A Computational Model of Social-Learning Mechanisms

In this article we propose a computational model that describes how observed behavior can influence an observer's own behavior, including the acquisition of new task descriptions. The sources of influence on our model's behavior are: beliefs about the world's possible states and actions causing transitions between them; baseline preferences for certain actions; a variable tendency to infer and share goals in observed behavior; and a variable tendency to act efficiently to reach rewarding states. Acting on these premises, our model is able to replicate key empirical studies of social learning in children and chimpanzees. We demonstrate how a simple artificial system can account for a variety of biological social transfer phenomena, such as goal-inference and over-imitation, by taking into account action constraints and incomplete knowledge about the world dynamics.