Learning by imitation, by reinforcement and by verbal rules in problem solving

Learning by imitation is a powerful process for acquiring new knowledge, but there has been little research exploring imitation’s potential service to 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 favoured 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) (Byrne, Russon, 1998). 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 learning (a group that was presented specific instructions to solve the problem). On a task consisting in finding, with 3 uses of a scale, the one ball, which is either heavier or lighter in the 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.