The Size-Weight Illusion is not anti-Bayesian after all: A unifying Bayesian account

Megan A K Peters, Wei Ji Ma, Ladan Shams

Research output: Contribution to journalArticle

Abstract

When we lift two differently-sized but equally-weighted objects, we expect the larger to be heavier, but the smaller feels heavier. However, traditional Bayesian approaches with "larger is heavier" priors predict the smaller object should feel lighter; this Size-Weight Illusion (SWI) has thus been labeled "anti-Bayesian" and has stymied psychologists for generations. We propose that previous Bayesian approaches neglect the brain's inference process about density. In our Bayesian model, objects' perceived heaviness relationship is based on both their size and inferred density relationship: Observers evaluate competing, categorical hypotheses about objects' relative densities, the inference about which is then used to produce the final estimate of weight. The model can qualitatively and quantitatively reproduce the SWI and explain other researchers' findings, and also makes a novel prediction, which we confirmed. This same computational mechanism accounts for other multisensory phenomena and illusions; that the SWI follows the same process suggests that competitive-prior Bayesian inference can explain human perception across many domains.

Original languageEnglish (US)
Article numbere2124
JournalPeerJ
Volume2016
Issue number6
DOIs
StatePublished - 2016

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Weights and Measures
Bayes Theorem
Brain
Specific Gravity
researchers
Research Personnel
Psychology
brain
prediction

Keywords

  • Bayesian inference
  • Heaviness perception
  • Hierarchical causal inference
  • Size-Weight Illusion

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)
  • Neuroscience(all)

Cite this

The Size-Weight Illusion is not anti-Bayesian after all : A unifying Bayesian account. / Peters, Megan A K; Ma, Wei Ji; Shams, Ladan.

In: PeerJ, Vol. 2016, No. 6, e2124, 2016.

Research output: Contribution to journalArticle

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