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.

Automated summary

Autism spectrum disorder (ASD) has a wide range of impacts on behaviors and neural functions, leading to numerous theories at different levels of description. Our proposal aims to relate existing behavioral, computational, algorithmic, and neural accounts of ASD to each other. We suggest that ASD can be seen as a disorder of causal inference at the computational level, which relies on divisive normalization at the algorithmic level. Impairments in divisive normalization may be caused by excitatory-to-inhibitory imbalances at the neural implementation level. We also explore similar frameworks, such as predictive coding and circular inference, in relation to ASD. Our goal is to inspire efforts in unifying the various explanations of ASD.