title: A Bayesian perspective on magnitude estimation.
abstract: 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.
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zürich & ETH Zürich, Switzerland. Electronic address: petzschner@biomed.ee.ethz.ch.
authors.forename: Frederike H
authors.initials: FH
authors.lastname: Petzschner
authors.affiliation: 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.
authors.forename: Stefan
authors.initials: S
authors.lastname: Glasauer
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zürich & ETH Zürich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
authors.forename: Klaas E
authors.initials: KE
authors.lastname: Stephan
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D049672:History, 19th Century; D006801:Humans; D007600:Judgment / Q000502:physiology*; D059906:Neuroimaging; D010465:Perception / Q000502:physiology*; D011601:Psychophysics / Q000266:history; D012043:Regression, Psychology
keywords: Stevens’ power law; Weber-Fechner law; generative model; perceptual inference; psychophysics; schizophrenia