No increased circular inference in adults with high levels of autistic traits or autism

International audienceAutism 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 self-reported diagnoses of 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 autistic traits or autism and circular inference. There was no correlation between prior or likelihood reverberation and autistic traits across the whole sample. Similarly, no differences in any of the circular inference model parameters were found between autistic participants and those with no diagnosis. 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

Bayesian_ASD_review

Study characteristics (including the relevant statistics) and processing script for: 10 years of Bayesian theories of autism: a systematic review. (2022). In Prep

CircularInference_ASD

Participant data, experiment, and analysis code for: Angeletos Chrysaitis N., Jardri R., Denève S., Seriès P. (2021). No increased circular inference in adults with high levels of autistic traits or autism. PLOS Computational Biology, 17(9), e1009006

10 years of Bayesian theories of autism: a systematic review

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