Bayesian Models of Perception and Action
An Introduction
Published on 8. August 2023
Book
Hardback
360 pages
978-0-262-04759-3 (ISBN)
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.
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
More details
Language
English
Place of publication
Cambridge (Massachusetts)
United States
Publishing group
MIT Press Ltd
Illustrations
128 colour illustrations
Dimensions
Height: 261 mm
Width: 209 mm
Thickness: 29 mm
Weight
1028 gr
ISBN-13
978-0-262-04759-3 (9780262047593)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Wei Ji Ma | Konrad Paul Kording | Daniel Goldreich
Bayesian Models of Perception and Action
An Introduction
E-Book
08/2023
MIT Press
€63.49
Available for download
Persons
Wei Ji Ma, Konrad Paul Kording, and Daniel Goldreich
Content
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
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