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 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
Table of Contents
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
