Learning by Imitation, Reinforcement and Verbal Rules in Problem Solving Tasks

Learning by imitation is a powerful process for acquiring new knowledge, but there has been little research exploring imitation’s potential in the problem solving domain. Classical problem solving techniques tend to center around reinforcement learning, which requires significant trial-and-error learning to reach successful goals and problem solutions. Heuristics, hints, and reasoning by analogy have been favored as improvements over reinforcement learning, whereas imitation learning has been regarded as rote memorizing. However, research on imitation learning in animals and infants suggests that what is being learned is the overall arrangement of actions (sequencing and planning). Applied to problem solving, this suggests that imitation learning might enable a problem solver to infer a complex hierarchical problem representation from observation alone. We compared three types of learning in problem solving tasks: imitation learning (a group that viewed successful problem solving demonstrations), reinforcement learning (a group that got feedback indicating whether their answer was correct or not) and explicit rule learning (a group that was presented specific instructions to solve the problem). The task required participants to find, with three uses of a scale, the one ball which was either heavier or lighter than the rest of a set of 12 balls. We found that subjects in the imitation learning and explicit learning groups outperformed those in the reinforcement learning group. We conclude that learning by imitation in problem solving tasks is worthwhile, efficient and even superior to explicit learning because of the minimal time and energy investment required from the mentor.