{"product_id":"bayesian-models-of-cognition-isbn-9780262049412","title":"Bayesian Models of Cognition","description":"\u003cb\u003eThe definitive introduction to Bayesian cognitive science, written by pioneers of the field.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eHow does human intelligence work, in engineering terms? How do our minds get so much from so little? Bayesian models of cognition provide a powerful framework for answering these questions by reverse-engineering the mind. This textbook offers an authoritative introduction to Bayesian cognitive science and a unifying theoretical perspective on how the mind works. Part I provides an introduction to the key mathematical ideas and illustrations with examples from the psychological literature, including detailed derivations of specific models and references that can be used to learn more about the underlying principles. Part II details more advanced topics and their applications before engaging with critiques of the reverse-engineering approach. Written by experts at the forefront of new research, this comprehensive text brings the fields of cognitive science and artificial intelligence back together and establishes a firmly grounded mathematical and computational foundation for the understanding of human intelligence. \u003cbr\u003e \u003cbr\u003e\u003cul\u003e\n\u003cli\u003eThe only textbook comprehensively introducing the Bayesian approach to cognition\u003c\/li\u003e\n\u003cli\u003eWritten by pioneers in the field\u003c\/li\u003e\n\u003cli\u003eOffers cutting-edge coverage of Bayesian cognitive science's research frontiers \u003c\/li\u003e\n\u003cli\u003eSuitable for advanced undergraduate and graduate students and researchers across the sciences with an interest in the mind, brain, and intelligence \u003c\/li\u003e\n\u003cli\u003eFeatures short tutorials and case studies of specific Bayesian models\u003c\/li\u003e\n\u003c\/ul\u003ePreface\u003cbr\u003ePart I: The Basics\u003cbr\u003e1 Introducing the Bayesian approach to cognitive science\u003cbr\u003e2 Probabilistic models of cognition in historical context\u003cbr\u003e3 Bayesian inference\u003cbr\u003e4 Graphical models\u003cbr\u003e5 Building complex generative models\u003cbr\u003e6 Approximate probabilistic inference\u003cbr\u003e7 From probabilities to actions\u003cbr\u003ePart II: Advanced Topics\u003cbr\u003e8 Learning inductive bias with hierarchical Bayesian models\u003cbr\u003e9 Capturing the growth of knowledge with nonparametric Bayesian models\u003cbr\u003e10 Estimating subjective probability distributions\u003cbr\u003e11 Sampling as a bridge across levels of analysis\u003cbr\u003e12 Bayesian models and neural networks\u003cbr\u003e13 Resource-rational analysis\u003cbr\u003e14 Theory of mind and inverse planning\u003cbr\u003e15 Intuitive physics as probabilistic inference\u003cbr\u003e16 Language processing and language learning\u003cbr\u003e17 Bayesian inference over logical representations\u003cbr\u003e18 Probabilistic programs as a unifying language of thought\u003cbr\u003e19 Learning as Bayesian inference over programs\u003cbr\u003e20 Bayesian models of cognitive development\u003cbr\u003e21 The limits of inference and algorithmic probability\u003cbr\u003e22 A Bayesian conversation\u003cbr\u003eConclusion\u003cbr\u003eAcknowledgments\u003cbr\u003eReferences\u003cb\u003eThomas L. Griffiths \u003c\/b\u003eis Henry R. Luce Professor of Information Technology, Consciousness and Culture in the Departments of Psychology and Computer Science at Princeton University and coauthor of \u003cu\u003eAlgorithms to Live By: The Computer Science of Human Decisions.\u003c\/u\u003e His research, which has received awards from the American Psychological Association and the National Academy of Sciences, among other organizations, explores connections between human and machine learning, using ideas from statistics and artificial intelligence to understand how people solve the challenging computational problems they encounter in everyday life. \u003cbr\u003e\u003cbr\u003e\u003cb\u003eNick Chater\u003c\/b\u003e is Professor of Behavioural Science at Warwick Business School and author \u003cu\u003eof The Mind Is Flat: The Remarkable Shallowness of the Improvising Brain,\u003c\/u\u003e among many other books. He studies the cognitive and social foundations of rationality and language and is the recipient of four national awards for psychological research and, in 2023, the Cognitive Science Society’s David E. Rumelhart Prize for contributions to the foundation of cognition.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eJoshua B. Tenenbaum\u003c\/b\u003e is Professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at MIT. He has received awards for research in mathematical and cognitive psychology from the American Psychological Association, the National Academy of Sciences, and the Society of Experimental Psychologists, and is a Macarthur Fellow.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46302005428453,"sku":"NP9780262049412","price":120.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262049412.jpg?v=1767722264","url":"https:\/\/k12savings.com\/products\/bayesian-models-of-cognition-isbn-9780262049412","provider":"K12savings","version":"1.0","type":"link"}