Decoding the time-course of object recognition in the human brain

from visual features to categorical decisions

Erika W. Contini*, Susan G. Wardle, Thomas A. Carlson

*Corresponding author for this work

Research output: Contribution to journalArticle

26 Citations (Scopus)


Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time-series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low-level visual features dominate decodable object representations early in the time-course, with more abstract representations related to object category emerging later. A key finding is that the time-course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low-level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time-course of object processing.

Original languageEnglish
Pages (from-to)165-176
Number of pages12
Early online date16 Feb 2017
Publication statusPublished - Oct 2017


  • MEG
  • EEG
  • MVPA
  • time-series decoding
  • object recognition
  • object categorisation

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