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 |
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| container_title | Trends in cognitive sciences |
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| creator | Petzschner, Frederike H Glasauer, Stefan Stephan, Klaas E |
| description | 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. |
| doi_str_mv | 10.1016/j.tics.2015.03.002 |
| format | Article |
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| ispartof | Trends in cognitive sciences, 2015-05, Vol.19 (5), p.285-293 |
| issn | 1364-6613 1879-307X |
| language | eng |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | 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 |
| title | A Bayesian perspective on magnitude estimation |
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