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Background Autism spectrum disorder (ASD) affects many aspects of life, from social interactions to (multi)sensory processing. Similarly, the condition expresses at a variety of levels of description, from genetics to neural circuits and interpersonal behavior. We attempt to bridge between domains and levels of description by detailing the behavioral, electrophysiological, and putative neural network basis of peripersonal space (PPS) updating in ASD during a social context, given that the encoding of this space relies on appropriate multisensory integration, is malleable by social context, and is thought to delineate the boundary between the self and others. Methods Fifty (20 male/30 female) young adults, either diagnosed with ASD or age- and sex-matched individuals, took part in a visuotactile reaction time task indexing PPS, while high-density electroencephalography was continuously recorded. Neural network modeling was performed in silico. Results Multisensory psychophysics demonstrates that while PPS in neurotypical individuals shrinks in the presence of others—as to “give space”—this does not occur in ASD. Likewise, electroencephalography recordings suggest that multisensory integration is altered by social context in neurotypical individuals but not in individuals with ASD. Finally, a biologically plausible neural network model shows, as a proof of principle, that PPS updating may be inflexible in ASD owing to the altered excitatory/inhibitory balance that characterizes neural circuits in animal models of ASD. Conclusions Findings are conceptually in line with recent statistical inference accounts, suggesting diminished flexibility in ASD, and further these observations by suggesting within an example relevant for social cognition that such inflexibility may be due to excitatory/inhibitory imbalances.

Anxious individuals are known to show impaired decision-making in economic gambling task and in everyday life decisions. This impairment can be due to aversion to uncertainty about outcomes (risk aversion) and/or aversion to negative outcomes (loss aversion). We investigate how non-clinical individuals with high levels of Generalised Anxiety Disorder (GAD) (N = 54) behave compared to less anxious subjects (N = 61) in a gambling decision-making task delivered online and designed to separate the distinct influences of risk and loss aversion on decision-making. By modelling subjects’ choices using computational models derived from Prospect Theory and fitted using Hierarchical Bayesian methods, we estimate individual levels of risk and loss aversion. We also link estimates of these parameters to individual propensity to risk averse behaviours during the COVID pandemic, like wearing safer types of face masks, or completing a COVID vaccination course. We report increased loss aversion in individuals with increased level of GAD compared to less anxious individuals and no differences in risk aversion. We also report no links between risk and loss aversion and attitudes towards COVID and vaccines. These results shed new light on the interplay of anxiety and risk and loss aversion and they can provide useful directions for clinical intervention.1Author SummaryIn everyday life, we are constantly faced with decisions in which we need to balance between possible losses and risks. In general, humans are believed to weight losses more than gains, i.e. they are loss averse, and they prefer certain to uncertain outcomes, i.e. they are risk averse. The COVID pandemic made this balance between risks, losses and everyday life even more explicit: should I get a COVID vaccination? What type of mask should I wear? Here, we investigate these constructs using a gambling task delivered online in which participants have to accept or refuse bets involving monetary gains, losses or sure wins. We also capture individual attitudes to COVID and vaccines with a new self-report questionnaire. We find that individuals with high levels of Generalised Anxiety Disorder present higher levels of loss aversion compared to less anxious individuals and no differences in risk aversion. We also report no links between computational measures of risk and loss aversion and attitude to COVID and vaccines, possibly due to limits in using simple tasks to describe behaviours through complex situations like the COVID pandemic.

Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their prior beliefs about the environment. Since then, multiple studies have attempted to test this theory and its subsequent predictive coding formulations. In this comprehensive review, we collect all relevant studies from the past ten years which included comparisons between autistic and neurotypical individuals or measured the participants’ autistic traits. We categorize them based on the type of the investigated priors and synthesize their findings. Our results show mixed evidence overall, with a slight majority of studies finding no general impairment in the integration of Bayesian priors. We show that priors developed during the experiments were more frequently impaired than those that participants had acquired previously, with various studies providing evidence for learning differences between participant groups. Together these findings hint at a deficit in the development of priors in autism. We also focus on the methodological and computational aspects of the included studies, finding low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.

BackgroundDepression is a challenge to diagnose reliably and the current gold standard for trials of DSM-5 has been in agreement between two or more medical specialists. Research studies aiming to objectively predict depression have typically used brain scanning. Less expensive methods from cognitive neuroscience may allow quicker and more reliable diagnoses, and contribute to reducing the costs of managing the condition. In the current study we aimed to develop a novel inexpensive system for detecting elevated symptoms of depression based on tracking face and eye movements during the performance of cognitive tasks.MethodsIn total, 75 participants performed two novel cognitive tasks with verbal affective distraction elements while their face and eye movements were recorded using inexpensive cameras. Data from 48 participants (mean age 25.5 years, standard deviation of 6.1 years, 25 with elevated symptoms of depression) passed quality control and were included in a case-control classification analysis with machine learning.ResultsClassification accuracy using cross-validation (within-study replication) reached 79% (sensitivity 76%, specificity 82%), when face and eye movement measures were combined. Symptomatic participants were characterised by less intense mouth and eyelid movements during different stages of the two tasks, and by differences in frequencies and durations of fixations on affectively salient distraction words.ConclusionsElevated symptoms of depression can be detected with face and eye movement tracking during the cognitive performance, with a close to clinically-relevant accuracy (~80%). Future studies should validate these results in larger samples and in clinical populations.

Eating disorders are associated with one of the highest mortality rates among all mental disorders, yet there is very little research about them within the newly emerging and promising field of computational psychiatry. As such, we focus on investigating a previously unexplored, yet core aspect of eating disorders–body image dissatisfaction. We continue a freshly opened debate about model-based learning and its trade-off against model-free learning–a proxy for goal-directed and habitual behaviour. We perform a behavioural study that utilises a two-step decision-making task and a reinforcement learning model to understand the effect of body image dissatisfaction on model-based learning in a population characterised by high scores of disordered eating and negative appearance beliefs, as recruited using Prolific. We find a significantly reduced model-based contribution in the body image dissatisfaction task condition in the population of interest as compared to a healthy control. This finding suggests general deficits in deliberate control in this population, leading to habitual, compulsive-like behaviours (body checking) dominating the experience. Importantly, the results may inform treatment approaches, which could focus on enhancing the reliance on goal-directed decision making to help cope with unwanted behaviours.

Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.

This scientific commentary refers to ‘Taking others into account: combining directly experienced and indirect information in schizophrenia’ by Simonsen et al. (doi:10.1093/brain/awab065).

Autism spectrum disorders have been proposed to arise from impairments in the probabilistic integration of prior knowledge with sensory inputs. Circular inference is one such possible impairment, in which excitation-to-inhibition imbalances in the cerebral cortex cause the reverberation and amplification of prior beliefs and sensory information. Recent empirical work has associated circular inference with the clinical dimensions of schizophrenia. Inhibition impairments have also been observed in autism, suggesting that signal reverberation might be present in that condition as well. In this study, we collected data from 21 participants with diagnosed autism spectrum disorders and 155 participants with a broad range of autistic traits in an online probabilistic decision-making task (the fisher task). We used previously established Bayesian models to investigate possible associations between autism or autistic traits and circular inference. No differences in prior or likelihood reverberation were found between autistic participants and those with no diagnosis. Similarly, there was no correlation between any of the circular inference model parameters and autistic traits across the whole sample. Furthermore, participants incorporated information from both priors and likelihoods in their decisions, with no relationship between their weights and psychiatric traits, contrary to what common theories for both autism and schizophrenia would suggest. These findings suggest that there is no increased signal reverberation in autism, despite the known presence of excitation-to-inhibition imbalances. They can be used to further contrast and refine the Bayesian theories of schizophrenia and autism, revealing a divergence in the computational mechanisms underlying the two conditions.Author SummaryPerception results from the combination of our sensory inputs with our brain’s previous knowledge of the environment. This is usually described as a process of Bayesian inference or predictive coding and is thought to underly a multitude of cognitive modalities. Impairments in this process are thought to explain various psychiatric disorders, in particular autism and schizophrenia, for which similar Bayesian theories have been proposed despite important differences in their symptoms. Recently, a new model of Bayesian impairment in schizophrenia has been proposed and validated using behavioural experiments, called the “circular inference” model. In the current study, we used the same task and computational modelling to explore whether circular inference could also account for autism spectrum disorder. We find that participants with autistic traits or diagnoses of autism do not present increased levels of circularity. This is the first study to investigate circular inference in autism, and one of the very few to explore possible autism and schizophrenia impairments with the same task and identical analytical methods. Our findings indicate one potential way in which the explanations of the two conditions might differ.

BackgroundExperience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression.MethodsThirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression.ResultsReward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression.ConclusionsOur findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.

Autism spectrum disorder (ASD) is a heterogenous disorder predominantly characterized by social and communicative differences, but increasingly recognized to also alter (multi)sensory function. To face the heterogeneity and ubiquity of ASD, researchers have proposed models of statistical inference operating at the level of ‘computations’. Here, we attempt to bridge both across domains – from social to sensory – and levels of description – from behavioral computations to neural ensemble activity to a biologically-plausible artificial neural network – in furthering our understanding of autism. We do so by mapping visuo-tactile peri-personal space (PPS), and examining its electroencephalography (EEG) correlates, in individuals with ASD and neurotypical individuals during both a social and non-social context given that (i) the sensory coding of PPS is well understood, (ii) this space is thought to distinguish between self and other, and (iii) PPS is known to remap during social interactions. In contrast to their neurotypical counterparts, psychophysical and EEG evidence suggested that PPS does not remap in ASD during a social context. To account for this observation, we then employed a neural network model of PPS and demonstrate that PPS remapping may be driven by changes in neural gain operating at the level of multisensory neurons. Critically, under the anomalous excitation-inhibition (E/I) regime of ASD, this gain modulation does not result in PPS resizing. Overall, our findings are in line with recent statistical inference accounts suggesting diminished flexibility in ASD, and further these accounts by demonstrating within an example relevant for social cognition that such inflexibility may be due to E/I imbalances.

The encoding of the space close to the body, named peri-personal space (PPS), is thought to play a crucial role in the unusual experiences of the self observed in schizophrenia (SCZ). However, it is unclear why SCZ patients and high schizotypal (H-SPQ) individuals present a narrower PPS and why the boundaries of the PPS are more sharply defined in patients. We hypothesise that the unusual PPS representation observed in SCZ is caused by an imbalance of excitation and inhibition (E/I) in recurrent synapses of unisensory neurons or an impairment of bottom-up and top-down connectivity between unisensory and multisensory neurons. These hypotheses were tested computationally by manipulating the effects of E/I imbalance, feedback weights and synaptic density in the network. Using simulations we explored the effects of such impairments in the PPS representation generated by the network and fitted the model to behavioural data. We found that increased excitation of sensory neurons could account for the smaller PPS observed in SCZ and H-SPQ, whereas a decrease of synaptic density caused the sharp definition of the PPS observed in SCZ. We propose a novel conceptual model of PPS representation in the SCZ spectrum that can account for alterations in self-world demarcation, failures in tactile discrimination and symptoms observed in patients.

Deficits in statistical learning and predictive processing could in principle explain inattention and distractibility in attention deficit hyperactivity disorder (ADHD).To test this, we evaluated whether adults diagnosed with ADHD (n = 17) differed from controls (n = 30) in implicitly learning and using low-level perceptual priors to guide sensory processing. We used a visual statistical learning task in which participants had to estimate the direction of coherently moving dots. Unbeknown to the participants, two directions were more frequently presented than the others, creating an implicit bias (prior) towards those directions. This task had previously revealed differences in autistic spectrum disorder and schizophrenia.Both groups acquired the prior expectations for the most frequent directions and, except for some subtle differences over time, there were no group difference in how much the priors influenced performance. This suggests that ADHD symptoms do not stem from difficulties in developing and/or using perceptual priors.Statement of RelevanceSchizophrenia, autistic spectrum disorder (ASD), and more recently anxiety and depression have all been described in terms of disturbances in predictive coding or Bayesian inference – a general mechanism for how incoming sensory information is integrated with prior knowledge and how such prior knowledge is learnt. In theory, Attention deficit hyperactivity disorder (ADHD) is also associated with a range of sensory processing differences that could be understood within the Bayesian inference framework. However, to our knowledge, there has been no experimental work explicitly testing differences in Bayesian inference in ADHD so far.Here we found the acquisition and use of the low-level perceptual priors to be intact in ADHD, compared to controls.Our study shows for the first time that the learning and use of low-level Bayesian priors are not impaired in ADHD, contrary to what has been described in other neurodevelopmental disorders.

A recent article shows that the brain automatically estimates the probabilities of possible future actions before it has even received all the information necessary to decide what to do next.Keywords: Bayesian brain; predictive coding; neural encoding; neural decoding; motor cortex

Clinical studies have reported altered reward processing in major depressive disorder. Using dynamic causal modelling, Rupprecher et al. show that blunted reward-related effective connectivity from the medial prefrontal cortex to the striatum is associated with increased severity of depressive symptoms., Major depressive disorder is a leading cause of disability and significant mortality, yet mechanistic understanding remains limited. Over the past decade evidence has accumulated from case-control studies that depressive illness is associated with blunted reward activation in the basal ganglia and other regions such as the medial prefrontal cortex. However it is unclear whether this finding can be replicated in a large number of subjects. The functional anatomy of the medial prefrontal cortex and basal ganglia has been extensively studied and the former has excitatory glutamatergic projections to the latter. Reduced effect of glutamatergic projections from the prefrontal cortex to the nucleus accumbens has been argued to underlie motivational disorders such as depression, and many prominent theories of major depressive disorder propose a role for abnormal cortico-limbic connectivity. However, it is unclear whether there is abnormal reward-linked effective connectivity between the medial prefrontal cortex and basal ganglia related to depression. While resting state connectivity abnormalities have been frequently reported in depression, it has not been possible to directly link these findings to reward-learning studies. Here, we tested two main hypotheses. First, mood symptoms are associated with blunted striatal reward prediction error signals in a large community-based sample of recovered and currently ill patients, similar to reports from a number of studies. Second, event-related directed medial prefrontal cortex to basal ganglia effective connectivity is abnormally increased or decreased related to the severity of mood symptoms. Using a Research Domain Criteria approach, data were acquired from a large community-based sample of subjects who participated in a probabilistic reward learning task during event-related functional MRI. Computational modelling of behaviour, model-free and model-based functional MRI, and effective connectivity dynamic causal modelling analyses were used to test hypotheses. Increased depressive symptom severity was related to decreased reward signals in areas which included the nucleus accumbens in 475 participants. Decreased reward-related effective connectivity from the medial prefrontal cortex to striatum was associated with increased depressive symptom severity in 165 participants. Decreased striatal activity may have been due to decreased cortical to striatal connectivity consistent with glutamatergic and cortical-limbic related theories of depression and resulted in reduced direct pathway basal ganglia output. Further study of basal ganglia pathophysiology is required to better understand these abnormalities in patients with depressive symptoms and syndromes.

Prominent theories suggest that symptoms of schizophrenia stem from learning deficiencies resulting in distorted internal models of the world. To test these theories further, we used a visual statistical learning task known to induce rapid implicit learning of the stimulus statistics. In this task, participants are presented with a field of coherently moving dots and are asked to report the presented direction of the dots (estimation task), and whether they saw any dots or not (detection task). Two of the directions were more frequently presented than the others. In controls, the implicit acquisition of the stimuli statistics influences their perception in two ways: (i) motion directions are perceived as being more similar to the most frequently presented directions than they really are (estimation biases); and (ii) in the absence of stimuli, participants sometimes report perceiving the most frequently presented directions (a form of hallucinations). Such behaviour is consistent with probabilistic inference, i.e. combining learnt perceptual priors with sensory evidence. We investigated whether patients with chronic, stable, treated schizophrenia (n = 20) differ from controls (n = 23) in the acquisition of the perceptual priors and/or their influence on perception. We found that although patients were slower than controls, they showed comparable acquisition of perceptual priors, approximating the stimulus statistics. This suggests that patients have no statistical learning deficits in our task. This may reflect our patients’ relative wellbeing on antipsychotic medication. Intriguingly, however, patients experienced significantly fewer (P = 0.016) hallucinations of the most frequently presented directions than controls when the stimulus was absent or when it was very weak (prior-based lapse estimations). This suggests that prior expectations had less influence on patients’ perception than on controls when stimuli were absent or below perceptual threshold.

Depression is a debilitating condition with a high prevalence, but aetiology and pathophysiology are still unclear. Various reward-learning paradigms have been used to show impairments in depression. Both trait pessimism and neuroticism are associated with depression, but their link with the impairments in reward learning and decision-making have not been investigated. A Pavlovian conditioning task was performed by 32 subjects, 15 with depression. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants’ behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Neuroticism scores across participants were significantly correlated with participants’ inability to follow their internal value estimations. Our results demonstrate behavioural differences in probabilistic reward learning between depressed patients and healthy controls. Neuroticism was associated with the impaired ability to follow internal reward values and consequently with worse decision-making.

Task-switching is an important cognitive skill that facilitates our ability to choose appropriate behavior in a varied and changing environment. Task-switching training studies have sought to improve this ability by practicing switching between multiple tasks. However, an efficacious training paradigm has been difficult to develop in part due to findings that small differences in task parameters influence switching behavior in a non-trivial manner. Here, for the first time we employ the Drift Diffusion Model (DDM) to understand the influence of feedback on task-switching and investigate how drift diffusion parameters change over the course of task switch training. We trained 316 participants on a simple task where they alternated sorting stimuli by color or by shape. Feedback differed in six different ways between subjects groups, ranging from No Feedback (NFB) to a variety of manipulations addressing trial-wise vs. Block Feedback (BFB), rewards vs. punishments, payment bonuses and different payouts depending upon the trial type (switch/non-switch). While overall performance was found to be affected by feedback, no effect of feedback was found on task-switching learning. Drift Diffusion Modeling revealed that the reductions in reaction time (RT) switch cost over the course of training were driven by a continually decreasing decision boundary. Furthermore, feedback effects on RT switch cost were also driven by differences in decision boundary, but not in drift rate. These results reveal that participants systematically modified their task-switching performance without yielding an overall gain in performance.

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals’ likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due not to weaker prior expectations but to more precise sensory representations.

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behaviour (e.g. Bayesian models– top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist & neural networks – bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioural vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesises the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the Dopamine, Glutamate, GABA, Dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.

Perceptual learning (PL) has been traditionally thought of as highly specific to stimulus properties, task and retinotopic position. This view is being progressively challenged, with accumulating evidence that learning can generalize (transfer) across various parameters under certain conditions. For example, retinotopic specificity can be diminished when the proportion of easy to hard trials is high, such as when multiple short staircases, instead of a single long one, are used during training. To date, there is a paucity of mechanistic explanations of what conditions affect transfer of learning. Here we present a model based on the popular Integrated Reweighting Theory model of PL but departing from its one-layer architecture by including a novel key feature: dynamic weighting of retinotopic-location-specific vs location-independent representations based on internal performance estimates of these representations. This dynamic weighting is closely related to gating in a mixture-of-experts architecture. Our dynamic performance-monitoring model (DPMM) unifies a variety of psychophysical data on transfer of PL, such as the short-vs-long staircase effect, as well as several findings from the double-training literature. Furthermore, the DPMM makes testable predictions and ultimately helps understand the mechanisms of generalization of PL, with potential applications to vision rehabilitation and enhancement.Keywords: perceptual learning, computational, model, specificity, transfer, double training, mixture-of-experts

The perceived speed of moving objects has long been known to depend on image contrast. Lowering the contrast of first-order motion stimuli typically decreases perceived speed – the well-known “Thompson effect”. It has been suggested that contrast-dependent biases are the result of optimal inference by the visual system, whereby unreliable sensory information is combined with prior beliefs. The Thompson effect is thought to result from the prior belief that objects move slowly (in Bayesian terminology, a “slow speed prior”). However, there is some evidence that the Thompson effect is attenuated or even reversed at higher speeds. Does the effect of contrast on perceived speed depend on absolute speed and what does this imply for Bayesian models with a slow speed prior? We asked subjects to compare the speeds of simultaneously presented drifting gratings of different contrasts. At low contrasts (3–15%), we found that the Thompson effect was attenuated at high speeds: at 8 and 12deg/s, perceived speed increased less with contrast than at 1 and 4deg/s; however, at higher contrasts (15–95%), the situation was reversed. A semi-parametric Bayesian model was used to extract the subjects’ speed priors and was subsequently improved by combining it with a model of speed tuning. These novel findings regarding the dual, contrast-dependent effect of high speeds help reconcile existing conflicting literature and suggest that physiologically plausible mechanisms of representation of speed in the visual cortex may need to be incorporated into Bayesian models to account for certain subtleties of human speed perception.

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people’s performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ([1982]). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects’ perceptual performance and features of the underlying neural mechanisms.

Visual aftereffects have been found for a wide variety of stimuli, ranging from oriented lines to human faces, but previous results suggested that face aftereffects were qualitatively different from orientation (tilt) aftereffects. Using computational models, we predicted that these differences were due to the limited range of faces used in previous studies. Here we report psychophysical results verifying this prediction. We used the same paradigm to test tilt aftereffects (TAE) and face gender aftereffects (FAE) and found that they exhibited qualitatively similar aftereffect curves, when a sufficiently large range of test faces was used. Overall, the results suggest that similar adaptation mechanisms may underlie both high-level and low-level visual processing.

Grey matter brain networks are disrupted in schizophrenia, but it is still unclear at which point during the development of the illness these disruptions arise and whether these can be associated with behavioural predictors of schizophrenia. We investigated if single-subject grey matter networks were disrupted in a sample of people at familial risk of schizophrenia. Single-subject grey matter networks were extracted from structural MRI scans of 144 high risk subjects, 32 recent-onset patients and 36 healthy controls. The following network properties were calculated: size, connectivity density, degree, path length, clustering coefficient, betweenness centrality and small world properties. People at risk of schizophrenia showed decreased path length and clustering in mostly prefrontal and temporal areas. Within the high risk sample, the path length of the posterior cingulate cortex and the betweenness centrality of the left inferior frontal operculum explained 81% of the variance in schizotypal cognitions, which was previously shown to be the strongest behavioural predictor of schizophrenia in the study. In contrast, local grey matter volume measurements explained 48% of variance in schizotypy. The present results suggest that single-subject grey matter networks can quantify behaviourally relevant biological alterations in people at increased risk for schizophrenia before disease onset.

Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher information correctly predicts the best-encoded stimulus for large populations and where the stimuli are closely spaced in forced choice tasks. In smaller populations with high variability, or in forced choice tasks with widely-spaced choices, the best-encoded stimulus falls at the peak of unimodal tuning curves, but is more variable for monotonic tuning curves. Task, population size and variability all need to be considered when assessing which stimuli a neuron represents, but the best-encoded stimulus can be estimated on a case-by case basis using commonly available computing facilities.

Our perception of the world is strongly influenced by our expectations, and a question of key importance is how the visual system develops and updates its expectations through interaction with the environment. We used a visual search task to investigate how expectations of different timescales (from the last few trials to hours to long-term statistics of natural scenes) interact to alter perception. We presented human observers with low-contrast white dots at 12 possible locations equally spaced on a circle, and we asked them to simultaneously identify the presence and location of the dots while manipulating their expectations by presenting stimuli at some locations more frequently than others. Our findings suggest that there are strong acuity differences between absolute target locations (e.g., horizontal vs. vertical) and preexisting long-term biases influencing observers' detection and localization performance, respectively. On top of these, subjects quickly learned about the stimulus distribution, which improved their detection performance but caused increased false alarms at the most frequently presented stimulus locations. Recent exposure to a stimulus resulted in significantly improved detection performance and significantly more false alarms but only at locations at which it was more probable that a stimulus would be presented. Our results can be modeled and understood within a Bayesian framework in terms of a near-optimal integration of sensory evidence with rapidly learned statistical priors, which are skewed toward the very recent history of trials and may help understanding the time scale of developing expectations at the neural level.

Perceptual learning is classically thought to be highly specific to the trained stimuli's retinal locations. However, recent research using a novel double-training paradigm has found dramatic transfer of perceptual learning to untrained locations. These results challenged existing models of perceptual learning and provoked intense debate in the field. Recently, Hung and Seitz (2014) showed that previously reported results could be reconciled by considering the details of the training procedure, in particular, whether it involves prolonged training at threshold using a single staircase procedure or multiple staircases. Here, we examine a hierarchical neural network model of the visual pathway, built upon previously proposed integrated reweighting models of perceptual learning, to understand how retinotopic transfer depends on the training procedure adopted. We propose that the transfer and specificity of learning between retinal locations can be explained by considering the task-difficulty and confidence during training. In our model, difficult tasks lead to higher learning of weights from early visual cortex to the decision unit, and thus to specificity, while easy tasks lead to higher learning of weights from later stages of the visual hierarchy and thus to more transfer. To model interindividual difference in task-difficulty, we relate task-difficulty to the confidence of subjects. We show that our confidence-based reweighting model can account for the results of Hung and Seitz (2014) and makes testable predictions.

Line-ends, corners and junctions are important singularities for form analysis, object recognition, depth ordering or motion processing. In this study, we investigate the extent to which processing the motion of line ends depends on the spatial configuration of their immediate surround. To that aim, we used two vertical collinear line segments, translating clockwise or anti-clockwise along a circular path, together with a direction discrimination task. Direction discrimination was measured independently for outer lineends––at both segments extremities––and inner line-ends––in between collinear segments––using line segments partially occluded by invisible masks such that the direction of either inner or outer line-ends motion was restricted to a sinusoidal translation along a horizontal axis, and thus irrelevant for the motion task. Under these conditions, access to the direction of inner line-ends is longer and more difficult than it is for outer line-ends. Subsequent experiments show that these effects depend on the degree of collinearity between line segments. Similar experiments were performed after volunteers took a dose of Lorazepam, a benzodiazepine that facilitates the fixation of GABA on GABAA receptors. The results show that the differences between the processing of inner and outer line-ends is reduced, suggesting that the effect of the surround is modulated by inhibitory mechanisms. Using a simple model, we propose that this effect can be explained by a competition between a segmentation process based on surround suppression and contour integration through long-range horizontal connections, at or prior to motion processing stages. � 2004 Elsevier Ltd. All rights reserved.

We report several experiments showing that a Gabor patch moving in apparent motion sequences appears much faster when its orientation is aligned with the motion path than when it is at an angle to it. This effect is very large and peaks at high speeds (64 degrees /s), decreases for higher and lower speeds and disappears at low speeds (4 degrees /s). This speed bias decreases as the angle between the motion axis and the orientation of the Gabor patch increases, but remains high for curvilinear paths, provided that element orientation is kept tangential to the motion trajectory. It is not accounted for by decision strategies relying on the overall length and duration of the motion sequence or the gap size (or spatial jump) between successive frames. We propose a simple explanation, thoroughly developed as a computational model in a companion paper (Seriès, Georges, LorenceauFrégnac: "Orientation dependent modulation of apparent speed: a model based on the dynamics of feedforward and horizontal connectivity in V1 cortex", this issue), according to which long-range horizontal connections in V1 elicit differential latency modulations in response to apparent motion sequences, whose read-out at an MT stage results in a perceptual speed bias. The consequences of these findings are discussed.

Psychophysical and physiological studies suggest that long-range horizontal connections in primary visual cortex participate in spatial integration and contour processing. Until recently, little attention has been paid to their intrinsic temporal properties. Recent physiological studies indicate, however, that the propagation of activity through long-range horizontal connections is slow, with time scales comparable to the perceptual scales involved in motion processing. Using a simple model of V1 connectivity, we explore some of the implications of this slow dynamics. The model predicts that V1 responses to a stimulus in the receptive field can be modulated by a previous stimulation, a few milliseconds to a few tens of milliseconds before, in the surround. We analyze this phenomenon and its possible consequences on speed perception, as a function of the spatio-temporal configuration of the visual inputs (relative orientation, spatial separation, temporal interval between the elements, sequence speed). We show that the dynamical interactions between feed-forward and horizontal signals in V1 can explain why the perceived speed of fast apparent motion sequences strongly depends on the orientation of their elements relative to the motion axis and can account for the range of speed for which this perceptual effect occurs (Georges, Seriès, Frégnac and Lorenceau, this issue).

Although much work has recently been directed at understanding social decision-making, relatively little is known about how different types of feedback impact adaptive changes in social behavior. To address this issue quantitatively, we designed a novel associative learning task called the “Tipping Game,” in which participants had to learn a social norm of tipping in restaurants. Participants were found to make more generous decisions from reward feedback in the form of facial expressions, in comparison to reward feedback in the form of symbols such as ticks and crosses. Furthermore, more participants displayed learning in the condition where they received social reward feedback than participants in the non-social condition. Modeling results showed that the pattern of performance displayed by participants receiving social reward feedback could be explained by a lower sensitivity to economic costs.