According to simulation or shared-substrates models of emotion recognition, our ability to recognize the emotions expressed by other individuals relies, at least in part, on processes that internally simulate the same emotional state in ourselves. The term “emotional expressions” is nearly synonymous, in many people's minds, with facial expressions of emotion. However, vocal prosody and whole-body cues also convey emotional information. What is the relationship between these various channels of emotional communication? We first briefly review simulation models of emotion recognition, (...) and then discuss neuroscientific evidence related to these models, including studies using facial expressions, whole-body cues, and vocal prosody. We conclude by discussing these data in the context of simulation and shared-substrates models of emotion recognition. (shrink)

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would (...) be helpful though not essential as the book includes a self-contained introduction to basic probability theory. (shrink)