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Exploring the spatio-temporal neural basis of face learning
Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To st...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461867/ https://www.ncbi.nlm.nih.gov/pubmed/28570739 http://dx.doi.org/10.1167/17.6.1 |
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author | Yang, Ying Xu, Yang Jew, Carol A. Pyles, John A. Kass, Robert E. Tarr, Michael J. |
author_facet | Yang, Ying Xu, Yang Jew, Carol A. Pyles, John A. Kass, Robert E. Tarr, Michael J. |
author_sort | Yang, Ying |
collection | PubMed |
description | Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. |
format | Online Article Text |
id | pubmed-5461867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-54618672017-06-08 Exploring the spatio-temporal neural basis of face learning Yang, Ying Xu, Yang Jew, Carol A. Pyles, John A. Kass, Robert E. Tarr, Michael J. J Vis Article Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. The Association for Research in Vision and Ophthalmology 2017-06-01 /pmc/articles/PMC5461867/ /pubmed/28570739 http://dx.doi.org/10.1167/17.6.1 Text en Copyright 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Article Yang, Ying Xu, Yang Jew, Carol A. Pyles, John A. Kass, Robert E. Tarr, Michael J. Exploring the spatio-temporal neural basis of face learning |
title | Exploring the spatio-temporal neural basis of face learning |
title_full | Exploring the spatio-temporal neural basis of face learning |
title_fullStr | Exploring the spatio-temporal neural basis of face learning |
title_full_unstemmed | Exploring the spatio-temporal neural basis of face learning |
title_short | Exploring the spatio-temporal neural basis of face learning |
title_sort | exploring the spatio-temporal neural basis of face learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461867/ https://www.ncbi.nlm.nih.gov/pubmed/28570739 http://dx.doi.org/10.1167/17.6.1 |
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