Bayesian Models of Perception and Action: An Introduction

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Description

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 neuroscienceBeginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex conceptsBroad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematicsWritten by leaders in the field of computational approaches to mind and brain

Additional information

Weight 1.004475 kg
Dimensions 2.9718 × 18.415 × 26.1874 cm
by

, ,

Format

Hardback

Language

Pages

408

Publisher

Year Published

2023-8-8

Imprint

Publication City/Country

USA

ISBN 10

0262047594

About The Author

Wei Ji Ma is Professor of Neural Science and Psychology at New York University, founder of the Growing up in Science series, and a founding member of the Scientist Action and Advocacy Network. Konrad Paul Kording is Professor of Bioengineering and Neuroscience at the University of Pennsylvania, cofounder of Neuromatch, and codirector of the CIFAR Program in Learning in Machines & Brains. Daniel Goldreich is Associate Professor of Psychology, Neuroscience, and Behaviour at McMaster University and director of the undergraduate Honours Neuroscience Program.

Table Of Content

Acknowledgments xvThe Four Steps of Bayesian Modeling xviiList of Acronyms xixIntroduction 11 Uncertainty and Inference 72 Using Bayes' Rule 313 Bayesian Inference under Measurement Noise 534 The Response Distribution 835 Cue Combination and Evidence Accumulation 1056 Learning as Inference 1257 Discrimination and Detection 1478 Binary Classification 1699 Top-Level Nuisance Variables and Ambiguity 19110 Same-Different Judgment 20511 Search 22712 Inference in a Changing World 24513 Combining Inference with Utility 25714 The Neural Likelihood Function 28115 Bayesian Models in Context 301Appendices 311A Notation 313B Basics of Probability Theory 315C Model Fitting and Model Comparison 343Bibliography 361Index 371

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