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Different features are stored independently in visual working memory but mediated by object-based representations

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Listed:
  • Yuri A. Markov

    (National Research University Higher School of Economics)

  • Natalia A. Tiurina

    (National Research University Higher School of Economics)

  • Igor S. Utochkin

    (National Research University Higher School of Economics)

Abstract

The question whether visual working memory (VWM) stores individual features or bound objects as basic units is actively debated. Evidence exists for both feature-based and object-based storages, as well as hierarchically organized representations maintaining both types of information at different levels. One argument for feature-based storage is that features belonging to different dimensions (e.g., color and orientations) can be stored without interference suggesting independent capacities for every dimension. Here, whether the lack of cross-dimensional interference reflects genuinely independent feature storages or mediated by common objects. In three experiments, participants remembered and recalled the colors and orientations of sets of objects. We independently manipulated set sizes within each feature dimension (making colors and orientations either identical or differing across objects). Critically, we assigned to-be-remembered colors and orientations either to same spatially integrated or to different spatially separated objects. We found that the precision and recall probability within each dimension was not affected be set size manipulations in a different dimension when the features belonged to integrated objects. However, manipulations with color set sizes did affect orientation memory when the features were separated. We conclude therefore that different feature dimensions can be encoded and stored independently but the advantage of the independent storages are mediated at the object-based level. This conclusion is consistent with the idea of hierarchically organized VWM.

Suggested Citation

  • Yuri A. Markov & Natalia A. Tiurina & Igor S. Utochkin, 2018. "Different features are stored independently in visual working memory but mediated by object-based representations," HSE Working papers WP BRP 101/PSY/2018, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:101psy2018
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    File URL: https://wp.hse.ru/data/2018/11/14/1141222017/101PSY2018.pdf
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    References listed on IDEAS

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    1. Weiwei Zhang & Steven J. Luck, 2008. "Discrete fixed-resolution representations in visual working memory," Nature, Nature, vol. 453(7192), pages 233-235, May.
    2. Yaoda Xu & Marvin M. Chun, 2006. "Dissociable neural mechanisms supporting visual short-term memory for objects," Nature, Nature, vol. 440(7080), pages 91-95, March.
    3. Yoni Pertzov & Mia Yuan Dong & Muy-Cheng Peich & Masud Husain, 2012. "Forgetting What Was Where: The Fragility of Object-Location Binding," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-12, October.
    4. Steven J. Luck & Edward K. Vogel, 1997. "The capacity of visual working memory for features and conjunctions," Nature, Nature, vol. 390(6657), pages 279-281, November.
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