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Decoding reveals the contents of visual working memory in early visual areas

Author

Listed:
  • Stephenie A. Harrison

    (Vanderbilt University, Nashville, Tennessee 37240, USA)

  • Frank Tong

    (Vanderbilt University, Nashville, Tennessee 37240, USA)

Abstract

Seeing is remembering Although we can hold several different items in working visual memory, how we remember specific details and visual features of individual objects remains a mystery. The neurons in the higher-order areas responsible for working memory seem to exhibit no selectivity for visual detail, and the early visual areas of the cerebral cortex are uniquely able to process incoming visual signals from the eye but, it was thought, not to perform higher cognitive functions such as memory. Using a new technique for decoding data from functional magnetic resonance imaging (fMRI), Stephanie Harrison and Frank Tong have found that early visual areas can retain specific information about features held in working memory. Volunteers were shown two striped patterns at different orientations and asked to memorize one of the orientations whilst being scanned by fMRI. From analysis of the scans it was possible to predict which of the two orientation patterns a subject was being retained in over 80% of tests.

Suggested Citation

  • Stephenie A. Harrison & Frank Tong, 2009. "Decoding reveals the contents of visual working memory in early visual areas," Nature, Nature, vol. 458(7238), pages 632-635, April.
    • Handle: RePEc:nat:nature:v:458:y:2009:i:7238:d:10.1038_nature07832
    DOI: 10.1038/nature07832
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    Citations

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    Cited by:

    1. Juan Linde-Domingo & Bernhard Spitzer, 2024. "Geometry of visuospatial working memory information in miniature gaze patterns," Nature Human Behaviour, Nature, vol. 8(2), pages 336-348, February.
    2. Zhiying Long & Yubao Wang & Xuanping Liu & Li Yao, 2019. "Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    3. Kay H Brodersen & Thomas M Schofield & Alexander P Leff & Cheng Soon Ong & Ekaterina I Lomakina & Joachim M Buhmann & Klaas E Stephan, 2011. "Generative Embedding for Model-Based Classification of fMRI Data," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-19, June.
    4. Nadine Dijkstra & Stephen M. Fleming, 2023. "Subjective signal strength distinguishes reality from imagination," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Serra E. Favila & Brice A. Kuhl & Jonathan Winawer, 2022. "Perception and memory have distinct spatial tuning properties in human visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    6. Kai Ueltzhöffer & Diana J N Armbruster-Genç & Christian J Fiebach, 2015. "Stochastic Dynamics Underlying Cognitive Stability and Flexibility," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-46, June.
    7. Duncan Edward Astle & Anna Christina Nobre & Gaia Scerif, 2009. "Applying an Attentional Set to Perceived and Remembered Features," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-12, October.
    8. Mohammad Zia Ul Haq Katshu & Giovanni d'Avossa, 2014. "Fine-Grained, Local Maps and Coarse, Global Representations Support Human Spatial Working Memory," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-13, September.

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