The development of gaze following as a Bayesian systems identification problem

Abstract

We propose a view of gaze following in which infants act as Bayesian learners actively attempting to identify the operating characteristics of the systems with which they interact. We present results of an experiment in which 28 infants (average age 10 months) interacted for a 3 minute period with a non-humanoid robot. For half the infants the robot simulated contingency structure typically produced by human beings. In particular it provided causal information about the existence of a line of regard. For the other 14 infants, the robot behaved in a manner which was not contingent with the environment. We found that a few minutes of interaction with the contingent robot was sufficient to elicit statistically detectable gaze following. There were clear signs that some of these infants were actively attempting to identify whether or not the robot was responsive to them. We propose that the infant brain is equipped to learn and analyze the contingency structure of real-time social interactions. Contingency is a fundamental perceptual dimension used by infants to recognize the operational properties of humans and to generalize existing behaviors to new social partners.