A Bayesian perspective on magnitude estimation

A Bayesian perspective on magnitude estimation

Highlights • A generic Bayesian framework explains regression, range, and sequential effects. • Weber-Fechner law and Stevens’ power law can be linked via Bayes’ Rule. • Stevens’ power law exponent can be reinterpreted as weighting of prior knowledge. • We link Bayesian explanations of magnitude est...

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Veröffentlicht in:Trends in cognitive sciences 2015-05, Vol.19 (5), p.285-293
Hauptverfasser: Petzschner, Frederike H, Glasauer, Stefan, Stephan, Klaas E
Format: Artikel
Sprache:eng
Schlagworte:
Bayes Theorem
Brain - physiology
generative model
History, 19th Century
Humans
Judgment - physiology
Neuroimaging
Neurology
Perception - physiology
perceptual inference
Psychiatry
psychophysics
Psychophysics - history
Regression (Psychology)
schizophrenia
Stevens’ power law
Weber-Fechner law
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Beschreibung
Zusammenfassung:Highlights • A generic Bayesian framework explains regression, range, and sequential effects. • Weber-Fechner law and Stevens’ power law can be linked via Bayes’ Rule. • Stevens’ power law exponent can be reinterpreted as weighting of prior knowledge. • We link Bayesian explanations of magnitude estimation to theories of schizophrenia.
ISSN:1364-6613
1879-307X
DOI:10.1016/j.tics.2015.03.002