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 expert 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, search-based models using state-value associations can reuse the same associations 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. We also observed a gradual reduction in the association between participants’ solution efficiency and their working memory capacity on the trained trials, suggesting a transition towards a problem-solving method less dependent upon working memory. We note that these findings contrast with the predictions of some search-based models of problem-solving expertise in which a rule-independent state-value function is reused across tasksets to decide actions. We discuss the implications of these results for future models of expert problem-solving and for the training of problem-solving skills.