Header

UZH-Logo

Maintenance Infos

A Bayesian perspective on magnitude estimation


Petzschner, Frederike H; Glasauer, Stefan; Stephan, Klaas E (2015). A Bayesian perspective on magnitude estimation. Trends in Cognitive Sciences, 19(5):285-293.

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.

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.

Statistics

Citations

Dimensions.ai Metrics
31 citations in Web of Science®
36 citations in Scopus®
56 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

0 downloads since deposited on 19 Nov 2015
0 downloads since 12 months

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Uncontrolled Keywords:Stevens’ power law, Weber-Fechner law, generative model, perceptual inference, psychophysics, schizophrenia
Language:English
Date:2015
Deposited On:19 Nov 2015 09:30
Last Modified:14 Feb 2018 09:43
Publisher:Elsevier
ISSN:1364-6613
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.tics.2015.03.002
PubMed ID:25843543

Download