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408 pages, 7 x 10 inch, 128 color illustrations |
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.
A free e-book should be available soon from the MIT Press website.
Older versions (use at your own risk):
An electronic document containing full or partial solutions to selected problems will be made available here by December 2023.
You can enter comments and corrections here. They will be taken into account in preparing the second edition of the book. In addition, errors will be listed in an Errata document, available here by the end of October 2023. Thank you in advance!
From the book:
Dedication
We dedicate this book to the memory of David Knill (1961-2014). All three of us have learned a good part of what we know about Bayesian modeling of perception and action from him. As a caring and patient mentor and as an excellent teacher, he also made studying this topic a lot more enjoyable for all of us. The field of Bayesian modeling of perception and action would not be where it is without him and this book would probably never have been written.
Acknowledgments
This book has been a long time in the making, and we are indebted to many people. We first came up with the idea in June 2009, when -- together with Alan Stocker and Jonathan Pillow -- we taught a computational neuroscience course at the Instituto Gulbenkian de Ciência in Oeiras, Portugal. At the time, in an impressive display of unbridled optimism, K.K. predicted that we would be done by December 2009. A short 13 years later, we have the book in hand. The delay has come with benefits, though: over the years, we have used chapter drafts and the book's ideas to teach Bayesian modeling to hundreds of undergraduate students, graduate students, and postdocs in our courses at McMaster University, Baylor College of Medicine, Northwestern University, New York University, the University of Pennsylvania, and in tutorials at conferences and summer schools. Many of these students, as well as our teaching assistants -- notably Ronald van den Berg, Anna Kutschireiter, Lucy Lai, Jennifer Laura Lee, Julie Lee, Jorge Menendez, Sashank Pisupati, Anne-Lene Sax, Shan Shen, Bei Xiao, and Hörmet Yiltiz -- and lab members (too many to list) contributed numerous corrections, comments, problem solutions, and problem suggestions. We thank Nuwan de Silva for test-doing all problems in an earlier version of the book. We thank readers of various drafts, in particular Luigi Acerbi, Robert Jacobs, Michael Landy, Zili Liu, and Javier Traver, for providing deep and useful feedback on the content and exposition; we also thank Robert and Zili for being two of our most steadfast supporters over the years. We are deeply grateful to Brennan Klein, a postdoc at Northeastern University, for professionalizing and redesigning our figures throughout the book, as well as for many fun drawings and for making us more dutiful as authors. This book would never have been finished without the help of 1,3,7-trimethylxanthine and we are thankful for its existence. We are grateful to Robert Prior from MIT Press, by name destined to be our editor, who repeatedly set us firm deadlines, which he patiently consented to extend when we invariably missed them, and who made a free online version possible. Finally, we would like to thank our families, who have been unreasonably supportive all these years.
In addition, we are grateful to all commenters here and here.