Signal detection theory applied to three visual search tasks - Identification, yes/no detection and localization

E. Leslie Cameron, Joanna C. Tai, Miguel P. Eckstein, Marisa Carrasco

Research output: Contribution to journalArticlepeer-review


Adding distracters to a display impairs performance on visual tasks (i.e. the set-size effect). While keeping the display characteristics constant, we investigated this effect in three tasks: 2 target identification, yes-no detection with 2 targets, and 8-alternative localization. A Signal Detection Theory (SDT) model, tailored for each task, accounts for the set-size effects observed in identification and localization tasks, and slightly under-predicts the set-size effect in a detection task. Given that sensitivity varies as a function of spatial frequency (SF), we measured performance in each of these three tasks in neutral and peripheral precue conditions for each of six spatial frequencies (0.5-12 cpd). For all spatial frequencies tested, performance on the three tasks decreased as set size increased in the neutral precue condition, and the peripheral precue reduced the effect. Larger set-size effects were observed at low SFs in the identification and localization tasks. This effect can be described using the SDT model, but was not predicted by it. For each of these tasks we also established the extent to which covert attention modulates performance across a range of set sizes. A peripheral precue substantially diminished the set-size effect and improved performance, even at set size 1. These results provide support for distracter exclusion, and suggest that signal enhancement may also be a mechanism by which covert attention can impose its effect.

Original languageEnglish (US)
Pages (from-to)295-325
Number of pages31
JournalSpatial Vision
Issue number4-5
StatePublished - 2004


  • Discrimination
  • Localization
  • Signal detection theory
  • Transient covert attention
  • Visual search
  • Yes/no detection

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Computer Vision and Pattern Recognition


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