4.2 Article

Proactive Enhancement and Suppression Elicited by Statistical Regularities in Visual Search

Publisher

AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/xhp0001002

Keywords

statistical learning; visual selection; proactive suppression; proactive enhancement; attention

Funding

  1. European Research Council (ERC) [833029]
  2. China Scholarship Council (CSC) scholarship [201908440284]

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This study investigated the impact of simultaneous statistical learning of target and distractor regularities on attentional selection. The results showed that observers are able to learn the regularities present in the search display and optimize their selection priorities accordingly.
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. Public Significance Statement Through statistical learning, we extract regularities present in the environment that allows us to optimize our search performance. An environment can be complex and have objects that are highly relevant and objects that need to be avoided. This study shows that we are able to extract these regularities and optimally adapt our search priorities even to such a complex environment. By means of an innovative task, the current study is able to provide a glimpse of the attentional priority settings at the moment the search display was presented.

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