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Wu R, Wang B, Zhuo Y, Chen L. Topological dominance in peripheral vision. J Vis 2021; 21:19. [PMID: 34570176 PMCID: PMC8479572 DOI: 10.1167/jov.21.10.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
The question of what peripheral vision is good for, especially in pattern recognition, is one of the most important and controversial issues in cognitive science. In a series of experiments, we provide substantial evidence that observers’ behavioral performance in the periphery is consistently superior to central vision for topological change detection, while nontopological change detection deteriorates with increasing eccentricity. These experiments generalize the topological account of object perception in the periphery to different kinds of topological changes (i.e., including introduction, disappearance, and change in number of holes) in comparison with a broad spectrum of geometric properties (e.g., luminance, similarity, spatial frequency, perimeter, and shape of the contour). Moreover, when the stimuli were scaled according to cortical magnification factor and the task difficulty was well controlled by adjusting luminance of the background, the advantage of topological change detection in the periphery remained. The observed advantage of topological change detection in the periphery supports the view that the topological definition of objects provides a coherent account for object perception in peripheral vision, allowing pattern recognition with limited acuity.
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Affiliation(s)
- Ruijie Wu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,
| | - Bo Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China.,University of Chinese Academy of Sciences, Beijing, China.,
| | - Yan Zhuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,
| | - Lin Chen
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.,
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