Feature Integration Theory Revisited : Dissociating Feature Detection and Attentional Guidance in Visual Search

Louis K. H. CHAN*, William G. HAYWARD

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

40 Citations (Scopus)

Abstract

In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT's postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework.

Original languageEnglish
Pages (from-to)119-132
Number of pages14
JournalJournal of Experimental Psychology: Human Perception and Performance
Volume35
Issue number1
DOIs
Publication statusPublished - Feb 2009
Externally publishedYes

Bibliographical note

This research was supported by a University of Hong Kong (HKU) Postgraduate Fellowship to Louis K. H. Chan and by Hong Kong Research Grants Council Grant HKU 7649/06H to William G. Hayward. We would like to thank Hermann J. Mu¨ller and Joseph Krummenacher for their helpful comments.

Keywords

  • dimension-based attention
  • feature integration theory
  • salience map
  • visual search

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