Statistical learning of distractor co-occurrences facilitates visual search

Abstract

AbstractVisual search is facilitated by knowledge of the relationship between the target and the distractors, including both where the target is likely to be amongst the distractors and how it differs from the distractors. Whether the statistical structure amongst distractors themselves, unrelated to target properties, facilitates search is less well understood. Here, we assessed the benefit of distractor structure using novel shapes whose relationship to each other was learned implicitly during visual search. Participants searched for target items in arrays of shapes that comprised either four pairs of co-occurring distractor shapes (structured scenes) or eight distractor shapes randomly partitioned into four pairs on each trial (unstructured scenes). Across five online experiments (N=1140), we found that after a period of search training, participants were more efficient when searching for targets in structured than unstructured scenes. This structure-benefit emerged independently of whether the position of the shapes within each pair was fixed or variable, and despite participants having no explicit knowledge of the structured pairs they had seen. These results show that implicitly learned co-occurrence statistics between distractor shapes increases search efficiency. Increased efficiency in the rejection of regularly co-occurring distractors may contribute to the efficiency of visual search in natural scenes, where such regularities are abundant.