Can predictive coding explain repetition suppression?

While in earlier work various local or bottom-up neural mechanisms were proposed to give rise to repetition suppression (RS), current theories suggest that top-down processes play a role in determining the repetition related reduction of the neural responses. In the current review we summarise those results, which support the role of these top-down processes, concentrating on the Bayesian models of predictive coding (PC). Such models assume that RS is related to the statistical probabilities of prior stimulus occurrences and to the future predictability of these stimuli. Here we review the current results that support or argue against this explanation. We point out that the heterogeneity of experimental manipulations that are thought to reflect predictive processes are likely to measure different processing steps, making their direct comparison difficult. In addition we emphasize the importance of identifying these sub-processes and clarifying their role in explaining RS. Finally, we propose a two-stage model for explaining the relationships of repetition and expectation phenomena in the human cortex.