Multivariate summary of a complex scene

Jihong Lee; Sang Wook Hong; Sang Chul Chong

doi:10.1016/j.visres.2021.08.006

Multivariate summary of a complex scene

Jihong Lee, Sang Wook Hong, Sang Chul Chong

Department of Psychology

Research output: Contribution to journal › Article › peer-review

Abstract

The current study investigated how people summarize and represent objects with multiple features to cope with the complexity due to the number of objects and feature dimensions. We presented a set of circles whose color and size were either correlated perfectly (r = 1) or not correlated at all (r = 0). Using a membership identification task, we found that participants formed a statistical representation that included information about conjunctions as well as each color and size dimensions. In addition, we found that participants represented different set boundaries depending on the correlation between features of a set. Lastly, a pair-matching task revealed that participants predicted one feature value from the other feature value based on the correlation between features of a set. Our findings suggest that people represent a multi-feature ensemble statistically as a multivariate feature distribution, which is an efficient strategy to cope with scene complexity.

Original language	English
Pages (from-to)	11-26
Number of pages	16
Journal	Vision Research
Volume	189
DOIs	https://doi.org/10.1016/j.visres.2021.08.006
Publication status	Published - 2021 Dec

Bibliographical note

Funding Information:
This work was supported by the Korean government (MSIT) (NRF-2017M3C7A1029658). All or part of the data in this research article were previously presented at the 20 th annual meeting of the Vision Sciences Society (June 2020) and at the 43rd European Conference on Visual Perception (August 2021). The data were also included in Jihong Lee’s master’s thesis. The raw data for all experiments are available on the Open Science Framework (https://osf.io/jd79k/).

Publisher Copyright:
© 2021 Elsevier Ltd

All Science Journal Classification (ASJC) codes

Ophthalmology
Sensory Systems

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10.1016/j.visres.2021.08.006

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title = "Multivariate summary of a complex scene",

abstract = "The current study investigated how people summarize and represent objects with multiple features to cope with the complexity due to the number of objects and feature dimensions. We presented a set of circles whose color and size were either correlated perfectly (r = 1) or not correlated at all (r = 0). Using a membership identification task, we found that participants formed a statistical representation that included information about conjunctions as well as each color and size dimensions. In addition, we found that participants represented different set boundaries depending on the correlation between features of a set. Lastly, a pair-matching task revealed that participants predicted one feature value from the other feature value based on the correlation between features of a set. Our findings suggest that people represent a multi-feature ensemble statistically as a multivariate feature distribution, which is an efficient strategy to cope with scene complexity.",

author = "Jihong Lee and Hong, {Sang Wook} and Chong, {Sang Chul}",

note = "Funding Information: This work was supported by the Korean government (MSIT) (NRF-2017M3C7A1029658). All or part of the data in this research article were previously presented at the 20 th annual meeting of the Vision Sciences Society (June 2020) and at the 43rd European Conference on Visual Perception (August 2021). The data were also included in Jihong Lee{\textquoteright}s master{\textquoteright}s thesis. The raw data for all experiments are available on the Open Science Framework (https://osf.io/jd79k/). Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

year = "2021",

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doi = "10.1016/j.visres.2021.08.006",

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AB - The current study investigated how people summarize and represent objects with multiple features to cope with the complexity due to the number of objects and feature dimensions. We presented a set of circles whose color and size were either correlated perfectly (r = 1) or not correlated at all (r = 0). Using a membership identification task, we found that participants formed a statistical representation that included information about conjunctions as well as each color and size dimensions. In addition, we found that participants represented different set boundaries depending on the correlation between features of a set. Lastly, a pair-matching task revealed that participants predicted one feature value from the other feature value based on the correlation between features of a set. Our findings suggest that people represent a multi-feature ensemble statistically as a multivariate feature distribution, which is an efficient strategy to cope with scene complexity.

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Multivariate summary of a complex scene

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