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OverviewThis authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. Full Product DetailsAuthor: Antonio KolossaPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2016 Dimensions: Width: 15.50cm , Height: 1.00cm , Length: 23.50cm Weight: 3.554kg ISBN: 9783319322841ISBN 10: 3319322842 Pages: 127 Publication Date: 23 May 2016 Audience: College/higher education , Postgraduate, Research & Scholarly Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsBasic Principles of ERP Research, Surprise, and Probability Estimation.- Introduction to Model Estimation and Selection Methods.- A New Theory of Trial-by-Trial P300 Amplitude Fluctuations.- Bayesian Inference and the Urn-Ball Task.- Summary and Outlook.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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