A predictive coding perspective on autism spectrum disorders

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). This Bayesian account explains why ASD impacts perception in systematic ways, but it does not clearly explain how. Most prominently, the Bayesian account lacks connections to the neural computation performed by the brain, and does not provide mechanistic explanations for ASD (Rust and Stocker, 2010; Colombo and Series, 2012). Nor does the Bayesian account explain what the biological origin is of the “prior”—the essential addition of the Bayesian models. In Marr’s terminology (Marr, 1982), Pellicano and Burr paper proposes a computational-level explanation for ASD, but not an account for the other two levels, representation and implementation. We propose that a predictive coding framework (schematized in Figure 1) may fill the gap and generate a testable framework open to further experimental investigations. In Pellicano and Burr’s general Bayesian approach, perception is based on the integration of stimulus information (encapsulated in the likelihood) and regularizing (contextual) Prediction (feedback = empirical prior)