Jeck DM, Qin M, Egeth H, Niebur E. Unique objects attract attention even when faint.
Vision Res 2019;
160:60-71. [PMID:
31047908 DOI:
10.1016/j.visres.2019.04.004]
[Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 04/11/2019] [Accepted: 04/14/2019] [Indexed: 11/20/2022]
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.
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