Bayesian Models of Perception and Action: An Introduction [Hardcover]

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

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 als8