Comprehension of goal-directed, intentional motion is an important but understudied visual function. To study it, we created a two-dimensional virtual environment populated by independently-programmed autonomous virtual agents, which navigate the environment, collecting food and competing with one another. Their behavior is modulated by a small number of distinct "mental states": exploring, gathering food, attacking, and fleeing. In two experiments, we studied subjects' ability to detect and classify the agents' continually changing mental states on the basis of their motions and interactions. Our analyses compared subjects' classifications to the ground truth state occupied by the observed agent's autonomous program. Although the true mental state is inherently hidden and must be inferred, subjects showed both high validity (correlation with ground truth) and high reliability (correlation with one another). The data provide intriguing evidence about the factors that influence estimates of mental state-a key step towards a true "psychophysics of intention."