SoCS: Modeling Agency and Intentions in Dynamic Environments as a Precursor to Efficient Human-Computer Interaction

Yale University, New Haven CT

Investigators

Abstract

People recognize dramatic situations and attribute roles and intentions to perceived characters, even when presented with extremely simple cues. As any cartoon viewer can attest, two animated shapes are sufficient to describe a scene involving tender lovers, brutal bullies, tense confrontations and hair-raising escapes. These basic notions of agency and intentionality are foundational to our social perception of the world. They provide the first discriminations between agents and objects, delineate which elements of the world can move with goal-directed purpose, and provide the primitive structure for describing cause and effect. Extensive laboratory experiments have described many of the basic properties that produce these perceptions on controlled stimuli. However there have been only limited attempts to quantify these processes and no attempts to see if these same properties hold on real-world activity patterns. This project models our human ability to perceive agency, intentionality, and goal-directed behavior in dynamic real-world environments. Using off-the-shelf real-time localization systems, the movements of people and objects are recorded as they engage in unstructured activity and staged group games. Drawing on both this empirical data and theories drawn from the psychophysical data, computational models are constructed that quantify, explain, and predict real-world social and goal-directed behavior. The benefits of this work include: (1) modeling tools for use within behavioral studies, (2) a real-world grounding for psychophysical studies, and (3) a computational model of social and intentional behavior that would enhance human-computer and human-robot interfaces.