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Attention to distinguishing features in object recognition: An interactive-iterative framework.

Authors
  • Baruch, Orit1
  • Kimchi, Ruth2
  • Goldsmith, Morris2
  • 1 University of Haifa, Israel. Electronic address: [email protected] , (Israel)
  • 2 University of Haifa, Israel. , (Israel)
Type
Published Article
Journal
Cognition
Publication Date
Jan 01, 2018
Volume
170
Pages
228–244
Identifiers
DOI: 10.1016/j.cognition.2017.10.007
PMID: 29078095
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

This article advances a framework that casts object recognition as a process of discrimination between alternative object identities, in which top-down and bottom-up processes interact-iteratively when necessary-with attention to distinguishing features playing a critical role. In two experiments, observers discriminated between different types of artificial fish. In parallel, a secondary, variable-SOA visual-probe detection task was used to examine the dynamics of visual attention. In Experiment 1, the fish varied in three distinguishing features: one indicating the general category (saltwater, freshwater), and one of the two other features indicating the specific type of fish within each category. As predicted, in the course of recognizing each fish, attention was allocated iteratively to the distinguishing features in an optimal manner: first to the general category feature, and then, based on its value, to the second feature that identified the specific fish. In Experiment 2, two types of fish could be discriminated on the basis of either of two distinguishing features, one more visually discriminable than the other. On some of the trials, one of the two alternative distinguishing features was occluded. As predicted, in the course of recognizing each fish, attention was directed initially to the more discriminable distinguishing feature, but when this feature was occluded, it was then redirected to the less discriminable feature. The implications of these findings, and the interactive-iterative framework they support, are discussed with regard to several fundamental issues having a long history in the literatures on object recognition, object categorization, and visual perception in general. Copyright © 2017 Elsevier B.V. All rights reserved.

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