Fast-moving stars around an intermediate-mass black hole in ω Centauri.

Black holes have been found over a wide range of masses, from stellar remnants with masses of 5-150 solar masses (M☉), to those found at the centres of galaxies with M > 105M☉. However, only a few debated candidate black holes exist between 150M☉ and 105M☉. Determining the population of these intermediate-mass black holes is an important step towards understanding supermassive black hole formation in the early universe1,2. Several studies have claimed the detection of a central black hole in ω Centauri, the most massive globular cluster of the Milky Way3-5. However, these studies have been questioned because of the possible mass contribution of stellar mass black holes, their sensitivity to the cluster centre and the lack of fast-moving stars above the escape velocity6-9. Here we report the observations of seven fast-moving stars in the central 3 arcsec (0.08 pc) of ω Centauri. The velocities of the fast-moving stars are significantly higher than the expected central escape velocity of the star cluster, so their presence can be explained only by being bound to a massive black hole. From the velocities alone, we can infer a firm lower limit of the black hole mass of about 8,200M☉, making this a good case for an intermediate-mass black hole in the local universe.

Theories of consciousness.

Recent years have seen a blossoming of theories about the biological and physical basis of consciousness. Good theories guide empirical research, allowing us to interpret data, develop new experimental techniques and expand our capacity to manipulate the phenomenon of interest. Indeed, it is only when couched in terms of a theory that empirical discoveries can ultimately deliver a satisfying understanding of a phenomenon. However, in the case of consciousness, it is unclear how current theories relate to each other, or whether they can be empirically distinguished. To clarify this complicated landscape, we review four prominent theoretical approaches to consciousness: higher-order theories, global workspace theories, re-entry and predictive processing theories and integrated information theory. We describe the key characteristics of each approach by identifying which aspects of consciousness they propose to explain, what their neurobiological commitments are and what empirical data are adduced in their support. We consider how some prominent empirical debates might distinguish among these theories, and we outline three ways in which theories need to be developed to deliver a mature regimen of theory-testing in the neuroscience of consciousness. There are good reasons to think that the iterative development, testing and comparison of theories of consciousness will lead to a deeper understanding of this most profound of mysteries.

Hybrid predictive coding: Inferring, fast and slow.

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.

Trial-by-trial predictions of subjective time from human brain activity.

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.

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference.

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.

A Predictive Processing Model of Episodic Memory and Time Perception.

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.

Greater than the parts: a review of the information decomposition approach to causal emergence.

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'.

I overthink-Therefore I am not: An active inference account of altered sense of self and agency in depersonalisation disorder.

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.

A continuity of Markov blanket interpretations under the free-energy principle.

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.

Learning action-oriented models through active inference.

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.

Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data.

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.

Decreased directed functional connectivity in the psychedelic state.

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.

Split-Brain: What We Know Now and Why This is Important for Understanding Consciousness.

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.

A supermassive black hole in an ultra-compact dwarf galaxy.

Ultra-compact dwarf galaxies are among the densest stellar systems in the Universe. These systems have masses of up to 2 × 10(8) solar masses, but half-light radii of just 3-50 parsecs. Dynamical mass estimates show that many such dwarfs are more massive than expected from their luminosity. It remains unclear whether these high dynamical mass estimates arise because of the presence of supermassive black holes or result from a non-standard stellar initial mass function that causes the average stellar mass to be higher than expected. Here we report adaptive optics kinematic data of the ultra-compact dwarf galaxy M60-UCD1 that show a central velocity dispersion peak exceeding 100 kilometres per second and modest rotation. Dynamical modelling of these data reveals the presence of a supermassive black hole with a mass of 2.1 × 10(7) solar masses. This is 15 per cent of the object's total mass. The high black hole mass and mass fraction suggest that M60-UCD1 is the stripped nucleus of a galaxy. Our analysis also shows that M60-UCD1's stellar mass is consistent with its luminosity, implying a large population of previously unrecognized supermassive black holes in other ultra-compact dwarf galaxies.

Individual differences in the tendency to see the expected.

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.

A single system account of enhanced recognition memory in synaesthesia.

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.

Neurite orientation and dispersion density imaging (NODDI) detects cortical and corticospinal tract degeneration in ALS.

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.

Intentional Binding Without Intentional Action.

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.

Face perception enhances insula and motor network reactivity in Tourette syndrome.

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.

Visual Perceptual Echo Reflects Learning of Regularities in Rapid Luminance Sequences.

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.SIGNIFICANCE STATEMENT 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.