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Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they appear 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 behavioural 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 near maximal reduction in the cost of computing the value 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 favour particular sequences of actions to achieve subgoals, creating novel complex actions or 'options’.


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