Detecting contingencies: an infomax approach.

The ability to detect social contingencies plays an important role in the social and emotional development of infants. Analyzing this problem from a computational perspective may provide important clues for understanding social development, as well as for the synthesis of social behavior in robots. In this paper, we show that the turn-taking behaviors observed in infants during contingency detection situations are tuned to optimally gather information as to whether a person is responsive to them. We show that simple reinforcement learning mechanisms can explain how infants acquire these efficient contingency detection schemas. The key is to use the reduction of uncertainty (information gain) as a reward signal. The result is an interesting form of learning in which the learner rewards itself for conducting actions that help reduce its own sense of uncertainty. This paper illustrates the possibilities of an emerging area of computer science and engineering that focuses on the computational understanding of human behavior and on its synthesis in robots. We believe that the theory of stochastic optimal control will play a key role providing a formal mathematical foundation for this newly emerging discipline.