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Priority-based transformations of stimulus representation in visual working memory
How does the brain prioritize among the contents of working memory (WM) to appropriately guide behavior? Previous work, employing inverted encoding modeling (IEM) of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) datasets, has shown that unprioritized memory items (UMI...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197029/ https://www.ncbi.nlm.nih.gov/pubmed/35653404 http://dx.doi.org/10.1371/journal.pcbi.1009062 |
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author | Wan, Quan Menendez, Jorge A. Postle, Bradley R. |
author_facet | Wan, Quan Menendez, Jorge A. Postle, Bradley R. |
author_sort | Wan, Quan |
collection | PubMed |
description | How does the brain prioritize among the contents of working memory (WM) to appropriately guide behavior? Previous work, employing inverted encoding modeling (IEM) of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) datasets, has shown that unprioritized memory items (UMI) are actively represented in the brain, but in a “flipped”, or opposite, format compared to prioritized memory items (PMI). To acquire independent evidence for such a priority-based representational transformation, and to explore underlying mechanisms, we trained recurrent neural networks (RNNs) with a long short-term memory (LSTM) architecture to perform a 2-back WM task. Visualization of LSTM hidden layer activity using Principal Component Analysis (PCA) confirmed that stimulus representations undergo a representational transformation–consistent with a flip—while transitioning from the functional status of UMI to PMI. Demixed (d)PCA of the same data identified two representational trajectories, one each within a UMI subspace and a PMI subspace, both undergoing a reversal of stimulus coding axes. dPCA of data from an EEG dataset also provided evidence for priority-based transformations of the representational code, albeit with some differences. This type of transformation could allow for retention of unprioritized information in WM while preventing it from interfering with concurrent behavior. The results from this initial exploration suggest that the algorithmic details of how this transformation is carried out by RNNs, versus by the human brain, may differ. |
format | Online Article Text |
id | pubmed-9197029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91970292022-06-15 Priority-based transformations of stimulus representation in visual working memory Wan, Quan Menendez, Jorge A. Postle, Bradley R. PLoS Comput Biol Research Article How does the brain prioritize among the contents of working memory (WM) to appropriately guide behavior? Previous work, employing inverted encoding modeling (IEM) of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) datasets, has shown that unprioritized memory items (UMI) are actively represented in the brain, but in a “flipped”, or opposite, format compared to prioritized memory items (PMI). To acquire independent evidence for such a priority-based representational transformation, and to explore underlying mechanisms, we trained recurrent neural networks (RNNs) with a long short-term memory (LSTM) architecture to perform a 2-back WM task. Visualization of LSTM hidden layer activity using Principal Component Analysis (PCA) confirmed that stimulus representations undergo a representational transformation–consistent with a flip—while transitioning from the functional status of UMI to PMI. Demixed (d)PCA of the same data identified two representational trajectories, one each within a UMI subspace and a PMI subspace, both undergoing a reversal of stimulus coding axes. dPCA of data from an EEG dataset also provided evidence for priority-based transformations of the representational code, albeit with some differences. This type of transformation could allow for retention of unprioritized information in WM while preventing it from interfering with concurrent behavior. The results from this initial exploration suggest that the algorithmic details of how this transformation is carried out by RNNs, versus by the human brain, may differ. Public Library of Science 2022-06-02 /pmc/articles/PMC9197029/ /pubmed/35653404 http://dx.doi.org/10.1371/journal.pcbi.1009062 Text en © 2022 Wan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wan, Quan Menendez, Jorge A. Postle, Bradley R. Priority-based transformations of stimulus representation in visual working memory |
title | Priority-based transformations of stimulus representation in visual working memory |
title_full | Priority-based transformations of stimulus representation in visual working memory |
title_fullStr | Priority-based transformations of stimulus representation in visual working memory |
title_full_unstemmed | Priority-based transformations of stimulus representation in visual working memory |
title_short | Priority-based transformations of stimulus representation in visual working memory |
title_sort | priority-based transformations of stimulus representation in visual working memory |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197029/ https://www.ncbi.nlm.nih.gov/pubmed/35653404 http://dx.doi.org/10.1371/journal.pcbi.1009062 |
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