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Error-correcting dynamics in visual working memory
Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations. Such errors have been shown to accumulate over time and increase with the number of items simultaneously held in working memory....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662698/ https://www.ncbi.nlm.nih.gov/pubmed/31358740 http://dx.doi.org/10.1038/s41467-019-11298-3 |
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author | Panichello, Matthew F. DePasquale, Brian Pillow, Jonathan W. Buschman, Timothy J. |
author_facet | Panichello, Matthew F. DePasquale, Brian Pillow, Jonathan W. Buschman, Timothy J. |
author_sort | Panichello, Matthew F. |
collection | PubMed |
description | Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations. Such errors have been shown to accumulate over time and increase with the number of items simultaneously held in working memory. Here, we show that discrete attractor dynamics mitigate the impact of noise on working memory. These dynamics pull memories towards a few stable representations in mnemonic space, inducing a bias in memory representations but reducing the effect of random diffusion. Model-based and model-free analyses of human and monkey behavior show that discrete attractor dynamics account for the distribution, bias, and precision of working memory reports. Furthermore, attractor dynamics are adaptive. They increase in strength as noise increases with memory load and experiments in humans show these dynamics adapt to the statistics of the environment, such that memories drift towards contextually-predicted values. Together, our results suggest attractor dynamics mitigate errors in working memory by counteracting noise and integrating contextual information into memories. |
format | Online Article Text |
id | pubmed-6662698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66626982019-07-29 Error-correcting dynamics in visual working memory Panichello, Matthew F. DePasquale, Brian Pillow, Jonathan W. Buschman, Timothy J. Nat Commun Article Working memory is critical to cognition, decoupling behavior from the immediate world. Yet, it is imperfect; internal noise introduces errors into memory representations. Such errors have been shown to accumulate over time and increase with the number of items simultaneously held in working memory. Here, we show that discrete attractor dynamics mitigate the impact of noise on working memory. These dynamics pull memories towards a few stable representations in mnemonic space, inducing a bias in memory representations but reducing the effect of random diffusion. Model-based and model-free analyses of human and monkey behavior show that discrete attractor dynamics account for the distribution, bias, and precision of working memory reports. Furthermore, attractor dynamics are adaptive. They increase in strength as noise increases with memory load and experiments in humans show these dynamics adapt to the statistics of the environment, such that memories drift towards contextually-predicted values. Together, our results suggest attractor dynamics mitigate errors in working memory by counteracting noise and integrating contextual information into memories. Nature Publishing Group UK 2019-07-29 /pmc/articles/PMC6662698/ /pubmed/31358740 http://dx.doi.org/10.1038/s41467-019-11298-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Panichello, Matthew F. DePasquale, Brian Pillow, Jonathan W. Buschman, Timothy J. Error-correcting dynamics in visual working memory |
title | Error-correcting dynamics in visual working memory |
title_full | Error-correcting dynamics in visual working memory |
title_fullStr | Error-correcting dynamics in visual working memory |
title_full_unstemmed | Error-correcting dynamics in visual working memory |
title_short | Error-correcting dynamics in visual working memory |
title_sort | error-correcting dynamics in visual working memory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662698/ https://www.ncbi.nlm.nih.gov/pubmed/31358740 http://dx.doi.org/10.1038/s41467-019-11298-3 |
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