Investigating a space-variant weighted salience account of visual selection

Benjamin T. Vincent, Tom Troscianko, Iain D. Gilchrist

    Research output: Contribution to journalArticlepeer-review

    21 Citations (Scopus)


    Weighted salience models are a popular framework for image-driven visual attentional processes. These models operate by: sampling the visual environment; calculating feature maps; combining them in a weighted sum and using this to determine where the eye will fixate next. We examine these stages in turn. We find that a biologically plausible non-uniform retinal sampling causes feature coding unreliability. The linear weighted sum operation seems an adequate model if statistical feature dimension dependencies are considered. Using signal detection theory we find good discrimination between targets and non-targets in the weighted sum, but the fixation criterion of ‘peak salience’ is suboptimal.
    Original languageEnglish
    Pages (from-to)1809-1820
    Number of pages12
    JournalVision Research
    Issue number13
    Publication statusPublished - 2007


    • Eye movements
    • Space variant
    • Bottom-up
    • Top-down
    • Weighted salience
    • Visual search


    Dive into the research topics of 'Investigating a space-variant weighted salience account of visual selection'. Together they form a unique fingerprint.

    Cite this