title: Weak priors versus overfitting of predictions in autism: Reply to Pellicano and Burr (TICS, 2012).
abstract: 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.
authors.affiliation: Department of Experimental Psychology, University of Leuven, Leuven, Tiensestraat 102, Belgium; E-mail: sander.vandecruys@ppw.kuleuven.be.
authors.forename: Sander
authors.initials: S
authors.lastname: Van de Cruys
authors.forename: Lee
authors.initials: L
authors.lastname: de-Wit
authors.forename: Kris
authors.initials: K
authors.lastname: Evers
authors.forename: Bart
authors.initials: B
authors.lastname: Boets
authors.forename: Johan
authors.initials: J
authors.lastname: Wagemans
keywords: Bayes; autism; perception; predictive coding; priors; vision