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...

Descripción completa

Detalles Bibliográficos
Autores principales: Clarke, Alex, Devereux, Barry J., Randall, Billi, Tyler, Lorraine K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
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
_version_ 1782349372884779008
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
work_keys_str_mv AT clarkealex predictingthetimecourseofindividualobjectswithmeg
AT devereuxbarryj predictingthetimecourseofindividualobjectswithmeg
AT randallbilli predictingthetimecourseofindividualobjectswithmeg
AT tylerlorrainek predictingthetimecourseofindividualobjectswithmeg