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The importance of hierarchically structured representations for tractableplanning has long been acknowledged. However, the questions of how peoplediscover such abstractions and how to define a set of optimal abstractionsremain open. This problem has been explored in cognitive science in the problemsolving literature and in computer science in hierarchical reinforcementlearning. Here, we emphasize an algorithmic perspective on learninghierarchical representations in which the objective is to efficiently encodethe structure of the problem, or, equivalently, to learn an algorithm withminimal length. We introduce a novel problem-solving paradigm that linksproblem solving and program induction under the Markov Decision Process (MDP)framework. Using this task, we target the question of whether humans discoverhierarchical solutions by maximizing efficiency in number of actions theygenerate or by minimizing the complexity of the resulting representation andfind evidence for the primacy of representational efficiency.

Representational efficiency outweighs action efficiency in human program induction