Ensemble representations reveal distinct neural coding of visual working memory

Byung-Il Oh, Yee-Joon Kim and Min-Suk Kang ()
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Byung-Il Oh: Sungkyunkwan University
Yee-Joon Kim: Institute for Basic Science (IBS)
Min-Suk Kang: Sungkyunkwan University

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract We characterized the population-level neural coding of ensemble representations in visual working memory from human electroencephalography. Ensemble representations provide a unique opportunity to investigate structured representations of working memory because the visual system encodes high-order summary statistics as well as noisy sensory inputs in a hierarchical manner. Here, we consistently observe stable coding of simple features as well as the ensemble mean in frontocentral electrodes, which even correlated with behavioral indices of the ensemble across individuals. In occipitoparietal electrodes, however, we find that remembered features are dynamically coded over time, whereas neural coding of the ensemble mean is absent in the old/new judgment task. In contrast, both dynamic and stable coding are found in the continuous estimation task. Our findings suggest that the prefrontal cortex holds behaviorally relevant abstract representations while visual representations in posterior and visual areas are modulated by the task demands.

Date: 2019
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DOI: 10.1038/s41467-019-13592-6

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