title: Predicting the Time Course of Individual Objects with MEG.
abstract: 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.
authors.affiliation: Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK.
authors.forename: Alex
authors.initials: A
authors.lastname: Clarke
authors.affiliation: Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK.
authors.forename: Barry J
authors.initials: BJ
authors.lastname: Devereux
authors.affiliation: Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK.
authors.forename: Billi
authors.initials: B
authors.lastname: Randall
authors.affiliation: Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK.
authors.forename: Lorraine K
authors.initials: LK
authors.lastname: Tyler
mesh_terms: D000328:Adult; D002540:Cerebral Cortex / Q000502:physiology*; D003210:Concept Formation / Q000502:physiology; D005260:Female; D006801:Humans; D015225:Magnetoencephalography; D008297:Male; D008959:Models, Neurological; D010364:Pattern Recognition, Visual / Q000502:physiology*; D021641:Recognition, Psychology / Q000502:physiology*; D012044:Regression Analysis; D012660:Semantics; D055815:Young Adult
keywords: Classification; HMax; model fit; object recognition; semantics