September 2011
Volume 11, Issue 11
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2011
Breakdown of object-based representations in visual working memory
Author Affiliations
  • Daryl Fougnie
    Department of Psychology, Harvard University, USA
    Vision Laboratory, Harvard University, USA
  • George A. Alvarez
    Department of Psychology, Harvard University, USA
    Vision Laboratory, Harvard University, USA
Journal of Vision September 2011, Vol.11, 1237. doi:10.1167/11.11.1237
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      Daryl Fougnie, George A. Alvarez; Breakdown of object-based representations in visual working memory. Journal of Vision 2011;11(11):1237. doi: 10.1167/11.11.1237.

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

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Abstract

How does the structure of the environment shape what we store in working memory? Information in the world is bound into meaningful units -objects- and it is widely believed that the contents of visual working memory are bound object representations. This account suggests that, for sample displays containing more information than can be stored, we have some knowledge of all the features of stored objects, and no featural information from the subset of objects not stored. Thus, the information that is retained is determined by how features are grouped into objects. Using a task that requires multiple feature reports to a single working memory item, we find evidence against this object-based model of working memory. Our task required participants to remember the color and orientation of five isosceles triangles. After a short delay, participants were required to report the color and orientation of a single, randomly selected, probe item. The task was challenging – histograms of response error of participants' color and orientation judgments showed a high proportion of guess responses. To estimate the guess rate and precision responses, the error distributions were fit with a mixture of a uniform and circular normal distribution (Zhang & Luck, 2008). Responses that were three standard deviations away from the target value were classified as guesses. Contrary to the predictions of object-based models, when participants randomly guessed the color they were still often quite accurate at indicating the orientation of the same item, and vice-versa. Follow up analysis and experiments show that these results were not due to failures arising during the feature report stage or the use of verbal rehearsal. In contrast to the object-based model, we propose a probabilistic model in which information for all items and all features is stored, but that representations fail probabilistically, and independently for each stored feature.

NRSA (NEI) 1F32EY020706 to D.F. and NIH-R03 MHO86743 to G.A. 
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