Dance is a universal and ancient human behavior; however, our understanding of the basis of this behavior is surprisingly weak. In this dissertation, I explore the cognitive and evolutionary foundations of human dance, providing evidence of two ways in which the production and perception of dance actions are rooted in the functions of more general cognitive systems.In doing so, I aim to both inform our understanding of dance, and use the study of dance to elucidate broader issues in cognition. Chapter 1 demonstrates that the ability to entrain, or move in time with an auditory beat, is not unique to humans. In addition, across hundreds of species, I find that all animals able to entrain can also vocally imitate sound. This supports the hypothesis that entrainment relies on cognitive machinery that originally evolved to support vocal imitation. Chapter 2 investigates the perception of dance-like actions. Previous work shows that we infer the goals of observed actions by calculating their efficiency as a means to external effects, like reaching an object or location. However, dance actions typically lack an external effect or external goal. In two experiments, I show that for dance-like actions, adults infer that the agents’ goal is simply to produce the movements themselves. Furthermore, this inference is driven by the actions’ inefficiency as a means to external goals. This inefficiency effectively rules out external goals, making movement-based goals the best explanation. Thus, perception of both dance and non-dance actions appears to rely the same type of efficiency-based goal inference. Chapter 3 builds on these findings, showing that the inference that the movements are the goal is closely related to our concept of dance. First, I find that participants view movement-based goals as more consistent with dance than with other activities. Second, I find that simply construing actions as having movement-based goals leads participants to view the actions as more dancelike, even when all participants have seen the exact same actions. Thus, even our categorization of actions as dance versus non-dance is rooted in the same cognitive processes as support our understanding of other intentional actions. / Psychology

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Gr¨unwald & Dawid, 2003). We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.