Does masked and unmasked priming reflect Bayesian inference as implemented in the Bayesian Reader?

Abstract

Norris and Kinoshita (2008) describe a Bayesian theory of masked and unmasked priming designed to explain a complex pattern of word and nonword priming across a range of tasks. Their theory is implemented within the Bayesian Reader model, and the model makes some predictions that are confirmed in a set of experiments. The authors consider alternative accounts of priming and conclude that only their theory can account for the results obtained. However, contrary to the authors' claims, the Bayesian Reader makes a number of incorrect predictions regarding masked and unmasked priming phenomena, whereas alternative theories can accommodate current findings.