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.
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Long-term cross-modal predictions may include a common prior for different forms of physical magnitude potentially located in posterior parietal cortex, and integration of predicted and actual interoceptive signals in insular cortex.
“Other proposals of long-term cross-modal predictions include a common prior for different forms of physical magnitude (e.g., distance, rotation angle, time) [40,41], that is potentially located in posterior parietal cortex [42], and integration of predicted and actual interoceptive signals in insular cortex [43,44].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [41]
A large body of psychophysical experiments indicates that basic perceptual judgements and multi-sensory integration show clear evidence for the operation of Bayesian inference.
“Third, a large body of psychophysical experiments indicate that basic perceptual judgements and multi-sensory integration show clear evidence for the operation of Bayesian inference (for overviews, Knill and Richards, 1996; Geisler and Kersten, 2002; Petzschner et al., 2015).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Petzschner et al., 2015
Perceptual aberrations, such as a reduced tendency to illusions, take a similar form in ASD and schizophrenia, suggesting that similar impairments in Bayesian inference may be present in both disorders.
“Perceptual aberrations, e.g., a reduced tendency to illusions (75), take a similar form in both disorders, suggesting that similar impairments in Bayesian inference may be present in both disorders.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [75]
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References 123 · Cited by 327
- Authors
- Petzschner, F. H., Glasauer, S., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- Bernstein Center for Computational Neuroscience Munich (DE)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Petzschner, Frederike H.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 19
- Issue
- 5
- Pages
- 285-293
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2074299699
- Cited by
- 327
- References
- 123
- Citations by year
- 2026: 27 · 2025: 29 · 2024: 34 · 2023: 28 · 2022: 35 · 2021: 36 · 2020: 42 · 2019: 29 · 2018: 25 · 2017: 22 · 2016: 17 · 2015: 2
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Wellcome Trust (091593)
- Universität Zürich
- René und Susanne Braginsky Stiftung
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Keywords
- magnitude estimation, Bayesian model, sensory perception, cross-modal similarity, psychophysics, schizophrenia, veridical perception
- MeSH terms
- Bayes Theorem, Brain, Humans, Judgment, Perception, Psychophysics, Regression, Psychology, History, 19th Century, Neuroimaging
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