Proactive Enhancement and Suppression Elicited by Statistical Regularities in Visual Search

The present study investigated how attentional selection is affected by simultaneous statistical learning of target and distractor regularities. Participants performed an additional singleton task in which the target singleton was presented more often in one location while the distractor singleton was presented more often in another location. On some trials, instead of the search task, participants performed a probe task, in which they had to detect the offset of a probe dot. This probe task made it possible to take a peek at the proactive selection priorities just at the moment the search display was presented. The results show that observers learn the regularities present in the search display such the location that is most likely to contain the target is enhanced while the location that is most likely to contain a distractor is suppressed. We show that these contingencies can be learned simultaneously resulting in optimal selection priorities. The probe task shows that both spatial enhancement and spatial suppression are present at the moment the actual search display is presented, indicating that the attentional priority settings are proactively modulated. We claim that through statistical learning the weights within the spatial priority map of selection are set in such a way that selection is optimally adapted to the implicitly learned regularities