Contextual cost: When a visual-search target is not where it should be

Tal Makovski, Yuhong V. Jiang

    Research output: Contribution to journalArticlepeer-review

    39 Scopus citations

    Abstract

    Visual search is often facilitated when the search display occasionally repeats, revealing a contextual-cueing effect. According to the associative-learning account, contextual cueing arises from associating the display configuration with the target location. However, recent findings emphasizing the importance of local context near the target have given rise to the possibility that low-level repetition priming may account for the contextual-cueing effect. This study distinguishes associative learning from local repetition priming by testing whether search is directed toward a target's expected location, even when the target is relocated. After participants searched for a T among Ls in displays that repeated 24 times, they completed a transfer session where the target was relocated locally to a previously blank location (Experiment 1) or to an adjacent distractor location (Experiment 2). Results revealed that contextual cueing decreased as the target appeared farther away from its expected location, ultimately resulting in a contextual cost when the target swapped locations with a local distractor. We conclude that target predictability is a key factor in contextual cueing.

    Original languageEnglish (US)
    Pages (from-to)216-225
    Number of pages10
    JournalQuarterly Journal of Experimental Psychology
    Volume63
    Issue number2
    DOIs
    StatePublished - Feb 2010

    Bibliographical note

    Funding Information:
    Correspondence should be addressed to Tal Makovski, N218 Elliott Hall, Minneapolis, MN 55455, USA. Email: tal.makovski@gmail.com This study was supported in part by National Institutes of Health Grant NIH MH071788 to Yuhong Jiang. We thank Melina Kunar, Khena Swallow, Derrick Watson, and two anonymous reviewers for helpful suggestions.

    Keywords

    • Contextual cueing
    • Repetition priming
    • Visual search

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