摘要：Pellicano and Burr (2012) argue that a Bayesian framework can help us understand the perceptual peculiarities in autism. We agree, but we think that their assumption of uniformly flat or equivocal priors in autism is not empirically supported. Moreover, we argue that any full account has to take into consideration not only the nature of priors in autism, but also how these priors are constructed or learned. We argue that predictive coding provides a more constrained framework that very naturally explains how priors are constructed in autism leading to strong, but overfitted, and non-generalizable predictions.
