Bayesian Inference and the Urn-Ball Task

Antonio Kolossa ()
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Antonio Kolossa: Technische Universität Braunschweig, Institut für Nachrichtentechnik

Chapter Chapter 4 in Computational Modeling of Neural Activities for Statistical Inference, 2016, pp 71-110 from Springer

Abstract: Abstract This chapter introduces a Bayesian observer model and an urn-ball task which is tailored to fit Bayes’ theorem and equips the subjects with prior knowledge about the distributions over the random variables contained in the task. The Bayesian observer model adjusts internal beliefs about hidden states in the environment and predictions about observable events. The scope of the analyzed data is extended to the complete late positive complex (P3a, P3b, Slow Wave) and the N250. It starts with a brief overview on the Bayesian observer model and the urn-ball task and their relation to the Bayesian brain hypothesis.

Keywords: Probability Weighting; Hide State; Probability Weighting Function; Posterior Model Probability; Late Positive Complex (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-32285-8_4

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DOI: 10.1007/978-3-319-32285-8_4

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