Disentangling signal and noise in autism spectrum disorder

Evaluating the validity of eye-tracking tasks and stimuli in detecting high-risk infants later diagnosed with autism: A meta-analysis

Gaze abnormalities are well documented in infants at elevated risk for autism spectrum disorder (ASD). However, variations in experimental design and stimuli across studies have led to mixed results. The current meta-analysis aimed to identify which type of eye tracking task and stimulus are most effective at differentiating high-risk infants (siblings of children with ASD) who later meet diagnosis criteria from low-risk infants without familial autism. We synthesized 35 studies that used eye tracking to investigate gaze behavior in infants at high genetic risk for autism before 2 years of age. We found that stimulus features, regions of interest (ROIs) and study quality moderated effect sizes across studies. Overall, dynamic stimuli and socially-relevant regions in the social stimuli (i.e. the target and activity of characters' shared focus) reliably detected high-risk infants who later develop ASD. Attention disengagement task and stimuli depicting interactions between human and nonhuman characters could identify high-risk infants who later develop ASD and those who have autism-related symptoms but do not meet the diagnostic criteria as well. These findings provide sensitive and reliable early markers of ASD, which is helpful to develop objective and quantitative early autism screening and intervention tools.

Visual processing and decision-making in autism and dyslexia: Insights from cross-syndrome approaches

Atypical visual processing has been reported in developmental conditions like autism and dyslexia, and some accounts propose a causal role for visual processing in the development of these conditions. However, few studies make direct comparisons between conditions, or use sufficiently sensitive methods, meaning that it is hard to say whether atypical visual processing tells us anything specific about these conditions, or whether it reflects a more general marker of atypical development. Here I review findings from two computational modelling approaches (equivalent noise and diffusion modelling) and related electroencephalography (EEG) indices which we have applied to data from autistic, dyslexic and typically developing children to reveal how the component processes involved in visual processing and decision-making are altered in autism and dyslexia. The results identify both areas of convergence and divergence in autistic and dyslexic children's visual processing and decision-making, with implications for influential theoretical accounts such as weak central coherence, increased internal noise, and dorsal-stream vulnerability. In both sets of studies, we also see considerable variability across children in all three groups. To better understand this variability, and further understand the convergence and divergence identified between conditions, future studies would benefit from studying how the component processes reviewed here relate to transdiagnostic dimensions, which will also give insights into individual differences in visual processing and decision-making more generally.

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.

Auditory Challenges and Listening Effort in School-Age Children With Autism: Insights From Pupillary Dynamics During Speech-in-Noise Perception

PURPOSE

This study aimed to investigate challenges in speech-in-noise (SiN) processing faced by school-age children with autism spectrum conditions (ASCs) and their impact on listening effort.

METHOD

Participants, including 23 Mandarin-speaking children with ASCs and 19 age-matched neurotypical (NT) peers, underwent sentence recognition tests in both quiet and noisy conditions, with a speech-shaped steady-state noise masker presented at 0-dB signal-to-noise ratio in the noisy condition. Recognition accuracy rates and task-evoked pupil responses were compared to assess behavioral performance and listening effort during auditory tasks.

RESULTS

No main effect of group was found on accuracy rates. Instead, significant effects emerged for autistic trait scores, listening conditions, and their interaction, indicating that higher trait scores were associated with poorer performance in noise. Pupillometric data revealed significantly larger and earlier peak dilations, along with more varied pupillary dynamics in the ASC group relative to the NT group, especially under noisy conditions. Importantly, the ASC group's peak dilation in quiet mirrored that of the NT group in noise. However, the ASC group consistently exhibited reduced mean dilations than the NT group.

CONCLUSIONS

Pupillary responses suggest a different resource allocation pattern in ASCs: An initial sharper and larger dilation may signal an intense, narrowed resource allocation, likely linked to heightened arousal, engagement, and cognitive load, whereas a subsequent faster tail-off may indicate a greater decrease in resource availability and engagement, or a quicker release of arousal and cognitive load. The presence of noise further accentuates this pattern. This highlights the unique SiN processing challenges children with ASCs may face, underscoring the importance of a nuanced, individual-centric approach for interventions and support.

Atypical and variable attention patterns reveal reduced contextual priors in children with autism spectrum disorder

Accumulating evidence suggests that individuals with autism spectrum disorder (ASD) show impairments in using contextual priors to predict others' actions and make intention inference. Yet less is known about whether and how children with ASD acquire contextual priors during action observation and how contextual priors relate to their action prediction and intention inference. To form proper contextual priors, individuals need to observe the social scenes in a reliable manner and focus on socially relevant information. By employing a data-driven scan path method and areas of interest (AOI)-based analysis, the current study investigated how contextual priors would relate to action prediction and intention understanding in 4-to-9-year-old children with ASD (N = 56) and typically developing (TD) children (N = 50) during free viewing of dynamic social scenes with different intentions. Results showed that children with ASD exhibited higher intra-subject variability when scanning social scenes and reduced attention to socially relevant areas. Moreover, children with high-level action prediction and intention understanding showed lower intra-subject variability and increased attention to socially relevant areas. These findings suggest that altered fixation patterns might restrain children with ASD from acquiring proper contextual priors, which has cascading downstream effects on their action prediction and intention understanding.

Processing of visual social-communication cues during a social-perception of action task in autistic and non-autistic observers

Social perception and communication differ between those with and without autism, even when verbal fluency and intellectual ability are equated. Previous work found that observers responded more quickly to an actor's points if the actor had chosen by themselves where to point instead of being directed where to point. Notably, this 'choice-advantage' effect decreased across non-autistic participants as the number of autistic-like traits and tendencies increased (Pesquita et al., 2016). Here, we build on that work using the same task to study individuals over a broader range of the spectrum, from autistic to non-autistic, measuring both response initiation and mouse movement times, and considering the response to each actor separately. Autistic and non-autistic observers viewed videos of three different actors pointing to one of two locations, without knowing that the actors were sometimes freely choosing to point to one target and other times being directed where to point. All observers exhibited a choice-advantage overall, meaning they responded more rapidly when actors were freely choosing versus when they were directed, indicating a sensitivity to the actors' postural cues and movements. Our fine-grained analyses found a more robust choice-advantage to some actors than others, with autistic observers showing a choice-advantage only in response to one of the actors, suggesting that both actor and observer characteristics influence the overall effect. We briefly explore existing actor characteristics that may have contributed to this effect, finding that both duration of exposure to pre-movement cues and kinematic cues of the actors likely influence the choice advantage to different degrees across the groups. Altogether, the evidence suggested that both autistic and non-autistic individuals could detect the choice-advantage signal, but that for autistic observers the choice-advantage was actor specific. Notably, we found that the influence of the signal, when present, was detected early for all actors by the non-autistic observers, but detected later and only for one actor by the autistic observers. Altogether, we have more accurately characterized the ability of social-perception in autistic individuals as intact, but highlighted that detection of signal is likely delayed/distributed compared to non-autistic observers and that it is important to investigate actor characteristics that may influence detection and use of their social-perception signals.

The world is nuanced but pixelated: Autistic individuals' perspective on HIPPEA

LAY ABSTRACT

Autism is a condition comprised of difficulties in social and communication contexts, sensory sensitivities as well as restrictive and repetitive behaviours. Many theories have tried to explain all the symptoms and behaviours associated with autism. We focus on one recent theory - High, Inflexible Precision of Prediction Errors in Autism (HIPPEA). We aim to understand how much this theory fits the experiences of autistic individuals. We collected data through 21 online questionnaires and 8 follow-up interviews. One of our participants was a parent of an autistic child, and the remaining were adults who reported a diagnosis of autism. We analysed the data by thinking about how it fitted with what we already knew and by looking for new insights which came up. Our results suggest that autistic individuals can make generalisations but that this happens more slowly across both social and non-social areas. These generalisations are very reliant on detail - in computer terms, they are 'pixelated'. This is in line with what HIPPEA suggests. We also showed that autistic individuals can be motivated to explore and engage socially, something that needs more consideration within HIPPEA. Overall, this study shows that HIPPEA can explain many autistic experiences, but that further refinement is needed.

Cognitive biases as an adaptive strategy in autism and schizophrenia spectrum: the compensation perspective on neurodiversity

This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.

A theory of autism bridging across levels of description

Autism impacts a wide range of behaviors and neural functions. As such, theories of autism spectrum disorder (ASD) are numerous and span different levels of description, from neurocognitive to molecular. We propose how existent behavioral, computational, algorithmic, and neural accounts of ASD may relate to one another. Specifically, we argue that ASD may be cast as a disorder of causal inference (computational level). This computation relies on marginalization, which is thought to be subserved by divisive normalization (algorithmic level). In turn, divisive normalization may be impaired by excitatory-to-inhibitory imbalances (neural implementation level). We also discuss ASD within similar frameworks, those of predictive coding and circular inference. Together, we hope to motivate work unifying the different accounts of ASD.

Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

Introduction

Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning.

Methods

Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility.

Results

Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli.

Discussion

These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.

Increased functional activity, bottom-up and intrinsic effective connectivity in autism

Sensory perceptual alterations such as sensory sensitivities in autism have been proposed to be caused by differences in sensory observation (Likelihood) or in forming models of the environment (Prior), which result in an increase in bottom-up information flow relative to top-down control. To investigate this conjecture, we had autistic individuals (AS) and neurotypicals (NT) perform a decision-under-uncertainty paradigm while undergoing functional magnetic resonance imaging (fMRI). There were no group differences in task performance and in Prior and Likelihood representations in brain activity. However, there were significant group differences in overall task activity, with the AS group showing significantly greater activation in the bilateral precuneus, mid-occipital gyrus, cuneus, superior frontal gyrus (SFG) and left putamen relative to the NT group. Further, when pooling the data across both groups, we found that those with higher AQ scores showed greater activity in the left cuneus and precuneus. Effective connectivity analysis using dynamic causal modelling (DCM) revealed that group differences in BOLD signals were underpinned by increased activity within sensory regions and a net increase in bottom-up connectivity from the occipital region to the precuneus and the left SFG. These findings support the hypothesis of increased bottom-up information flow in autism during sensory learning tasks.

Perceptual decision-making in autism as assessed by “spot the difference” visual cognition tasks

Discriminating between similar figures proves to be a remarkably demanding task due to the limited capacity of our visual cognitive processes. Here we examine how perceptual inference and decision-making are modulated by differences arising from neurodiversity. A large sample of autistic (n = 140) and typical (n = 147) participants completed two forced choice similarity judgement tasks online. Each task consisted of “match” (identical figures) and “mismatch” (subtle differences between figures) conditions. Signal detection theory analyses indicated a response bias by the autism group during conditions of uncertainty. More specifically, autistic participants were more likely to choose the “mismatch” option, thus leading to more hits on the “mismatch” condition, but also more false alarms on the “match” condition. These results suggest differences in response strategies during perceptual decision-making in autism.

Autistic-Like Traits and Positive Schizotypy as Diametric Specializations of the Predictive Mind

According to the predictive-processing framework, only prediction errors (rather than all sensory inputs) are processed by an organism's perceptual system. Prediction errors can be weighted such that errors from more reliable sources will be more influential in updating prior beliefs. It has previously been argued that autism-spectrum conditions can be understood as resulting from a predictive-processing mechanism in which an inflexibly high weight is given to sensory-prediction errors that results in overfitting their predictive models to the world. Deficits in executive functioning, theory of mind, and central coherence are all argued to flow naturally from this core underlying mechanism. The diametric model of autism and psychosis suggests a simple extension of this hypothesis. If people on the autism spectrum give an inflexibly high weight to sensory input, could it be that people with a predisposition to psychosis (i.e., people high in positive schizotypy) give an inflexibly low weight to sensory input? In this article I argue that evidence strongly supports this hypothesis. An inflexibly low weight given to sensory input can explain such disparate features of positive schizotypy as increased exploratory behavior, apophenia, hyper theory of mind, hyperactive imagination, attentional differences, and having idiosyncratic worldviews.

Investigating Motor Preparation in Autism Spectrum Disorder With and Without Attention Deficit/Hyperactivity Disorder

This study investigated motor preparation and action-consequence prediction using the lateralized readiness potential (LRP). Motor impairments are common in autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. Alterations in predictive processes may impact motor planning. Whether motor planning deficits are characteristic of ASD broadly or magnified in the context of co-morbid ADHD is unclear. ASD children with (ASD + ADHD; n = 12) and without (ASD - ADHD; n = 9) comorbid ADHD and typical controls (n = 29) performed voluntary motor actions that either did or did not result in auditory consequences. ASD - ADHD children demonstrated LRP enhancement when their action produced an effect while ASD + ADHD children had attenuated responses regardless of action-effect pairings. Findings suggest influence of ADHD comorbidity on motor preparation and prediction in ASD.

Reduced Primacy Bias in Autism during Early Sensory Processing

Recent theories of autism propose that a core deficit in autism would be a less context-sensitive weighting of prediction errors. There is also first support for this hypothesis on an early sensory level. However, an open question is whether this decreased context sensitivity is caused by faster updating of one's model of the world (i.e., higher weighting of new information), proposed by predictive coding theories, or slower model updating. Here, we differentiated between these two hypotheses by investigating how first impressions shape the mismatch negativity (MMN), reflecting early sensory prediction error processing. An autism and matched control group of human adults (both n = 27, 8 female) were compared on the multi-timescale MMN paradigm, in which tones were presented that were either standard (frequently occurring) or deviant (rare), and these roles reversed every block. A well-replicated observation is that the initial model (i.e., the standard and deviant sound in the first block) influences MMN amplitudes in later blocks. If autism is characterized by faster model updating, and thus a smaller primacy bias, we hypothesized (and demonstrate using a simple reinforcement learning model) that their MMN amplitudes should be less influenced by the initial context. In line with this hypothesis, we found that MMN responses in the autism group did not differ between the initial deviant and initial standard sounds as they did in the control group. These findings are consistent with the idea that autism is characterized by faster model updating during early sensory processing, as proposed by predictive coding accounts of autism.SIGNIFICANCE STATEMENT Recent theories of autism propose that a core deficit in autism is that they are faster to update their models of the world based on new sensory information. Here, we tested this hypothesis by investigating how first impressions shape brain responses during early sensory processing, and hypothesized that individuals with autism would be less influenced by these first impressions. In line with earlier studies, our results show that early sensory processing was influenced by first impressions in a control group. However, this was not the case in an autism group. This suggests that individuals with autism are faster to abandon their initial model, and is consistent with the proposal that they are faster to update their models of the world.

Differences in functional brain organization during gesture recognition between autistic and neurotypical individuals

Persons with and without autism process sensory information differently. Differences in sensory processing are directly relevant to social functioning and communicative abilities, which are known to be hampered in persons with autism. We collected functional magnetic resonance imaging data from 25 autistic individuals and 25 neurotypical individuals while they performed a silent gesture recognition task. We exploited brain network topology, a holistic quantification of how networks within the brain are organized to provide new insights into how visual communicative signals are processed in autistic and neurotypical individuals. Performing graph theoretical analysis, we calculated two network properties of the action observation network: 'local efficiency', as a measure of network segregation, and 'global efficiency', as a measure of network integration. We found that persons with autism and neurotypical persons differ in how the action observation network is organized. Persons with autism utilize a more clustered, local-processing-oriented network configuration (i.e. higher local efficiency) rather than the more integrative network organization seen in neurotypicals (i.e. higher global efficiency). These results shed new light on the complex interplay between social and sensory processing in autism.

Behavioural and neural indices of perceptual decision-making in autistic children during visual motion tasks

Many studies report atypical responses to sensory information in autistic individuals, yet it is not clear which stages of processing are affected, with little consideration given to decision-making processes. We combined diffusion modelling with high-density EEG to identify which processing stages differ between 50 autistic and 50 typically developing children aged 6-14 years during two visual motion tasks. Our pre-registered hypotheses were that autistic children would show task-dependent differences in sensory evidence accumulation, alongside a more cautious decision-making style and longer non-decision time across tasks. We tested these hypotheses using hierarchical Bayesian diffusion models with a rigorous blind modelling approach, finding no conclusive evidence for our hypotheses. Using a data-driven method, we identified a response-locked centro-parietal component previously linked to the decision-making process. The build-up in this component did not consistently relate to evidence accumulation in autistic children. This suggests that the relationship between the EEG measure and diffusion-modelling is not straightforward in autistic children. Compared to a related study of children with dyslexia, motion processing differences appear less pronounced in autistic children. Exploratory analyses also suggest weak evidence that ADHD symptoms moderate perceptual decision-making in autistic children.

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

Special treatment of prediction errors in autism spectrum disorder

For autistic individuals, sensory stimulation can be experienced as overwhelming. Models of predictive coding postulate that cortical mechanisms disamplify predictable information and amplify prediction errors that surpass a defined precision level. In autism, the neuronal processing is putting an inflexibly high precision on prediction errors according to the HIPPEA theory (High, Inflexible Precision of Prediction Errors in Autism). We used an apparent motion paradigm to test this prediction. In apparent motion paradigms, the illusory motion of an object creates a prediction about where and when an internally generated token would be moving along the apparent motion trace. This illusion facilitates the perception of a flashing stimulus (target) appearing in-time with the apparent motion token and is perceived as a predictable event (predictable target). In contrast, a flashing stimulus appearing out-of-time with the apparent motion illusion is an unpredictable target that is less often detected even though it produces a prediction error signal. If a prediction error does not surpass a given precision threshold the stimulation event is discounted and therefore less often detected than predictable tokens. In autism, the precision threshold is lower and the same prediction errors (unpredictable target) triggers a detection similar to that of a predictable flash stimulus. To test this hypothesis, we recruited 11 autistic males and 9 neurotypical matched controls. The participants were tasked to detect flashing stimuli placed on an apparent motion trace either in-time or out-of-time with the apparent motion illusion. Descriptively, 66% (6/9) of neurotypical and 64% (7/11) of autistic participants were better at detecting predictable targets. The prediction established by illusory motion appears to assist autistic and neurotypical individuals equally in the detection of predictable over unpredictable targets. Importantly, 55% (6/11) of autistic participants had faster responses for unpredictable targets, whereas only 22% (2/9) of neurotypicals had faster responses to unpredictable compared to predictable targets. Hence, these tentative results suggest that for autistic participants, unpredictable targets produce an above threshold prediction error, which leads to faster response. This difference in unpredictable target detection can be encapsulated under the HIPPEA theory, suggesting that precision setting could be aberrant in autistic individuals with respect to prediction errors. These tentative results should be considered in light of the small sample. For this reason, we provide the full set of materials necessary to replicate and extend the results.

Intact predictive motor sequence learning in autism spectrum disorder

Atypical motor learning has been suggested to underpin the development of motoric challenges (e.g., handwriting difficulties) in autism. Bayesian accounts of autistic cognition propose a mechanistic explanation for differences in the learning process in autism. Specifically, that autistic individuals overweight incoming, at the expense of prior, information and are thus less likely to (a) build stable expectations of upcoming events and (b) react to statistically surprising events. Although Bayesian accounts have been suggested to explain differences in learning across a range of domains, to date, such accounts have not been extended to motor learning. 28 autistic and 35 non-autistic controls (IQ > 70) completed a computerised task in which they learned sequences of actions. On occasional "surprising" trials, an expected action had to be replaced with an unexpected action. Sequence learning was indexed as the reaction time difference between blocks which featured a predictable sequence and those that did not. Surprise-related slowing was indexed as the reaction time difference between surprising and unsurprising trials. No differences in sequence-learning or surprise-related slowing were observed between the groups. Bayesian statistics provided anecdotal to moderate evidence to support the conclusion that sequence learning and surprise-related slowing were comparable between the two groups. We conclude that individuals with autism do not show atypicalities in response to surprising events in the context of motor sequence-learning. These data demand careful consideration of the way in which Bayesian accounts of autism can (and cannot) be extended to the domain of motor learning.

Brief Report: Feasibility of the Probabilistic Reversal Learning Task as an Outcome Measure in an Intervention Trial for Individuals with Autism Spectrum Disorder

Cognitive flexibility deficits are a hallmark feature of autism spectrum disorder (ASD), but few evidence-based behavioral interventions have successfully addressed this treatment target. Outcome measurement selection may help account for previous findings. The probabilistic reversal learning task (PRL) is a measure of cognitive flexibility previously validated for use in ASD, but its use as an outcome measure has not yet been assessed. The current study examined the feasibility, reproducibility, and sensitivity of PRL in a within-subjects trial of Regulating Together, a group-based intervention targeting emotion regulation. We demonstrated the PRL is highly feasible, showed test–retest reproducibility, and is sensitive to detect change following the intervention. Our findings demonstrate the PRL task may be a useful outcome measure of cognitive flexibility in future intervention trials in ASD.

Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

Global motion evoked potentials in autistic and dyslexic children: A cross-syndrome approach

Atypicalities in psychophysical thresholds for global motion processing have been reported in many neurodevelopmental conditions, including autism and dyslexia. Cross-syndrome comparisons of neural dynamics may help determine whether altered motion processing is a general marker of atypical development or condition-specific. Here, we assessed group differences in N2 peak amplitude (previously proposed as a marker of motion-specific processing) in typically developing (n = 57), autistic (n = 29) and dyslexic children (n = 44) aged 6-14 years, in two global motion tasks. High-density EEG data were collected while children judged the direction of global motion stimuli as quickly and accurately as possible, following a period of random motion. Using a data-driven component decomposition technique, we identified a reliable component that was maximal over occipital electrodes and had an N2-like peak at ~160 msec. We found no group differences in N2 peak amplitude, in either task. However, for both autistic and dyslexic children, there was evidence of atypicalities in later stages of processing that require follow up in future research. Our results suggest that early sensory encoding of motion information is unimpaired in dyslexic and autistic children. Group differences in later processing stages could reflect sustained global motion responses, decision-making, metacognitive processes and/or response generation, which may also distinguish between autistic and dyslexic individuals.

Implicit learning in 3-year-olds with high and low likelihood of autism shows no evidence of precision weighting differences

Predictive Processing accounts of autism claim that autistic individuals assign higher precision to their prediction errors than non-autistic individuals, that is, autistic individuals update their predictions more readily when faced with unexpected sensory input. Since setting the level of precision is a fundamental part of perception and learning, we propose that such differences should be detectable in various domains at a very early age, before clinical symptoms have fully emerged. We therefore tested 3-year-old younger siblings of autistic children, with a high likelihood of later receiving an autism diagnosis themselves, and low-likelihood children with an older sibling without autism. We used a novel implicit learning paradigm to examine the effect of sensory noise on the predictions participants built. In order to learn a sequence, our participants had to select which visual information to attend to and disregard low-level prediction errors caused by the sensory noise, which the theory claims is more difficult for autistic individuals. Contrary to the proposed higher precision-weighting of prediction errors in autism, the high-likelihood children did not show signs of updating their predictions more readily when we added sensory noise compared to the low-likelihood children, either in their reaction times or in the recurrence and determinism of their response locations. These results raise challenges for Predictive Processing theories of autism, specifically for the notion that prediction errors are inflexibly highly weighted by individuals with autism.

Meta-analytic evidence of differential prefrontal and early sensory cortex activity during non-social sensory perception in autism

To date, neuroimaging research has had a limited focus on non-social features of autism. As a result, neurobiological explanations for atypical sensory perception in autism are lacking. To address this, we quantitively condensed findings from the non-social autism fMRI literature in line with the current best practices for neuroimaging meta-analyses. Using activation likelihood estimation (ALE), we conducted a series of robust meta-analyses across 83 experiments from 52 fMRI studies investigating differences between autistic (n = 891) and typical (n = 967) participants. We found that typical controls, compared to autistic people, show greater activity in the prefrontal cortex (BA9, BA10) during perception tasks. More refined analyses revealed that, when compared to typical controls, autistic people show greater recruitment of the extrastriate V2 cortex (BA18) during visual processing. Taken together, these findings contribute to our understanding of current theories of autistic perception, and highlight some of the challenges of cognitive neuroscience research in autism.

Structural and contextual priors affect visual search in children with and without autism

Bayesian predictive coding theories of autism spectrum disorder propose that impaired acquisition or a broader shape of prior probability distributions lies at the core of the condition. However, we still know very little about how probability distributions are learned and encoded by children, let alone children with autism. Here, we take advantage of a recently developed distribution learning paradigm to characterize how children with and without autism acquire information about probability distributions. Twenty-four autistic and 25-matched neurotypical children searched for an odd-one-out target among a set of distractor lines with orientations sampled from a Gaussian distribution repeated across multiple trials to allow for learning of the parameters (mean and variance) of the distribution. We could measure the width (variance) of the participant's encoded distribution by introducing a target-distractor role-reversal while varying the similarity between target and previous distractor mean. Both groups performed similarly on the visual search task and learned the distractor distribution to a similar extent. However, the variance learned was much broader than the one presented, consistent with less informative priors in children irrespective of autism diagnosis. These findings have important implications for Bayesian accounts of perception throughout development, and Bayesian accounts of autism specifically. LAY SUMMARY: Recent theories about the underlying cognitive mechanisms of autism propose that the way autistic individuals estimate variability or uncertainty in their perceptual environment may differ from how typical individuals do so. Children had to search an oddly tilted line in a set of lines pointing in different directions, and based on their response times we examined how they learned about the variability in a set of objects. We found that autistic children learn variability as well as typical children, but both groups learn with less precision than typical adults do on the same task.

Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence

According to a recent influential proposal, several phenotypic features of autism spectrum disorder (ASD) may be accounted for by differences in predictive skills between individuals with ASD and neurotypical individuals. In this systematic review, we describe results from 47 studies that have empirically tested this hypothesis. We assess the results based on two observable aspects of prediction: learning a pairing between an antecedent and a consequence and responding to an antecedent in a predictive manner. Taken together, these studies suggest distinct differences in both predictive learning and predictive response. Studies documenting differences in learning predictive pairings indicate challenges in detecting such relationships especially when predictive features of an antecedent have low salience or consistency, and studies showing differences in habituation and perceptual adaptation suggest low-level predictive processing differences in ASD. These challenges may account for the observed differences in the influence of predictive priors, in spontaneous predictive movement or gaze, and in social prediction. An important goal for future research will be to better define and constrain the broad domain-general hypothesis by testing multiple types of prediction within the same individuals. Additional promising avenues include studying prediction within naturalistic contexts and assessing the effect of prediction-based intervention on supporting functional outcomes for individuals with ASD. LAY SUMMARY: Researchers have suggested that many features of autism spectrum disorder (ASD) may be explained by differences in the prediction skills of people with ASD. We review results from 47 studies. These studies suggest that ASD may be associated with differences in the learning of predictive pairings (e.g., learning cause and effect) and in low-level predictive processing in the brain (e.g., processing repeated sounds). These findings lay the groundwork for research that can improve our understanding of ASD and inform interventions. Autism Res 2021, 14: 604-630. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

Low-level, prediction-based sensory and motor processes are unimpaired in Autism

A new promising account of human brain function suggests that sensory cortices try to optimise information processing via predictions that are based on prior experiences. The brain is thus likened to a probabilistic prediction machine. There has been a growing - though inconsistent - literature to suggest that features of autism spectrum conditions (ASCs) are associated with a deficit in modelling the world through such prediction-based inference. However empirical evidence for differences in low-level sensorimotor predictions in autism is still lacking. One approach to examining predictive processing in the sensorimotor domain is in the context of self-generated (predictable) as opposed to externally-generated (less predictable) effects. We employed two complementary tasks - forcematching and intentional binding - which examine self-versus externally-generated action effects in terms of sensory attenuation and intentional binding respectively in adults with and without autism. The results show that autism was associated with normal levels of sensory attenuation of internally-generated force and with unaltered temporal attraction of voluntary actions and their outcomes. Thus, our results do not support a general deficit in predictive processing in autism.

Does Psychedelic Therapy Have a Transdiagnostic Action and Prophylactic Potential?

Addressing global mental health is a major 21st-century challenge. Current treatments have recognized limitations; in this context, new ones that are prophylactic and effective across diagnostic boundaries would represent a major advance. The view that there exists a core of transdiagnostic overlap between psychiatric disorders has re-emerged in recent years, and evidence that psychedelic therapy holds promise for a range of psychiatric disorders supports the position that it may be transdiagnostically effective. Here, we propose that psychedelic therapy's core, transdiagnostically relevant action lies in its ability to increase neuronal and mental plasticity, thus enhancing the potential for change, which we consider to be a key to its therapeutic benefits. Moreover, we suggest that enhanced plasticity via psychedelics, combined with a psychotherapeutic approach, can aid healthy adaptability and resilience, which are protective factors for long-term well-being. We present candidate neurological and psychological markers of this plasticity and link them with a predictive processing model of the action of psychedelics. We propose that a model of psychedelic-induced plasticity combined with an adequate therapeutic context has prophylactic and transdiagnostic potential, implying that it could have a broad, positive impact on public health.

Increased variability but intact integration during visual navigation in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here, we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects' dynamic belief states. Both groups showed a previously documented bias pattern by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a nonuniform speed prior rather than imperfect integration. We tracked participants' beliefs and found no difference in the speed prior, but there was heightened variability in the ASD group. Both end point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced, and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.

The Relation Between Preference for Predictability and Autistic Traits

A common idea about individuals with autism spectrum disorder (ASD) is that they have an above-average preference for predictability and sameness. However, surprisingly little research has gone toward this core symptom, and some studies suggest the preference for predictability in ASD might be less general than commonly assumed. Here, we investigated this important symptom of ASD using three different paradigms, which allowed us to measure preference for predictability under well-controlled experimental conditions. Specifically, we used a dimensional approach by investigating correlations between autistic traits (as measured with the Autism-Spectrum Quotient and Social Responsiveness Scale in a neurotypical population) and the scores on three different tasks. The "music preference" task assessed preferences for tone sequences that varied in predictability. The "perceptual fluency" task required participants to evaluate stimuli that were preceded by a similar versus dissimilar subliminally presented prime. The "gambling" task presented four decks of cards that had equal outcome probabilities but varied in predictability. We observed positive correlations between autistic traits and a preference for predictability in both the music preference and perceptual fluency task. We did not find our hypothesized correlation with gambling behavior but did observe a post hoc correlation showing that participants with more autistic traits were faster to choose the predictable deck. Together, these findings show that a relation between autistic traits and preference for predictability can be observed in a standardized lab environment, and should be considered an important first step toward a better, more mechanistic understanding of insistence on sameness in ASD. Autism Res 2020, 13: 1144-1154. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: A core symptom of autism spectrum disorder (ASD) is a strong preference for predictability, but little research has gone toward it. We show that neurotypical adults with more autistic traits have stronger preferences for predictable tunes, evaluate images that can be predicted as more beautiful, and are faster in choosing a gambling option resulting in predictable reward. These results offer the first important evidence that insistence on sameness in ASD can be studied in controlled lab settings.

Global Motion Perception in Autism Spectrum Disorder: A Meta-Analysis

Visual perception in individuals with autism spectrum disorder (ASD) is often debated in terms of enhanced local and impaired global perception. Deficits in global motion perception seem to support this characterization, although the evidence is inconsistent. We conducted a large meta-analysis on global motion, combining 48 articles on biological and coherent motion. Results provide evidence for a small global motion processing deficit in individuals with ASD compared to controls in both biological and coherent motion. This deficit appears to be present independent of the paradigm, task, dependent variable, age or IQ of the groups. Results indicate that individuals with ASD are less sensitive to these types of global motion, although the difference in neural mechanisms underlying this behavioral difference remains unclear.

REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics

This paper formulates the action of psychedelics by integrating the free-energy principle and entropic brain hypothesis. We call this formulation relaxed beliefs under psychedelics (REBUS) and the anarchic brain, founded on the principle that-via their entropic effect on spontaneous cortical activity-psychedelics work to relax the precision of high-level priors or beliefs, thereby liberating bottom-up information flow, particularly via intrinsic sources such as the limbic system. We assemble evidence for this model and show how it can explain a broad range of phenomena associated with the psychedelic experience. With regard to their potential therapeutic use, we propose that psychedelics work to relax the precision weighting of pathologically overweighted priors underpinning various expressions of mental illness. We propose that this process entails an increased sensitization of high-level priors to bottom-up signaling (stemming from intrinsic sources), and that this heightened sensitivity enables the potential revision and deweighting of overweighted priors. We end by discussing further implications of the model, such as that psychedelics can bring about the revision of other heavily weighted high-level priors, not directly related to mental health, such as those underlying partisan and/or overly-confident political, religious, and/or philosophical perspectives. SIGNIFICANCE STATEMENT: Psychedelics are capturing interest, with efforts underway to bring psilocybin therapy to marketing authorisation and legal access within a decade, spearheaded by the findings of a series of phase 2 trials. In this climate, a compelling unified model of how psychedelics alter brain function to alter consciousness would have appeal. Towards this end, we have sought to integrate a leading model of global brain function, hierarchical predictive coding, with an often-cited model of the acute action of psychedelics, the entropic brain hypothesis. The resulting synthesis states that psychedelics work to relax high-level priors, sensitising them to liberated bottom-up information flow, which, with the right intention, care provision and context, can help guide and cultivate the revision of entrenched pathological priors.

Explaining hyper-sensitivity and hypo-responsivity in autism with a common predictive coding-based mechanism

Ward's signal detection theory-based framework elucidates some aspects of interindividual differences in sensitivity, but, we argue, obscures others. Specifically, it disregards the important challenge of inferring the meaning of sensory inputs. Within Bayesian predictive coding accounts, the meaning is given by inferences to more deeply hidden causes of sensory inputs and is generally the basis for initiating context-appropriate (e.g., social) behavior. As such, when inference of hierarchical causes is hampered, as accounts of autism based on deficient precision estimation imply, a form of hyporesponsivity can emerge (together with the hypersensitivity already highlighted by Ward).

The dog (Canis familiaris) as a translational model of autism: It is high time we move from promise to reality

Selecting appropriate animal models for a particular human phenomenon is a difficult but important challenge. The difficulty lies in finding animal behaviors that are not only sufficiently relevant and analog to the complex human symptoms (face validity) but also have similar underlying biological and etiological mechanisms (translational or construct validity), and have "human-like" responses to treatment (predictive validity). Over the past several years, the domestic dog (Canis familiaris) has become increasingly proposed as a model for comparative and translational neuroscience. In parallel to the recent advances in canine behavior research, dogs have also been proposed as a model of many human neuropsychiatric conditions, including autism spectrum disorder (ASD). In this opinion paper we will shortly discuss the challenging nature of autism research then summarize the different neurocognitive frameworks for ASD making the case for a canine model of autism. The translational value of a dog model stems from the recognition that (a) there is a large inter-individual variability in the manifestation of dogs' social cognitive abilities including both high and low phenotypic extremes; (b) the phenotypic similarity between the dog and human symptoms are much higher than between the rodent and human symptoms; (c) the symptoms are functionally analogous to the human condition; and (d) more likely to have similar etiology. This article is categorized under: Psychology > Comparative Psychology Cognitive Biology > Evolutionary Roots of Cognition.

Tactile hypersensitivity and GABA concentration in the sensorimotor cortex of adults with autism

Sensory hypersensitivity is frequently encountered in autism spectrum disorder (ASD). Gamma-aminobutyric acid (GABA) has been hypothesized to play a role in tactile hypersensitivity. The aim of the present study was twofold. First, as a study showed that children with ASD have decreased GABA concentrations in the sensorimotor cortex, we aimed at determining whether the GABA reduction remained in adults with ASD. For this purpose, we used magnetic resonance spectroscopy to measure GABA concentration in the sensorimotor cortex of neurotypical adults (n = 19) and ASD adults (n = 18). Second, we aimed at characterizing correlations between GABA concentration and tactile hypersensitivity in ASD. GABA concentration in the sensorimotor cortex of adults with ASD was lower than in neurotypical adults (decrease by 17%). Interestingly, GABA concentrations were positively correlated with self-reported tactile hypersensitivity in adults with ASD (r = 0.50, P = 0.01), but not in neurotypical adults. In addition, GABA concentrations were negatively correlated with the intra-individual variation during threshold measurement, both in neurotypical adults (r = -0.47, P = 0.04) and in adults with ASD (r = -0.59, P = 0.01). In other words, in both groups, the higher the GABA level, the more precise the tactile sensation. These results highlight the key role of GABA in tactile sensitivity, and suggest that atypical GABA modulation contributes to tactile hypersensitivity in ASD. We discuss the hypothesis that hypersensitivity in ASD could be due to suboptimal predictions about sensations. Autism Research 2019, 12: 562-575. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: People with autism spectrum disorder (ASD) often experience tactile hypersensitivity. Here, our goal was to highlight a link between tactile hypersensitivity and the concentration of gamma-aminobutyric acid (GABA) (an inhibitory neurotransmitter) in the brain of adults with ASD. Indeed, self-reported hypersensitivity correlated with reduced GABA levels in brain areas processing touch. Our study suggests that this neurotransmitter may play a key role in tactile hypersensitivity in autism.

Individual differences in sensory sensitivity: A synthesizing framework and evidence from normal variation and developmental conditions

For some people, simple sensory stimuli (e.g., noises, patterns) may reliably evoke intense and aversive reactions. This is common in certain clinical groups (e.g., autism) and varies greatly in the neurotypical population. This paper critically evaluates the concept of individual differences in sensory sensitivity, explores its possible underlying neurobiological basis, and presents a roadmap for future research in this area. A distinction is made between subjective sensory sensitivity (self-reported symptoms); neural sensory sensitivity (the degree of neural activity induced by sensory stimuli); and behavioral sensory sensitivity (detection and discrimination of sensory stimuli). Whereas increased subjective and neural sensory sensitivity are assumed to increase together, the status of behavioral sensory sensitivity depends on the extent to which the increased neural activity is linked to signal or noise. A signal detection framework is presented that offers a unifying framework for exploring sensory sensitivity across different conditions. The framework is discussed, in more concrete terms, by linking it to four existing theoretical accounts of atypical sensory sensitivity (not necessarily mutually exclusive): increased excitation-to-inhibition ratio; predictive coding; increased neural noise; and atypical brain connectivity.

A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision

Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thoroughly investigated. Using a humanoid robot controlled by a neural network using a precision-weighted prediction error minimization mechanism, it is suggested that both increased and decreased sensory precision could induce the behavioral rigidity characterized by resistance to change that is characteristic of autistic behavior. Specifically, decreased sensory precision caused any error signals to be disregarded, leading to invariability of the robot's intention, while increased sensory precision caused an excessive response to error signals, leading to fluctuations and subsequent fixation of intention. The results may provide a system-level explanation of mechanisms underlying different types of behavioral rigidity in autism spectrum and other psychiatric disorders. In addition, our findings suggest that symptoms caused by decreased and increased sensory precision could be distinguishable by examining the internal experience of patients and neural activity coding prediction error signals in the biological brain.

Sensory Prediction Errors Are Less Modulated by Global Context in Autism Spectrum Disorder

BACKGROUND

Recent predictive coding accounts of autism spectrum disorder (ASD) suggest that a key deficit in ASD concerns the inflexibility in modulating local prediction errors as a function of global top-down expectations. As a direct test of this central hypothesis, we used electroencephalography to investigate whether local prediction error processing was less modulated by global context (i.e., global stimulus frequency) in ASD.

METHODS

A group of 18 adults with ASD was compared with a group of 24 typically developed adults on a well-validated hierarchical auditory oddball task in which participants listened to short sequences of either five identical sounds (local standard) or four identical sounds and a fifth deviant sound (local deviant). The latter condition is known to generate the mismatch negativity (MMN) component, believed to reflect early sensory prediction error processing. Crucially, previous studies have shown that in blocks with a higher frequency of local deviant sequences, top-down expectations seem to attenuate the MMN. We predicted that this modulation by global context would be less pronounced in the ASD group.

RESULTS

Both groups showed an MMN that was modulated by global context. However, this effect was smaller in the ASD group as compared with the typically developed group. In contrast, the P3b, as an electroencephalographic marker of conscious expectation processes, did not differ across groups.

CONCLUSIONS

Our results demonstrate that people with ASD are less flexible in modulating their local predictions (reflected in MMN), thereby confirming the central hypothesis of contemporary predictive coding accounts of ASD.

Hierarchical Letters in ASD: High Stimulus Variability Under Different Attentional Modes

Studies using hierarchical patterns to test global precedence and local-global interference in individuals with ASD have produced mixed results. The current study focused on stimulus variability and locational uncertainty, while using different attentional modes. Two groups of 44 children with and without ASD completed a divided attention task as well as a global and local selective attention task. The results revealed global-to-local and local-to-global interference in ASD, to the same extent as in the TD group. Both groups struggled with the same type of stimuli (i.e., ignoring the global level information) and performed similar in all three tasks. Future studies on (visual) information processing in ASD should pursue the impact of stimulus noise and trial-by-trial uncertainty further.

High internal noise and poor external noise filtering characterize perception in autism spectrum disorder

An emerging hypothesis postulates that internal noise is a key factor influencing perceptual abilities in autism spectrum disorder (ASD). Given fundamental and inescapable effects of noise on nearly all aspects of neural processing, this could be a critical abnormality with broad implications for perception, behavior, and cognition. However, this proposal has been challenged by both theoretical and empirical studies. A crucial question is whether and how internal noise limits perception in ASD, independently from other sources of perceptual inefficiency, such as the ability to filter out external noise. Here, we separately estimated internal noise and external noise filtering in ASD. In children and adolescents with and without ASD, we computationally modeled individuals' visual orientation discrimination in the presence of varying levels of external noise. The results revealed increased internal noise and worse external noise filtering in individuals with ASD. For both factors, we also observed high inter-individual variability in ASD, with only the internal noise estimates significantly correlating with severity of ASD symptoms. We provide evidence for reduced perceptual efficiency in ASD that is due to both increased internal noise and worse external noise filtering, while highlighting internal noise as a possible contributing factor to variability in ASD symptoms.

Sensory perception in autism

Autism is a complex neurodevelopmental condition, and little is known about its neurobiology. Much of autism research has focused on the social, communication and cognitive difficulties associated with the condition. However, the recent revision of the diagnostic criteria for autism has brought another key domain of autistic experience into focus: sensory processing. Here, we review the properties of sensory processing in autism and discuss recent computational and neurobiological insights arising from attention to these behaviours. We argue that sensory traits have important implications for the development of animal and computational models of the condition. Finally, we consider how difficulties in sensory processing may relate to the other domains of behaviour that characterize autism.

Gravity as a Strong Prior: Implications for Perception and Action

In the future, humans are likely to be exposed to environments with altered gravity conditions, be it only visually (Virtual and Augmented Reality), or visually and bodily (space travel). As visually and bodily perceived gravity as well as an interiorized representation of earth gravity are involved in a series of tasks, such as catching, grasping, body orientation estimation and spatial inferences, humans will need to adapt to these new gravity conditions. Performance under earth gravity discrepant conditions has been shown to be relatively poor, and few studies conducted in gravity adaptation are rather discouraging. Especially in VR on earth, conflicts between bodily and visual gravity cues seem to make a full adaptation to visually perceived earth-discrepant gravities nearly impossible, and even in space, when visual and bodily cues are congruent, adaptation is extremely slow. We invoke a Bayesian framework for gravity related perceptual processes, in which earth gravity holds the status of a so called “strong prior”. As other strong priors, the gravity prior has developed through years and years of experience in an earth gravity environment. For this reason, the reliability of this representation is extremely high and overrules any sensory information to its contrary. While also other factors such as the multisensory nature of gravity perception need to be taken into account, we present the strong prior account as a unifying explanation for empirical results in gravity perception and adaptation to earth-discrepant gravities.