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Informationen zum Autor Wei Ji Ma, Konrad Paul Kording, and Daniel Goldreich Klappentext "An introduction to constructing and reasoning with Bayesian (probabilistic) models of perceptual decision making and action, modeling how the brain perceives and decides"-- Zusammenfassung An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics Written by leaders in the field of computational approaches to mind and brain Inhaltsverzeichnis Acknowledgments xv The Four Steps of Bayesian Modeling xvii List of Acronyms xix Introduction 1 1 Uncertainty and Inference 7 2 Using Bayes' Rule 31 3 Bayesian Inference under Measurement Noise 53 4 The Response Distribution 83 5 Cue Combination and Evidence Accumulation 105 6 Learning as Inference 125 7 Discrimination and Detection 147 8 Binary Classification 169 9 Top-Level Nuisance Variables and Ambiguity 191 10 Same-Different Judgment 205 11 Search 227 12 Inference in a Changing World 245 13 Combining Inference with Utility 257 14 The Neural Likelihood Function 281 15 Bayesian Models in Context 301 Appendices 311 A Notation 313 B Basics of Probability Theory 315 C Model Fitting and Model Comparison 343 Bibliography 361 Index 371...
