Predicting the time course of individual objects with MEG

Alex Clarke*, Barry J. Devereux, Billi Randall, Lorraine K. Tyler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)
176 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)3602-3612
JournalCerebral Cortex
Issue number10
Early online date09 Sep 2014
Publication statusPublished - Oct 2015
Externally publishedYes


  • Classification
  • HMax
  • Model fit
  • Object recognition
  • Semantics

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Computer Vision and Pattern Recognition
  • Sensory Systems

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