Predicting the Time Course of Individual Objects with MEG
To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269546/ https://www.ncbi.nlm.nih.gov/pubmed/25209607 http://dx.doi.org/10.1093/cercor/bhu203 |
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author | Clarke, Alex Devereux, Barry J. Randall, Billi Tyler, Lorraine K. |
author_facet | Clarke, Alex Devereux, Barry J. Randall, Billi Tyler, Lorraine K. |
author_sort | Clarke, Alex |
collection | PubMed |
description | To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model of object recognition which provides an integrated account of how visual and semantic information emerge over time; therefore, it remains unknown how and when semantic representations are evoked from visual inputs. Here, we test whether a model of individual objects—based on combining the HMax computational model of vision with semantic-feature information—can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms. These results provide important insights into the functional properties of visual processing across time. |
format | Online Article Text |
id | pubmed-4269546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-42695462015-09-29 Predicting the Time Course of Individual Objects with MEG Clarke, Alex Devereux, Barry J. Randall, Billi Tyler, Lorraine K. Cereb Cortex Articles To respond appropriately to objects, we must process visual inputs rapidly and assign them meaning. This involves highly dynamic, interactive neural processes through which information accumulates and cognitive operations are resolved across multiple time scales. However, there is currently no model of object recognition which provides an integrated account of how visual and semantic information emerge over time; therefore, it remains unknown how and when semantic representations are evoked from visual inputs. Here, we test whether a model of individual objects—based on combining the HMax computational model of vision with semantic-feature information—can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms. These results provide important insights into the functional properties of visual processing across time. Oxford University Press 2015-10 2014-09-09 /pmc/articles/PMC4269546/ /pubmed/25209607 http://dx.doi.org/10.1093/cercor/bhu203 Text en © The Author 2014. Published by Oxford University Press http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Articles Clarke, Alex Devereux, Barry J. Randall, Billi Tyler, Lorraine K. Predicting the Time Course of Individual Objects with MEG |
title | Predicting the Time Course of Individual Objects with MEG |
title_full | Predicting the Time Course of Individual Objects with MEG |
title_fullStr | Predicting the Time Course of Individual Objects with MEG |
title_full_unstemmed | Predicting the Time Course of Individual Objects with MEG |
title_short | Predicting the Time Course of Individual Objects with MEG |
title_sort | predicting the time course of individual objects with meg |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269546/ https://www.ncbi.nlm.nih.gov/pubmed/25209607 http://dx.doi.org/10.1093/cercor/bhu203 |
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