{"product_id":"bayesian-models-of-perception-and-action-isbn-9780262047593","title":"Bayesian Models of Perception and Action","description":"\u003cb\u003eAn accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eMany 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, \u003ci\u003eBayesian Models of Perception and Action\u003c\/i\u003e is the first textbook to teach this widely used computational framework to beginners.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eIntroduces Bayesian models of perception and action, which are central to cognitive science and neuroscience\u003c\/li\u003e\n\u003cli\u003eBeginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts\u003c\/li\u003e\n\u003cli\u003eBroad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics\u003c\/li\u003e\n\u003cli\u003eWritten by leaders in the field of computational approaches to mind and brain\u003c\/li\u003e\n\u003c\/ul\u003eAcknowledgments xv\u003cbr\u003eThe Four Steps of Bayesian Modeling xvii\u003cbr\u003eList of Acronyms xix\u003cbr\u003e\u003cbr\u003eIntroduction 1\u003cbr\u003e1 Uncertainty and Inference 7\u003cbr\u003e2 Using Bayes' Rule 31\u003cbr\u003e3 Bayesian Inference under Measurement Noise 53\u003cbr\u003e4 The Response Distribution 83\u003cbr\u003e5 Cue Combination and Evidence Accumulation 105\u003cbr\u003e6 Learning as Inference 125\u003cbr\u003e7 Discrimination and Detection 147\u003cbr\u003e8 Binary Classification 169\u003cbr\u003e9 Top-Level Nuisance Variables and Ambiguity 191\u003cbr\u003e10 Same-Different Judgment 205\u003cbr\u003e11 Search 227\u003cbr\u003e12 Inference in a Changing World 245\u003cbr\u003e13 Combining Inference with Utility 257\u003cbr\u003e14 The Neural Likelihood Function 281\u003cbr\u003e15 Bayesian Models in Context 301\u003cbr\u003e\u003cbr\u003eAppendices 311\u003cbr\u003eA Notation 313\u003cbr\u003eB Basics of Probability Theory 315\u003cbr\u003eC Model Fitting and Model Comparison 343\u003cbr\u003e\u003cbr\u003eBibliography 361\u003cbr\u003eIndex 371\u003cb\u003eWei Ji Ma\u003c\/b\u003e is Professor of Neural Science and Psychology at New York University, founder of the Growing up in Science series, and a founding member of the Scientist Action and Advocacy Network. \u003cb\u003eKonrad Paul Kording\u003c\/b\u003e is Professor of Bioengineering and Neuroscience at the University of Pennsylvania, cofounder of Neuromatch, and codirector of the CIFAR Program in Learning in Machines \u0026amp; Brains.\u003cb\u003e Daniel Goldreich\u003c\/b\u003e is Associate Professor of Psychology, Neuroscience, and Behaviour at McMaster University and director of the undergraduate Honours Neuroscience Program.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46303728238821,"sku":"NP9780262047593","price":65.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262047593.jpg?v=1767722265","url":"https:\/\/k12savings.com\/products\/bayesian-models-of-perception-and-action-isbn-9780262047593","provider":"K12savings","version":"1.0","type":"link"}