Online decoding of object-based attention using real-time fMRI

Adnan M. Niazi, Philip L.C. van den Broek, Stefan Klanke, Markus Barth, Mannes Poel, Peter Desain, Marcel A.J. van Gerven

    Research output: Contribution to journalArticleAcademicpeer-review

    9 Citations (Scopus)


    Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for moment-to-moment decoding of attention to spatially overlapped objects belonging to two different object categories. First, a whole-brain classifier was trained on pictures of faces and places. Subjects then saw transparently overlapped pictures of a face and a place, and attended to only one of them while ignoring the other. The category of the attended object, face or place, was decoded on a scan-by-scan basis using the previously trained decoder. The decoder performed at 77.6% accuracy indicating that despite competing bottom-up sensory input, object-based visual attention biased neural patterns towards that of the attended object. Furthermore, a comparison between different classification approaches indicated that the representation of faces and places is distributed rather than focal. This implies that real-time decoding of object-based attention requires a multivariate decoding approach that can detect these distributed patterns of cortical activity.
    Original languageUndefined
    Pages (from-to)319-329
    Number of pages11
    JournalEuropean journal of neuroscience
    Issue number2
    Publication statusPublished - Jan 2014


    • EWI-23933
    • multivariate decoding
    • object-based attention
    • Categorization
    • METIS-303970
    • IR-89543
    • real-time fMRI

    Cite this