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Visual search strategies are known to capitalise on the relationship between the target and the distractors – i.e., how the target differs from the distractors and where the target is likely to be amongst the distractors. Additionally, it has been proposed that statistical structure amongst distractors can also benefit search, enabling observers to parse the scene more efficiently by reducing the number of distractors to contend with. Evidence for this hypothesis comes from studies using everyday object stimuli, showing that search efficiency improves when distractor objects are positioned in familiar arrangements (e.g., lamp above table; Kaiser et al. 2014). Here we asked whether the benefit of distractor structure extends to novel shapes, whose spatial relationships (groups) must be learned implicitly during visual search. By using novel shapes, potential low-level visual differences and semantic relationship differences between conditions can be ruled out. Participants searched for pre-cued target items in arrays of shapes that comprised either four fixed pairs of shapes (structured displays) or eight shapes randomly partitioned into four pairs on each trial (unstructured displays). The distractor positions varied across trials. Across three online experiments (N=700), we found that after a period of search training, participants were more efficient when searching for targets in structured displays, even though post-experiment they could not identify which pairs were fixed in a two-alternative forced-choice task. These results show that implicitly learned statistical regularities between distractor shapes increase search efficiency. [Funding source: Horizon 2020 Framework Programme (725970)]
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