Attention sharpens the distinction between expected and unexpected percepts in the visual brain.

Attention, the prioritization of goal-relevant stimuli, and expectation, the modulation of stimulus processing by probabilistic context, represent the two main endogenous determinants of visual cognition. Neural selectivity in visual cortex is enhanced for both attended and expected stimuli, but the functional relationship between these mechanisms is poorly understood. Here, we adjudicated between two current hypotheses of how attention relates to predictive processing, namely, that attention either enhances or filters out perceptual prediction errors (PEs), the PE-promotion model versus the PE-suppression model. We acquired fMRI data from category-selective visual regions while human subjects viewed expected and unexpected stimuli that were either attended or unattended. Then, we trained multivariate neural pattern classifiers to discriminate expected from unexpected stimuli, depending on whether these stimuli had been attended or unattended. If attention promotes PEs, then this should increase the disparity of neural patterns associated with expected and unexpected stimuli, thus enhancing the classifier's ability to distinguish between the two. In contrast, if attention suppresses PEs, then this should reduce the disparity between neural signals for expected and unexpected percepts, thus impairing classifier performance. We demonstrate that attention greatly enhances a neural pattern classifier's ability to discriminate between expected and unexpected stimuli in a region- and stimulus category-specific fashion. These findings are incompatible with the PE-suppression model, but they strongly support the PE-promotion model, whereby attention increases the precision of prediction errors. Our results clarify the relationship between attention and expectation, casting attention as a mechanism for accelerating online error correction in predicting task-relevant visual inputs.