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
About the Author
Wei Ji Ma 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. Konrad Paul Kording is Professor of Bioengineering and Neuroscience at the University of Pennsylvania, cofounder of Neuromatch, and codirector of the CIFAR Program in Learning in Machines & Brains. Daniel Goldreich is Associate Professor of Psychology, Neuroscience, and Behaviour at McMaster University and director of the undergraduate Honours Neuroscience Program.