Predicting the Time Course of Individual Objects with MEG

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...

Full description

Bibliographic Details
Main Authors: Clarke, Alex, Devereux, Barry J., Randall, Billi, Tyler, Lorraine K.
Format: Online
Language:English
Published: Oxford University Press 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269546/
id pubmed-4269546
recordtype oai_dc
spelling pubmed-42695462015-09-29 Predicting the Time Course of Individual Objects with MEG Clarke, Alex Devereux, Barry J. Randall, Billi Tyler, Lorraine K. 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
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Clarke, Alex
Devereux, Barry J.
Randall, Billi
Tyler, Lorraine K.
spellingShingle Clarke, Alex
Devereux, Barry J.
Randall, Billi
Tyler, Lorraine K.
Predicting the Time Course of Individual Objects with MEG
author_facet Clarke, Alex
Devereux, Barry J.
Randall, Billi
Tyler, Lorraine K.
author_sort Clarke, Alex
title Predicting the Time Course of Individual Objects with MEG
title_short 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_sort predicting the time course of individual objects with meg
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.
publisher Oxford University Press
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269546/
_version_ 1613168495122448384

Similar Items