No Evidence of Transfer of Learning Between Problem-Solving Tasks Using Different Transformation Rules.

As demand for problem-solving skills continues to grow, so does the need for efficient training methods. A potential method of achieving efficient training of problem-solving skills is to design a training programme that maximises transfer of learning between problem domains. However, current models of problem-solving make contrasting predictions about the conditions under which cross-domain transfer is possible. While pattern-recognition models using state-action associations allow only limited transfer to tasks with different actions, heuristic search-based models using state-value estimates can reuse learning across tasks, potentially achieving greater transfer. In this study, we consider the case of transformation problem-solving tasks where each task uses a distinct and non-overlapping set of transformation rules. Participants trained using tasks from one taskset over four consecutive days and completed pre- and post-training probes on the untrained set. Training improved participants' solution rate, efficiency, and decision speed, but there were no reliable improvements for the untrained tasks beyond what could be explained by direct practice during the probe blocks. There were no consistent trends in participants' self-reported strategies across sessions, but use of a strategy involving explicit deliberation over which actions to take was consistently associated with better decisions. We note that the absence of transfer contrasts with the predictions of the heuristic search account of problem-solving in which a learned rule-independent state-value heuristic is reused across tasksets to decide actions. We discuss potential reasons for this absence of transfer and its possible implications for future models of problem-solving and for the training of problem-solving skills.