mechanism for the spatiotemporal transformations of sensory input necessary for both texture discrimination and selective conditioning to textures.

BACKGROUND: Hepatitis A virus (HAV) vaccination is recommended worldwide for patients with chronic liver disease to prevent decompensation due to superinfection with HAV. India being endemic for HAV, the prevalence of pre-existing antibodies against HAV due to subclinical exposure to the virus in childhood among patients with chronic liver disease may be high and, therefore, vaccination may not be needed. However, little data are available on the prevalence of HAV antibody among patients with chronic liver disease in India. METHODS: All patients with chronic liver disease seen at Gastroenterology Center, Army Hospital R and R, New Delhi during the year 2002 and diagnosed to have either chronic liver disease were tested for the presence of IgG anti-HAV antibody in their sera (using a commercial ELISA kit). All patients with acute exacerbation or rapid deterioration of a preexisting chronic liver disease were separately studied for presence of IgM anti-HAV. In addition, a matched number of patients who attended the center due to diseases other than liver disease were also studied as controls. RESULTS: One hundred and eighty seven patients of chronic liver disease and 89 controls were studied. Mean age of these two groups was 38.6 and 42.1 years and 153 (81.8%) and 78 (87.6%) of them were males respectively. Etiology of chronic liver disease was HBV infection in 91(48.7%), HCV infection in 62 (33.2%), autoimmune chronic hepatitis in 3 (1.6%), PBC in seven (3.7%) and cryptogenic 24 (12.8%). Of these 179 (95.7%) patients tested positive for IgG anti-HAV. A total of 37 hospitalisations in 29 patients were noted during the study period due to acute exacerbation of pre-existing chronic liver disease. None of these were positive for IgM anti-HAV, while 28 were positive for IgG anti-HAV. Among the controls, 87 controls (94.6%) were positive IgG anti-HAV. The prevalence of anti-HAV positivity was similar among patients with various etiologies. CONCLUSION: Vaccination against HAV is not routinely required among patients with chronic liver disease in India as there is a very high prevalence of pre-existing antibodies in these patients. HAV superinfection as a cause of acute exacerbation of chronic liver disease was not seen in this.

This paper presents evidence indicating that the signals generated by neural responses to visual input can be either enhanced by increasing or suppressed by decreasing the area of the stimuli to which attention is directed. We used magnetoencephalography (MEG) to measure the frequency-tagged steady-state visual evoked responses of 11 subjects presented with two superimposed images flickering at different frequencies. Each image consisted of seven parallel bars of equal length; in any image, all bars were either red or green and either horizontal or vertical. At randomly chosen times during the experiments, any one of the three middle bars in either image transiently increased or decreased in width. Subjects were asked to attend to one image and ignore the other and to respond to changes in bar width in the attended image with a key press. In one condition, subject responses were required for changes in any of the three central bars of the attended image. We found that visual steady-state evoked responses to the attended image were enhanced relative to those evoked by the unattended image in this condition. In a second condition, subject responses were required for changes only in the middle bar. In this condition, the responses to the attended image were suppressed relative to those of the unattended image. These results may reflect relative differences in the synchronization and desynchronization of responding neuronal populations.

The introduction of noise into an evolutionary iterated prisoner's dilemma model can promote the evolution of strategy memory. In this paper, an analysis in terms of Ashby's (1956) law of requisite variety enables a distinction to be drawn between the adaptive evolution of memory and non-adaptive evolution, or drift, a distinction which, in such situations, is remarkably insensitive to fitness statistics. As part of this, it is demonstrated how the influence of noise can depend on its locus in the evolving system. Additional evidence is presented for a second influence of noise in facilitating drift, an influence which can be interpreted in terms of noise-induced genotype-phenotype degeneracy.

The author shows a class of type-two feasible functionals, C/sub 2/, that satisfies Cook's conditions, (1990) and cannot be expressed as the lambda closure of type-one poly-time functions and any recursively enumerable set of type-two feasible functionals. Further, no class of total type-two functionals containing this class is representable as the lambda closure of a recursively enumerable set of type-two total computable functionals and type-one poly-time functions. The definition of C/sub 2/ provides a clear computational procedure for functionals of C/sub 2/. Using functionals of class C/sub 2/ a more general notion of polynomial-time reducibility between two arbitrary type-one functions can be introduced.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

By constructing and analyzing a physically situated brain-based device (i.e. a device with sensors and actuators whose behavior is guided by a simulated nervous system), we show that reentrant connectivity and dynamic synchronization can provide an effective mechanism for binding the visual features of objects.

An interferometric sensor for monitoring the localised strains and vibrations is presented. The modified Michelson interferometer is employed to process the signal received from the sensing interferometer. Details of the sensor amplifier and signal processing scheme which can provide real-time monitoring of surface vibrations (amplitude, frequency and direction of motion) are also presented.

We give examples of classes of rigid structures which are of unbounded rigidity but least fixed point (Partial fixed point) logic can express all Boolean PTIME (PSPACE) queries on these classes. This shows that definability of linear order in FO+LEP although sufficient for it to capture Boolean PTIME queries, is not necessary even on the classes of rigid structures. The situation however appears very different for nonzero-ary queries. Next, we turn to the study of fixed point logics on arbitrary classes of structures. We completely characterize the recursively enumerable classes of finite structures on which PFP captures all PSPACE queries of arbitrary arities. We also state in some alternative forms several natural necessary and some sufficient conditions for PFP to capture PSPACE queries on classes of finite structures. The conditions similar to the ones proposed above work for LFP and PTIME also in some special cases but to prove the same necessary conditions in general for LFP to capture PTIME seems harder and remains open.

Let Q be a finite set of generalized quantifiers. By L/sup k/(Q) we denote the k-variable fragment of FO(Q), first order logic extended with Q. We show that for each k, there is a PFP(Q)-definable linear pre-order whose equivalence classes in any finite structure 21 are the L/sup k/(Q)-types in 21. For some special classes of generalized quantifiers Q, we show that such an ordering of L/sup k/(Q)-types is already definable in IFP(Q). As applications of the above results, we prove some generalizations of the Abiteboul-Vianu theorem. For instance, we show that for any finite set Q of modular counting quantifiers, P=PSPACE if, and only if, IFP(Q)=PFP(Q) over finite structures. On the other hand, we show that an ordering of L/sup k/(Q)-types is not always definable in IFP(Q). Indeed, we construct a single, polynomial time computable quantifier P such that the equivalence relation /spl equiv//sup k,P/, and hence ordering on L/sup k/(P)-types, is not definable in IFP(P).

Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshop's eight speakers, plus a section designed to reflect the debates that took place during the workshop's discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor.

A new interpretation of Godfrey-Smith's `environmental complexity thesis' is described which draws together two broad themes; the relation of functional properties of behaviour to environmental structure, and the distinction between behavioural and mechanistic levels of description. The specific idea defended here is that behavioural and/or mechanistic complexity can be understood in terms of mediating well-adapted responses to environmental variability. Particular attention is paid to the value of agent-based modelling within this framework.

The matching law describes how individual foragers often allocate their choices, occasionally suboptimally, in experimental situations. The `ideal free distribution' predicts how groups of foraging agents should distribute themselves, optimally, over patchy environments. This paper explores the possibility that a single behavioural heuristic can account for both phenomena, allowing the potential suboptimality of matching to be understood in terms of adaptation to a group context. Two simple heuristics are compared, epsilon-sampling and omega-sampling: the latter is successful in both cases, but contrary to claims in the literature the former is successful in neither. These results emphasise the importance of multiple environmental value estimates in effective decision making.

In this article a series of agent-based models support the hypothesis that behaviors adapted to a group situation may be suboptimal (or “irrational”) when expressed by an isolated individual. These models focus on two areas of current concern in behavioral ecology and experimental psychology: the “interference function” (which relates the intake rate of a focal forager to the density of conspecifics) and the “matching law” (which formalizes the observation that many animals match the frequency of their response to different stimuli in proportion to the reward obtained from each stimulus type). Each model employs genetic algorithms to evolve foraging behaviors for multiple agents in spatially explicit environments, structured at the level of situated perception and action. A second concern of this article is to extend the understanding of both matching and interference per se by modeling at this level.

As the Simulation of Adaptive Behaviour (SAB) field continues to mature, it is essential that general methodological positions become elaborated into practical programmes of research. This paper describes how a particular flavour of SAB modelling - the use of genetic algorithms to design situated agent (animat) architectures - can effectively complement `optimal foraging theory&amp;apos;, as it is understood in theoretical biology. This allows several fundamental problems that arise directly out of the framework of orthodox optimal foraging theory to be addressed, but, as with any trade-off, is not without disadvantages of its own. 1. Introduction This paper considers the methodological potential of a set of models at one intersection of the overlapping fields of artificial life (AL) and the simulation of adaptive behaviour (SAB). These models, which for want of an appropriate existing term shall be called Individual-based Optimal Situated (IOS) models, derive from the larger clas...

This thesis presents an externalist exploration of the relations between behaviour, mechanism, and environment, as they arise in a variety of agent-environment systems. It offers contributions at conceptual, methodological, and empirical levels of discourse. Externalism describes the attempt to understand the internal in terms of the external, and the thesis begins by developing a conceptual framework justifying the use of artificial evolution models in the application of this perspective to agent-environment systems. In particular, it is argued that such models play a crucial role in elaborating the distinction between behavioural and mechanistic levels of description. There follows a series of models, of both game-theoretic and evolutionary-robotic character, which focus on explaining internal ‘complexity’ in terms of adaptation to (external) environmental variability. As part of this project, accounts of the ‘evolution of complexity’ in general are critiqued, and the practical importance of noise in artificial evolution is discussed. The thesis continues with an integration of this externalist project with the well established theoretical biology methodology of ‘optimal foraging theory’. A novel methodology ‘individualbased optimal situated modelling’ is described, which extends orthodox optimal foraging theory through (1) the use of artificial evolution as an optimisation procedure and, (2) modelling agentenvironment interaction at the level of situated perception and action. The conceptual leverage afforded by this extension is illustrated in its application to the problem of behaviour coordination in a simple agent-environment system; for example, the need for a dedicated ‘action selection mechanism’ is questioned. The methodology is then addressed to a range of issues in contemporary theoretical biology and psychology: the ‘interference function’, the ‘ideal free distribution’, and the ‘individual matching law’, issues which are united by a concern with individual choice and its collective consequences. A series of models are presented which demonstrate, in these contexts, that (1) behaviours for which there is debate about the level of complexity required for their underlying mechanism, can be subserved by surprisingly simple mechanisms, and (2) behaviours which may be irrational when expressed by an isolated individual can be understood as rational in a group context. Submitted for the degree of D. Phil. University of Sussex

In alert dogs with electrodes implanted into the kidney nerves the afferent activity following the influence upon kidney receptors, was studied with the aid of lazix and mannitol administration against the background of the water-milk uptake. The water-milk uptake was followed by no significant changes of nervous activity. Metabolic disturbances due to lazix altered the kidney nerves activity. Acceleration of the kidney filtration due to mannitol was followed by a short-lasting unstable decrease in the afferent nerves activity.

The hypothesis that environmental variability promotes the evolution of organism complexity is explored and illustrated, in two contexts. A coevolutionary `Iterated Prisoner&amp;apos;s Dilemma&amp;apos; (IPD) ecology, populated by strategies determined by variable length genotypes, provides a quantitative demonstration, and an example from evolutionary robotics (ER) provides a more qualitative and naturalistic exploration. In the ER example, the above hypothesis is illustrated in real environments, and the organism complexity is seen in robots exhibiting relatively complex behaviours and neural dynamics. Implications are drawn for the emergence of complexity in general, and also for artificial evolution as a design methodology. Introduction The general principle that there is organism complexity by virtue of environmental complexity, has a substantial historical pedigree. This is prominent in the work of Ashby (1952), and, earlier, Dewey (1929), and has enjoyed more recent attention from Godfrey-Smith ...

The evolution of complexity is investigated in the context of an `iterated prisoner&amp;apos;s dilemma&amp;apos; (IPD) co-evolutionary/game-theoretic ecology, populated by strategies determined by variable length genotypes. New evidence is found to support the dual hypotheses that both uncertainty, and interaction (by way of population stability), foster the evolution of progressively more complex entities. It is also argued that during periods of major evolutionary upheaval, complex entities suffer disproportionately and become less abundant in the population. The research is presented as an elaboration of the general principle that there is complexity in an organism by virtue of complexity in the environment, and has implications both for deepening understanding of the nature of biological evolution and for guiding the progress of artificial evolution. 1 Introduction As Stephen Jay Gould [6] has consistently pointed out, the age of bacteria is not about to end anytime soon. Yet it can hardly be deni...

In this paper we extend the “Oracle Turing Machine” model to compute functionals of all finite types. Using this model we define analogs of class C 1 (type 2 basic feasible functionals), of [11], in all finite types. We get two apparently different classes which we call D and E when C 1 is generalized to finite types. These classes correspond to two different ways of querying higher type inputs. Both these classes are shown to satisfy the necessary conditions proposed by Cook [5], which any class of feasible functionals must satisfy. Class E, as expected, turns out to be the BFF, thus providing a more natural computational characterization of higher type basic feasible functionals. Class D is the same as BFF for type 2, but appears to be larger than BFF for types 3 and above; however, showing separation between these two classes is open. The question, “Is class D larger than class E?” is equivalent to the question “Does computing the indices of subprograms used to query higher type inputs add to the computational power compared to the case when only a fixed finite number of parameterizable functionals are used for querying all higher type inputs? ”. We also briefly touch upon the preservation of higher complexity functional classes by feasible functionals [12].

Five experiments examined within-event learning in rats by inducing an appetite for one of the elements (salt) of a compound stimulus and assessing preference for the other element (almond). Almond preference was conditional upon (1) the almond flavour having been presented in compound with the salt, and (2) the assessment being conducted when the rats were out of sodium balance (Experiment 1). Presentations of the compound in one environment (A) and of the salt and almond elements in a second environment (B) resulted in greater almond preference when rats were tested in A than in B (Experiment 2). Almond preference was reduced when separate presentations of the compound and almond (Experiment 3) or of the compound and salt (Experiment 4) occurred in the same environments but not when these presentations occurred in different environments. Rats exposed to the compound in A and then extinguished to the elements in either A or B showed a reduced almond preference when tested in the extinction environment, but not when tested in the other environment (Experiment 5). Thus, extinction of within-event learning is context-specific and subject to renewal. The results were interpreted in terms of an associative model whereby separate presentations of the elements result in a symmetrical inhibitory link which is contextually gated (Bouton, 1993).

The findings for 14 risk variables were correlated with the results of coronary angiography in 8807 patients enrolled in the multicenter Coronary Artery Surgery Study. Discriminant function analysis revealed that age, sex, cigarette smoking and level of blood cholesterol best distinguished between the groups with (6688 patients) and without (2119 patients) coronary artery disease (CAD). The relative risk for CAD in patients with the combination of cigarette smoking and an elevated cholesterol level was high in women 55 years old or younger and in men 35 years old or younger. Few women 45 years old or younger (7 out of 97) had CAD when neither of these risk factors were present. Despite these correlations, only limited gains accrued from the use of discriminant function analysis in correctly allocating patients into disease and nondisease groups. Extent and severity of disease in 15298 patients with CAD were significantly (p less than 0.001) but modestly correlated with age, sex, blood cholesterol level and history of diabetes or hypertension. No positive correlation occurred between the arteriographic measures of disease and the cigarette smoking history. Thus, factors influencing the presence of disease may differ from those influencing its extent and severity.

Summary. A single application of paraquat at 0·56 kg/ha applied in 224 l/ha gave short‐lived control of Mikania cordata. An increase in duration of control was obtained either by increasing the dose from 0·28 kg/ha to 1·12 kg/ha with a constant volume rate (224 l/ha) or by using a constant dose (0·56 kg/ha) and reducing the volume from 896 l/ha to 112 l/ha. Complete eradication of M. cordata was achieved by applying paraquat at 0·56 kg/ha or 0·28 kg/ha followed 2‐3 weeks later by a second application at 0·28 kg/ha in 112 l/ha. La lutte contre Mikania cordata ( Burm. F. ) B. L. Robinson dans les cultures au moyen du paraquat

Summary. Paraquat‐based treatments were evaluated for the control of Imperata cylindrica (L.) Beauv., a perennial grass with extensive rhizomes of great regenerative power. Single applications of 0–5 lb/ac paraquat gave good initial control, but regeneration was rapid. Increases up to 2–0 lb/ac had little effect on the duration of control, nor did the volume rate from 40 to 120 gal/ac. When an initial application of 05 lb/ac was followed by two further applications of 0–25 lb/ac, when the level of control had decreased to 50%, lasting control was obtained. Application of 6–8 lb/ac dalapon followed by 0–25 lb/ac paraquat also gave good control, comparable with that achieved by the conventional treatment of dalapon alone at 15–20 lb/ac. La lutte chimique contre Imperata cylindrica (L.) Beauv. en Malaisie Résumé. Des traitements utilisant le paraquat ont étè essayés pour lutter contre Imperata cylindrica (L.) Beauv., graminée pérenne avec des rhizomes importants qui manifestent un pouvoir de régénération élevé, Des applications uniques de paraquat à la dose 0,56 kg/ha ement un bon eifel initial mais la régéneration fut rapide. L'accroissement de la dose jusquà 2,2 kg/ha n'eut qu'une faible incidence sur la durée de l'action herbicide, l'augmentation du volume de traitement de 450 à 1350 1/ha ne provoqua pas non plus d'augmentation de I'efficacité. Une seule application de paraquat à 0,56 kg/ha, quand le niveau de I'efficacité eut décru jusquà 50%, suivie de deux applications supplémen‐taires à 0,275 kg/ha, donna une efficacité plus durable. Des applications de dalapon de 6,7 à 9,0 kg/ha suivies de traitements au paraquat à 0,275 kg/ha donnèerent un résultat aussi bon que celui obtenu par les traitements habituels au dalapon seul, à la dose dc 16 à 22 kg/ha. Chemisehe Bekampfung von Imperata cylindrica (L.) Beauv. in Malaysia Zusammenfassung. Es erfolgten Untersuchungen zur Bekämpfung von Imperata cylindrica (L.) Beauv., einem mehrjährigen Gras mit einem ausgedehnten Rhizomsystem und starker Regenerationskraft, mit Paraquat, Einmalige Applikation von 0,56 kg/ha Paraquat hatte zwar eine gute Anfangswirkung, doch erfolgte rasche Regeneration. Eine Erhöhung bis auf 2,2 kg/ha war ebenso wie eine Änderung der Spritzbrühmenge von ca 450–1350 I/ha von geringem Einfluss auf die Nachhaltigkeit der Bekämpfungs‐massnahmen. Eine einmalige Anwendung von 0,56 kg/ha Paraquat gefolgt von zwei weiteren Applikationen von 0,28 kg/ha, wenn die Regeneration ungelahr 50% erreicht hatte, ergab nachhaltige Wirkung. Behandlungen mit 6,7‐9 kg/ha Dalapon gefolgt von 0,28 kg/ha Paraquat hatten ebenfalis eine gute Wirkung, die mit der einer konven‐tionellen Behandlung mit 16,8‐22 kg/ha Dalapon vergleiehbar war.

The effects of auxin on the inhibition of lateral buds in decapitated bean plants are enhanced if kinetin is applied together with auxin. The uptake of (14)C-indoleacetic acid by the stumps of decapitated plants is increased in the presence of kinetin and leads to extensive transport of (14)C-indoleacetic acid in the stems. The increased bud inhibition resulting when auxin and kinetin are applied together may be due to greater amounts of auxin reaching the buds, but an alternative explanation is that metabolites are directed from the buds to the point of hormone application.