Automatic guidance of attention during real-world visual search

Katharina N. Seidl-Rathkopf, Nicholas B. Turk-Browne, Sabine Kastner

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Looking for objects in cluttered natural environments is a frequent task in everyday life. This process can be difficult, because the features, locations, and times of appearance of relevant objects often are not known in advance. Thus, a mechanism by which attention is automatically biased toward information that is potentially relevant may be helpful. We tested for such a mechanism across five experiments by engaging participants in real-world visual search and then assessing attentional capture for information that was related to the search set but was otherwise irrelevant. Isolated objects captured attention while preparing to search for objects from the same category embedded in a scene, as revealed by lower detection performance (Experiment 1A). This capture effect was driven by a central processing bottleneck rather than the withdrawal of spatial attention (Experiment 1B), occurred automatically even in a secondary task (Experiment 2A), and reflected enhancement of matching information rather than suppression of nonmatching information (Experiment 2B). Finally, attentional capture extended to objects that were semantically associated with the target category (Experiment 3). We conclude that attention is efficiently drawn towards a wide range of information that may be relevant for an upcoming real-world visual search. This mechanism may be adaptive, allowing us to find information useful for our behavioral goals in the face of uncertainty.

Original languageEnglish (US)
Pages (from-to)1881-1895
Number of pages15
JournalAttention, Perception, and Psychophysics
Volume77
Issue number6
DOIs
StatePublished - Aug 1 2015

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Experimental and Cognitive Psychology
  • Sensory Systems
  • Linguistics and Language

Keywords

  • Attentional capture
  • Scene perception
  • Visual search

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