Unique objects attract attention even when faint

Daniel M. Jeck, Michael Qin, Howard Egeth, Ernst Niebur

Research output: Contribution to journalArticle

Abstract

Locally contrasting objects, e.g. a red apple surrounded by green apples, attract attention. Does this generalize to differences in feature space? That is, do unique objects-regardless of their location-stand out from a collection of objects that are similar to one another, even when the unique object has lower local contrast with the background than the other objects? Behavioral data show indeed a preference for unique items but previous experiments enabled viewers to anticipate what response they were “supposed” to give. We developed a new experimental paradigm that minimizes such top-down effects. Pitting local contrast against global uniqueness, we show that unique stimuli attract attention even in not-anticipated, never-seen images, and even when the unique stimuli are faint (low contrast). A computational model explains how competition between objects in feature space favors dissimilar objects over those with similar features. The model explains how humans select unique objects, without a loss of performance on natural scenes.

Original languageEnglish (US)
Pages (from-to)60-71
Number of pages12
JournalVision Research
Volume160
DOIs
StatePublished - Jul 2019

Keywords

  • Attention
  • Bottom-up
  • Saliency
  • Top-down
  • Uniqueness
  • Weak signals

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

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