Interplay of approximate planning strategies

Significance Many problems, particularly sequential planning problems, are computationally very demanding. How humans combine strategies to approximate and simplify these problems is not understood. Using modelling to unpick performance in a planning task, we find that humans are able to exploit the structure of the task to subdivide it and reduce processing requirements nearly optimally. Subtasks are combined in a simple, greedy manner, however, and within subtasks there is evidence of inhibitory reflexes in response to losses. Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or “options.”