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A Bayesian perspective on magnitude estimation.
Journal article

A Bayesian perspective on magnitude estimation.

  • Petzschner FH Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zürich & ETH Zürich, Switzerland. Electronic address: petzschner@biomed.ee.ethz.ch.
  • Glasauer S Center for Sensorimotor Research and Department of Neurology, Ludwig-Maximilian University Munich, Munich, Germany; German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilian University Munich, Munich, Germany; Bernstein Center for Computational Neuroscience, Ludwig-Maximilian University Munich, Munich, Germany.
  • Stephan KE Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zürich & ETH Zürich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
  • 2015-04-07
Published in:
  • Trends in cognitive sciences. - 2015
Stevens’ power law
Weber-Fechner law
generative model
perceptual inference
psychophysics
schizophrenia
Bayes Theorem
Brain
History, 19th Century
Humans
Judgment
Neuroimaging
Perception
Psychophysics
Regression, Psychology
English Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/127058
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