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A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors
Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the abilit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292638/ https://www.ncbi.nlm.nih.gov/pubmed/34305517 http://dx.doi.org/10.3389/fnins.2021.676779 |
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author | Miles, Jesse T. Kidder, Kevan S. Wang, Ziheng Zhu, Yiru Gire, David H. Mizumori, Sheri J. Y. |
author_facet | Miles, Jesse T. Kidder, Kevan S. Wang, Ziheng Zhu, Yiru Gire, David H. Mizumori, Sheri J. Y. |
author_sort | Miles, Jesse T. |
collection | PubMed |
description | Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics does not identify many trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features does not improve classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification in binary decision tasks, and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus. |
format | Online Article Text |
id | pubmed-8292638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82926382021-07-22 A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors Miles, Jesse T. Kidder, Kevan S. Wang, Ziheng Zhu, Yiru Gire, David H. Mizumori, Sheri J. Y. Front Neurosci Neuroscience Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics does not identify many trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features does not improve classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification in binary decision tasks, and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus. Frontiers Media S.A. 2021-07-07 /pmc/articles/PMC8292638/ /pubmed/34305517 http://dx.doi.org/10.3389/fnins.2021.676779 Text en Copyright © 2021 Miles, Kidder, Wang, Zhu, Gire and Mizumori. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Miles, Jesse T. Kidder, Kevan S. Wang, Ziheng Zhu, Yiru Gire, David H. Mizumori, Sheri J. Y. A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors |
title | A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors |
title_full | A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors |
title_fullStr | A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors |
title_full_unstemmed | A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors |
title_short | A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors |
title_sort | machine learning approach for detecting vicarious trial and error behaviors |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292638/ https://www.ncbi.nlm.nih.gov/pubmed/34305517 http://dx.doi.org/10.3389/fnins.2021.676779 |
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