Modeling human communicative dynamics brings exciting new problems and challenges to the NIPS community. The first goal of this workshop is to raise awareness in the machine learning community of these problems, including some applications needs, the special properties of these input streams, and the modeling challenges. The second goal is to exchange information about methods, techniques, and algorithms suitable for modeling human communication dynamics.

Face-to-face communication is a highly interactive process in which the participants mutually exchange and interpret verbal and nonverbal messages. Both the interpersonal dynamics and the dynamic interactions among an individual\'s perceptual, cognitive, and motor processes are swift and complex. How people accomplish these feats of coordination is a question of great scientific interest. Models of human communication dynamics also have much potential practical value, for applications including the understanding of communications problems such as autism and the creation of socially intelligent robots able to recognize, predict, and analyze verbal and nonverbal behaviors in real-time interaction with humans.