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Cited by 399

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Parietal and Frontal Cortex Encode Stimulus-Specific Mnemonic Representations during Visual Working Memory

Open access
Ester, Edward F., Sprague, Thomas C., Serences, John T.·2015·Neuron

Abstract

Summary Working memory (WM) enables the storage and manipulation of information in an active state. WM storage has long been associated with sustained increases in activation across a network of frontal and parietal cortical regions. However, recent evidence suggests that these regions primarily encode information related to general task goals rather than feature-selective representations of specific memoranda. These goal-related representations are thought to provide top-down feedback that coordinates the representation of fine-grained details in early sensory areas. Here, we test this model using fMRI-based reconstructions of remembered visual details from region-level activation patterns. We could reconstruct high-fidelity representations of a remembered orientation based on activation patterns in occipital visual cortex and in several sub-regions of frontal and parietal cortex, independent of sustained increases in mean activation. These results challenge models of WM that postulate disjoint frontoparietal “top-down control” and posterior sensory “feature storage” networks.

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Crossref · 10.1016/j.neuron.2015.07.013

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Metadata

Authors
Ester, E. F., Sprague, T. C., & Serences, J. T.
Venue
Neuron
Publisher
Elsevier BV
Published
2015-08
Volume
87
Issue
4
Pages
893-905
ISSN
0896-6273 (ISSN-L)
OpenAlex
W1509412094
Cited by
399
References
15
FWCI
19.54
Type
journal article
Language
en
Open access
bronze

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Ester, E. F., Sprague, T. C., & Serences, J. T. (2015). Parietal and Frontal Cortex Encode Stimulus-Specific Mnemonic Representations during Visual Working Memory. Neuron. https://doi.org/10.1016/j.neuron.2015.07.013

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.