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A surface-based code contributes to visual shape perception
Author(s) -
Martin Arguin,
Ian Marleau,
Mercédès Aubin,
Sacha Zahabi,
E. Charles Leek
Publication year - 2019
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/19.11.6
Subject(s) - representation (politics) , code (set theory) , surface (topology) , perception , similarity (geometry) , computer science , artifact (error) , artificial intelligence , computer vision , human visual system model , conjunction (astronomy) , image (mathematics) , pattern recognition (psychology) , mathematics , set (abstract data type) , psychology , geometry , physics , astronomy , neuroscience , politics , political science , law , programming language
Considerable uncertainty remains regarding the types of features human vision uses for shape representation. Visual-search experiments are reported which assessed the hypothesis of a surface-based (i.e., edge-bounded polygons) code for shape representation in human vision. The results indicate slower search rates and/or longer response times when the target shape shares its constituent surfaces with distractors (conjunction condition) than when the target surfaces are unique in the display (nonconjunction condition). This demonstration is made using test conditions that strictly control any potential artifact pertaining to target-distractor similarity. The surface-based code suggested by this surface-conjunction effect is strictly 2-D, since the effect occurs even when the surfaces are shared between the target and distractors in the 2-D image but not in their 3-D instantiation. Congruently, this latter finding is unaltered by manipulations of the richness of the depth information offered by the stimuli. It is proposed that human vision uses a 2-D surface-based code for shape representation which, considering other key findings in the field, probably coexists with an alternative representation mode based on a type of structural description that can integrate information pertaining to the 3-D aspect of shapes.

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