Spatial predictive context speeds up visual search by biasing local attentional competition

Abstract

AbstractThe human visual system is equipped to rapidly and implicitly learn and exploit the statistical regularities in our environment. Within visual search, contextual cueing demonstrates how implicit knowledge of scenes can improve search performance. This is commonly interpreted as spatial context in the scenes becoming predictive of the target location, which leads to a more efficient guidance of attention during search. However, what drives this enhanced guidance is unknown. First, it is unclear whether improved attentional guidance is enabled by target enhancement or distractor suppression. Second, it is unknown whether the entire scene (global context) or more local context drives this phenomenon. In the present MEG experiment, we leveraged Rapid Invisible Frequency Tagging (RIFT) to answer these two outstanding questions. We found that the improved performance when searching implicitly familiar scenes was accompanied by a stronger neural representation of the target stimulus, at the cost specifically of those distractors directly surrounding the target. Crucially, this biasing of local attentional competition was behaviorally relevant when searching familiar scenes, indicating that it is the local, and not global, spatial context that is modulated, culminating in a search advantage for familiar scenes. Taken together, we conclude that implicitly learned spatial predictive context improves how we search our environment by sharpening the attentional field.