September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Category-based updating of object representations
Author Affiliations
  • Ru Qi Yu
    Department of Psychology, University of British Columbia
  • Jiaying Zhao
    Department of Psychology, University of British Columbia
    Institute for Resources, Environment and Sustainability, University of British Columbia
Journal of Vision August 2017, Vol.17, 167. doi:10.1167/17.10.167
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      Ru Qi Yu, Jiaying Zhao; Category-based updating of object representations. Journal of Vision 2017;17(10):167. doi: 10.1167/17.10.167.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The visual system is efficient at detecting regularities in the environment. When two objects reliably co-occur, changes in one object are automatically transferred to its co-occurring partner. It is unknown how such updating can transpire across categorical boundaries. In Experiment 1, participants viewed a random temporal stream of objects, which came from two distinct categories based on texture (i.e., objects in Category A had stripes vs. objects in Category B had dots). Each object had a unique shape. After exposure, one object in Category A (e.g., A1) increased in size, and participants recalled the size of another object in the same category (e.g., A2) or in the different category (e.g., B1). We found that objects in the same category were recalled to be reliably larger than objects in the different category, suggesting that changes in one object are more likely to be transferred to another in the same category than in an object in a different category. To elucidate if the cross-category transfer can be facilitated by statistical regularities, we conducted Experiment 2, where participants viewed the same objects, except now objects in the two categories were temporally paired (i.e., A1 reliably appeared before B1). After exposure, one object in Category A (e.g., A1) increased in size, and participants recalled the size of the cross-category paired partner (B1), the within-category random object (A2), or cross-category random object (B2). We found that the within-category object (A2) was recalled to be reliably larger than any cross-category object (B1 or B2). This suggests that changes in one object were more strongly transferred to other objects in the same category any objects of a different category, regardless of statistical regularities. These results reveal a within-category advantage of updating of feature changes, that they are more readily transferred within the same category than across categories.

Meeting abstract presented at VSS 2017

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