Perceptual inference and learning in autism : a behavioral and neurophysiological approach

La perception de notre environnement repose sur les informations sensorielles reçues, mais aussi sur nos a priori. Dans le cadre Bayésien, ces a priori capturent les régularités de notre environnement et sont essentiels pour inférer les causes de nos sensations. Récemment, les théories du cerveau Bayésien ont été appliquées à l'autisme pour tenter d'en expliquer les symptômes. Les troubles du spectre de l'autisme (TSA) sont caractérisés par des difficultés de compréhension des interactions sociales, par des comportements restreints et répétitifs, et par une perception sensorielle atypique.Cette thèse vise à caractériser l'inférence et l'apprentissage perceptifs dans les TSA, en étudiant la sensorialité et la construction d'a priori. Nous avons utilisé des tests comportementaux, des modèles computationnels, des questionnaires, de l'imagerie fonctionnelle et de la spectroscopie par résonnance magnétique chez des adultes avec ou sans TSA. La définition des profils sensoriels de personnes avec des hauts quotients autistiques a été affinée grâce à un questionnaire dont nous avons validé la traduction française. En explorant les stratégies d'apprentissage perceptif, nous avons ensuite montré que les personnes avec TSA étaient moins enclines à spontanément utiliser une mode d'apprentissage permettant de généraliser. L'étude de la construction implicite des a priori a montré que les personnes avec TSA étaient capables d'apprendre un a priori, mais l'ajustaient difficilement suite à un changement de contexte. Enfin, l'étude des corrélats neurophysiologiques de l'inférence perceptive a révélé un réseau cérébral et une neuromodulation différents dans les TSA.L'ensemble de ces résultats met en lumière une perception atypique dans les TSA, marquée par un apprentissage et une pondération anormale des a priori. Une approche Bayésienne des TSA pourrait améliorer leur caractérisation, diagnostics et prises en chargeHow we perceive our environment relies both on sensory information and on our priors or expectations. Within the Baysian framework, these priors capture the underlying statistical regularities of our environment and allow inferring sensation causes. Recently, Bayesian brain theories suggested that autistic symptoms could arise from an atypical weighting of sensory information and priors. Autism spectrum disorders (ASD) is characterized defined by difficulties in social interactions, by restricted and repetitive patterns of behaviors, and by an atypical sensory perception.This thesis aims at characterizing perceptual inference and learning in ASD, and studies sensory sensitivity and prior learning. This was investigated using behavioral tasks, computational models, questionnaires, functional magnetic resonance imaging and magnetic resonance spectroscopy in adults with or without ASD. Sensory profiles in people with high autism spectrum quotients were first refined, using a questionnaire that we validated in French. The study of perceptual learning strategies then revealed that subjects with ASD were less inclined to spontaneously use a learning style enabling generalization. The implicit learning of priors was explored and showed that subjects with ASD were able to build up a prior but had difficulties adjusting it in changing contexts. Finally, the investigation of the neurophysiological correlates and molecular underpinnings of a similar task showed that perceptual decisions biased by priors relied on a distinct neural network in ASD, and was not related to the same modulation by the glutamate/GABA ratio.The overall results shed light on an atypical learning and weighting of priors in ASD, resulting in an abnormal perceptual inference. A Bayesian approach could help characterizing ASD and could contribute to ASD diagnosis and car

Dataset article: Disentangling sensory precision and prior expectation of change in autism during tactile discrimination

<p>Datasets of the three experiments presented in the article "Disentangling sensory precision and prior expectation of change in autism during tactile discrimination" by Sapey-Triomphe et al., 2023.</p&gt

Inférence et apprentissage perceptifs dans l’autisme : une approche comportementale et neurophysiologique

How we perceive our environment relies both on sensory information and on our priors or expectations. Within the Baysian framework, these priors capture the underlying statistical regularities of our environment and allow inferring sensation causes. Recently, Bayesian brain theories suggested that autistic symptoms could arise from an atypical weighting of sensory information and priors. Autism spectrum disorders (ASD) is characterized defined by difficulties in social interactions, by restricted and repetitive patterns of behaviors, and by an atypical sensory perception.This thesis aims at characterizing perceptual inference and learning in ASD, and studies sensory sensitivity and prior learning. This was investigated using behavioral tasks, computational models, questionnaires, functional magnetic resonance imaging and magnetic resonance spectroscopy in adults with or without ASD. Sensory profiles in people with high autism spectrum quotients were first refined, using a questionnaire that we validated in French. The study of perceptual learning strategies then revealed that subjects with ASD were less inclined to spontaneously use a learning style enabling generalization. The implicit learning of priors was explored and showed that subjects with ASD were able to build up a prior but had difficulties adjusting it in changing contexts. Finally, the investigation of the neurophysiological correlates and molecular underpinnings of a similar task showed that perceptual decisions biased by priors relied on a distinct neural network in ASD, and was not related to the same modulation by the glutamate/GABA ratio.The overall results shed light on an atypical learning and weighting of priors in ASD, resulting in an abnormal perceptual inference. A Bayesian approach could help characterizing ASD and could contribute to ASD diagnosis and careLa perception de notre environnement repose sur les informations sensorielles reçues, mais aussi sur nos a priori. Dans le cadre Bayésien, ces a priori capturent les régularités de notre environnement et sont essentiels pour inférer les causes de nos sensations. Récemment, les théories du cerveau Bayésien ont été appliquées à l'autisme pour tenter d'en expliquer les symptômes. Les troubles du spectre de l'autisme (TSA) sont caractérisés par des difficultés de compréhension des interactions sociales, par des comportements restreints et répétitifs, et par une perception sensorielle atypique.Cette thèse vise à caractériser l'inférence et l'apprentissage perceptifs dans les TSA, en étudiant la sensorialité et la construction d'a priori. Nous avons utilisé des tests comportementaux, des modèles computationnels, des questionnaires, de l'imagerie fonctionnelle et de la spectroscopie par résonnance magnétique chez des adultes avec ou sans TSA. La définition des profils sensoriels de personnes avec des hauts quotients autistiques a été affinée grâce à un questionnaire dont nous avons validé la traduction française. En explorant les stratégies d'apprentissage perceptif, nous avons ensuite montré que les personnes avec TSA étaient moins enclines à spontanément utiliser une mode d'apprentissage permettant de généraliser. L'étude de la construction implicite des a priori a montré que les personnes avec TSA étaient capables d'apprendre un a priori, mais l'ajustaient difficilement suite à un changement de contexte. Enfin, l'étude des corrélats neurophysiologiques de l'inférence perceptive a révélé un réseau cérébral et une neuromodulation différents dans les TSA.L'ensemble de ces résultats met en lumière une perception atypique dans les TSA, marquée par un apprentissage et une pondération anormale des a priori. Une approche Bayésienne des TSA pourrait améliorer leur caractérisation, diagnostics et prises en charg

The Glasgow Sensory Questionnaire: Validation of a French Language Version and Refinement of Sensory Profiles of People with High Autism-Spectrum Quotient

International audienceSensory sensitivity peculiarities represent an important characteristic of Autism Spectrum Disorders (ASD). We first validated a French language version of the Glasgow Sensory Questionnaire (GSQ) (Robertson and Simmons 2013). The GSQ score was strongly positively correlated with the Autism-Spectrum Quotient (AQ) (r = .81, p < 10-6, n = 245). We further examined sensory profiles of groups with high versus low AQ. The high AQ group scored higher at the GSQ than the low AQ group for every sensory modality. Moreover, the high AQ group showed greater consistency in their patterns of hypersensitivity and hyposensitivity between sensory modalities, and stronger correlations between hyper and hyposensitivity. Results are discussed in the context of theories accounting for atypical sensory perception in ASD

Prediction learning in adults with autism and its molecular correlates

Abstract Background According to Bayesian hypotheses, individuals with Autism Spectrum Disorder (ASD) have difficulties making accurate predictions about their environment. In particular, the mechanisms by which they assign precision to predictions or sensory inputs would be suboptimal in ASD. These mechanisms are thought to be mostly mediated by glutamate and GABA. Here, we aimed to shed light on prediction learning in ASD and on its neurobiological correlates. Methods Twenty-six neurotypical and 26 autistic adults participated in an associative learning task where they had to learn a probabilistic association between a tone and the rotation direction of two dots, in a volatile context. They also took part in magnetic resonance spectroscopy (MRS) measurements to quantify Glx (glutamate and glutamine), GABA + and glutathione in a low-level perceptual region (occipital cortex) and in a higher-level region involved in prediction learning (inferior frontal gyrus). Results Neurotypical and autistic adults had their percepts biased by their expectations, and this bias was smaller for individuals with a more atypical sensory sensitivity. Both groups were able to learn the association and to update their beliefs after a change in contingency. Interestingly, the percentage of correct predictions was correlated with the Glx/GABA + ratio in the occipital cortex (positive correlation) and in the right inferior frontal gyrus (negative correlation). In this region, MRS results also showed an increased concentration of Glx in the ASD group compared to the neurotypical group. Limitations We used a quite restrictive approach to select the MR spectra showing a good fit, which led to the exclusion of some MRS datasets and therefore to the reduction of the sample size for certain metabolites/regions. Conclusions Autistic adults appeared to have intact abilities to make predictions in this task, in contrast with the Bayesian hypotheses of ASD. Yet, higher ratios of Glx/GABA + in a frontal region were associated with decreased predictive abilities, and ASD individuals tended to have more Glx in this region. This neurobiological difference might contribute to suboptimal predictive mechanisms in ASD in certain contexts

Tactile Hypersensitivity and GABA Concentration in the Sensorimotor Cortex of Adults with Autism

International audienceSensory 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. 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

Adults with Autism Tend to Underestimate the Hidden Environmental Structure: Evidence from a Visual Associative Learning Task

International audienceThe learning-style theory of Autism Spectrum Disorders (ASD) (Qian and Lipkin 2011) states that individuals with ASD differ from neurotypics in the way they learn and store information about the environment and its structure. ASD would rather adopt a lookup-table strategy (LUT: memorizing each experience), while neurotypics would favor an interpolation style (INT: extracting regularities to generalize). In a series of visual behavioral tasks, we tested this hypothesis in 20 neurotypical and 20 ASD adults. ASD participants had difficulties using the INT style when instructions were hidden but not when instructions were revealed. Rather than an inability to use rules, ASD would be characterized by a disinclination to generalize and infer such rules

Deciphering human motion to discriminate social interactions: a developmental neuroimaging study

International audienceNon-verbal communication plays a major role in social interaction understanding. Using functional magnetic resonance imaging, we explored the development of the neural networks involved in social interaction recognition based on human motion in children (8-11), adolescents (13-17), and adults (20-41). Participants watched point-light videos depicting two actors interacting or moving independently and were asked whether these agents were interacting or not. All groups successfully performed the discrimination task, but children had a lower performance and longer response times than the older groups. In all three groups, the posterior parts of the superior temporal sulci and middle temporal gyri, the inferior frontal gyri and the anterior temporal lobes showed greater activation when observing social interactions. In addition, adolescents and adults recruited the caudate nucleus and some frontal regions that are part of the mirror system. Adults showed greater activations in parietal and frontal regions (part of them belonging to the social brain) than adolescents. An increased number of regions that are part of the mirror system network or the social brain, as well as the caudate nucleus, were recruited with age. In conclusion, a shared set of brain regions enabling the discrimination of social interactions from neutral movements through human motion is already present in 8-year-old children. Developmental processes such as refinements in the social brain and mirror system would help grasping subtle cues in non-verbal aspects of social interactions

Neural correlates of hierarchical predictive processes in autistic adults

Abstract Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination

Abstract Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD