Bayesian accounts of covert selective attention: a tutorial review

Benjamin T. Vincent (Lead / Corresponding author)

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)
    342 Downloads (Pure)


    Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. Graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

    Original languageEnglish
    Pages (from-to)1013-1032
    Number of pages20
    JournalAttention, Perception, and Psychophysics
    Issue number4
    Early online date27 Feb 2015
    Publication statusPublished - May 2015


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