A tutorial on Bayesian models of perception

Benjamin T. Vincent (Lead / Corresponding author)

    Research output: Contribution to journalArticle

    10 Citations (Scopus)
    388 Downloads (Pure)

    Abstract

    The notion that perception involves Bayesian inference is an increasingly popular position taken by many researchers. Bayesian models have provided insights into many perceptual phenomena, but their description and practical implementation does not always convey their theoretical appeal or conceptual elegance. This tutorial provides an introduction to core concepts in Bayesian modelling and should help a wide variety of readers to more deeply understand, or to generate their own Bayesian models of perception. Core theoretical and implementational issues are covered, using the 2 alternative-forced-choice task as a case study. Supplementary code is available to help bridge the gap between model description and practical implementation (see Appendix B).

    Original languageEnglish
    Pages (from-to)103-114
    Number of pages12
    JournalJournal of Mathematical Psychology
    Volume66
    Early online date13 Mar 2015
    DOIs
    Publication statusPublished - Jun 2015

    Keywords

    • Alternative forced choice
    • Bayesian inference
    • Bayesian network
    • Ideal observer
    • MCMC
    • Probabilistic generative model
    • Psychometric function

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