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.
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© 2016 Springer International Publishing Switzerland
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Kolossa, A. (2016). Bayesian Inference and the Urn-Ball Task. In: Computational Modeling of Neural Activities for Statistical Inference . Springer, Cham. https://doi.org/10.1007/978-3-319-32285-8_4
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DOI: https://doi.org/10.1007/978-3-319-32285-8_4
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32284-1
Online ISBN: 978-3-319-32285-8
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