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Oxford University Press, Cerebral Cortex, p. bhw147, 2016

DOI: 10.1093/cercor/bhw147

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Contour Integration over Time: Psychophysical and fMRI Evidence

Journal article published in 2016 by Shu-Guang Kuai, Kuai Sg, Wu Li, Cong Yu, Zoe Kourtzi ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Other ; This is the author accepted manuscript. The final version is available from Oxford University Press via http://dx.doi.org/10.1093/cercor/bhw147 ; Abstract ; The brain integrates discrete but collinear stimuli to perceive global contours. Previous contour integration (CI) studies mainly focus on integration over space, and CI is attributed to either V1 long-range connections or contour processing in high-visual areas that top-down modulate V1 responses. Here, we show that CI also occurs over time in a design that minimizes the roles of V1 long-range interactions. We use tilted contours embedded in random orientation noise and moving horizontally behind a fixed vertical slit. Individual contour elements traveling up/down within the slit would be encoded over time by parallel, rather than aligned, V1 neurons. However, we find robust contour detection even when the slit permits only one viewable contour element. Similar to CI over space, CI over time also obeys the rule of collinearity. fMRI evidence shows that while CI over space engages visual areas as early as V1, CI over time mainly engages higher dorsal and ventral visual areas involved in shape processing, as well as posterior parietal regions involved in visual memory that can represent the orientation of temporally integrated contours. These results suggest at least partially dissociable mechanisms for implementing the Gestalt rule of continuity in CI over space and time. ; Other ; European Community’s Seventh Framework Programme (FP7/2007-2013) (Grant ID: 255577), Biotechnology and Biological Sciences Research Council (Grant IDs: D52199X , E027436), National Natural Science Foundation of China (Grant IDs: 31230030, 31571160, 91432102)