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
