Abstract
A predictive coding perspective on autism spectrum disorders
Highlights
In a recent article entitled “When the world becomes ‘too real’: Bayesian explanation of autistic perception,” Elizabeth Pellicano and David Burr (Pellicano and Burr, 2012b) introduce an intriguing new hypothesis, a Bayesian account, concerning the possible origins of perceptual deficits in Autism Spectrum Disorder (ASD)
Pellicano and Burr suggest that people with ASD have weak priors compared to the typicallydeveloping population, explaining a key finding that autistic observers are less influenced by contextual information, and see the world more accurately, as their perception is less modulated by experience
This Bayesian account provides an explanation for the bias favoring local over global processing in ASD
Summary
In a recent article entitled “When the world becomes ‘too real’: Bayesian explanation of autistic perception,” Elizabeth Pellicano and David Burr (Pellicano and Burr, 2012b) introduce an intriguing new hypothesis, a Bayesian account, concerning the possible origins of perceptual deficits in Autism Spectrum Disorder (ASD). Pellicano and Burr suggest that people with ASD have weak priors compared to the typicallydeveloping population, explaining a key finding that autistic observers are less influenced by contextual information, and see the world more accurately (as it is), as their perception is less modulated by experience. The predictive coding framework provides a natural implementation of the prior used in the Bayesian model proposed by Pellicano and Burr.
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