Perception as Evidence Accumulation and Bayesian Inference

Insights From Masked Priming

Dennis Norris*, Sachiko Kinoshita

*Corresponding author for this work

    Research output: Contribution to journalArticle

    135 Citations (Scopus)

    Abstract

    The authors argue that perception is Bayesian inference based on accumulation of noisy evidence and that, in masked priming, the perceptual system is tricked into treating the prime and the target as a single object. Of the 2 algorithms considered for formalizing how the evidence sampled from a prime and target is combined, only 1 was shown to be consistent with the existing data from the visual word recognition literature. This algorithm was incorporated into the Bayesian Reader model (D. Norris, 2006), and its predictions were confirmed in 3 experiments. The experiments showed that the pattern of masked priming is not a fixed function of the relations between the prime and the target but can be changed radically by changing the task from lexical decision to a same-different judgment. Implications of the Bayesian framework of masked priming for unconscious cognition and visual masking are discussed.

    Original languageEnglish
    Pages (from-to)434-455
    Number of pages22
    JournalJournal of Experimental Psychology: General
    Volume137
    Issue number3
    DOIs
    Publication statusPublished - Aug 2008

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