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Sequential Bayesian updating as a model for human perception

  • Sequential Bayesian updating has been proposed as model for explaining various systematic biases in human perception, such as the central tendency, range effects, and serial dependence. The present chapter introduces to the principal ideas behind Bayesian updating for the random-change model introduced previously and shows how to implement sequential updating using the exact method via probability distributions, the Kalman filter for Gaussian distributions, and a particle filter for approximate sequential updating. Finally, it is demonstrated how to couple perception to action by selecting an appropriate action based on the posterior distribution that results from sequential updating.

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Metadaten
Author: Stefan GlasauerORCiD
DOI:https://doi.org/10.1016/bs.pbr.2019.04.025
ISSN:1875-7855
ISSN:0079-6123
Title of the source (English):Progress in Brain Research
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2019
Contributing Corporation:BTU Cottbus-Senftenberg
Volume/Year:249
First Page:3
Last Page:18
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Computational Neuroscience
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