10 years of Bayesian theories of autism: A comprehensive review. (February 2023) - Angeletos Chrysaitis, Nikitas; Seriès, Peggy
Abstract: Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research. Highlights: Popular theories propose that Bayesian priors are weaker in autism. We review 83 studies that have tested this hypothesis and find little support. Priors developed during experiments were slightly more likely to differ. Effect sizes were small; studies were significantly underpowered. Methods were heterogeneous and did not follow recommended guidelines.