Testing predictive coding theories of autism spectrum disorder using models of active inference

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Predictive coding has been translated into several accounts of atypical processing in Autism. These accounts include predictions that individuals with autism underweight their prior beliefs, assign high and inflexible levels of precision to prediction errors, or are intolerant of environmental volatility. Here we used a hierarchical Bayesian modelling approach (the Hierarchical Gaussian Filter - Mathys et al 2011;2014) to test these predictions in a simple object lifting task and a dynamic movement task (ball interception). The original data sets for this work are described in: https://academic.oup.com/brain/article/143/10/3151/5911081 and https://www.nature.com/articles/s41598-021-99864-y For more information about this and related projects see: https://sshs.exeter.ac.uk/research/humanmovement/vital/projects/sensorimotorcontrol/

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