The definitive introduction to Bayesian cognitive science, written by pioneers of the field. How 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.
- The only textbook comprehensively introducing the Bayesian approach to cognition
- Written by pioneers in the field
- Offers cutting-edge coverage of Bayesian cognitive science's research frontiers
- Suitable for advanced undergraduate and graduate students and researchers across the sciences with an interest in the mind, brain, and intelligence
- Features short tutorials and case studies of specific Bayesian models
《貝葉斯認知科學的權威入門，作者為該領域的先驅。》
人類智慧如何運作，從工程的角度來看？我們的思維如何能從少量的信息中獲取如此多的內容？貝葉斯認知模型提供了一個強大的框架來回答這些問題，通過逆向工程來理解心智。本教科書提供了貝葉斯認知科學的權威介紹，並對心智運作的統一理論視角進行探討。第一部分介紹了關鍵的數學概念，並通過心理學文獻中的例子進行說明，包括特定模型的詳細推導和可用於深入了解基本原則的參考資料。第二部分詳細介紹了更高級的主題及其應用，然後探討了對逆向工程方法的批評。這本全面的教材由新研究前沿的專家撰寫，將認知科學和人工智慧的領域重新結合，並為理解人類智慧建立了堅實的數學和計算基礎。
- 唯一全面介紹貝葉斯認知方法的教科書
- 由該領域的先驅撰寫
- 提供貝葉斯認知科學研究前沿的前沿內容
- 適合對心智、大腦和智慧感興趣的高年級本科生、研究生及各科學領域的研究人員
- 包含針對特定貝葉斯模型的短期教程和案例研究
Thomas L. Griffiths is Henry R. Luce Professor of Information Technology, Consciousness and Culture in the Departments of Psychology and Computer Science at Princeton University and coauthor of Algorithms to Live By: The Computer Science of Human Decisions. 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.
Nick Chater is Professor of Behavioural Science at Warwick Business School and author of The Mind Is Flat: The Remarkable Shallowness of the Improvising Brain, 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.
Joshua B. Tenenbaum 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.
Thomas L. Griffiths 是普林斯頓大學心理學與計算機科學系的亨利·R·盧斯信息技術、意識與文化教授，也是《Algorithms to Live By: The Computer Science of Human Decisions》的共同作者。他的研究獲得了美國心理學會和國家科學院等多個組織的獎項，探討人類與機器學習之間的聯繫，利用統計學和人工智能的概念來理解人們如何解決日常生活中遇到的挑戰性計算問題。
Nick Chater 是華威商學院的行為科學教授，也是《The Mind Is Flat: The Remarkable Shallowness of the Improvising Brain》等多本書籍的作者。他研究理性和語言的認知與社會基礎，並因心理學研究獲得四項國家獎項，於2023年獲得認知科學學會的David E. Rumelhart獎，以表彰他對認知基礎的貢獻。
Joshua B. Tenenbaum 是麻省理工學院腦與認知科學系的計算認知科學教授。他因在數學和認知心理學方面的研究獲得美國心理學會、國家科學院和實驗心理學家協會的獎項，並且是麥克阿瑟獎學金得主。
