Discovery of hierarchical representations for efficient planning

Momchil S. Tomov, Samyukta Yagati, Agni Kumar, Wanqian Yang, Samuel J. Gershman

Abstract: Author summary Human planning is hierarchical. Whether planning something simple like cooking dinner or something complex like a trip abroad, we usually begin with a rough mental sketch of the goals we want to achieve (“go to Spain, then go back home”). This sketch is then progressively refined into a detailed sequence of sub-goals (“book flight”, “pack luggage”), sub-sub-goals, and so on, down to the actual sequence of bodily movements that is much more complicated than the original plan. Efficient planning therefore requires knowledge of the abstract high-level concepts that constitute the essence of hierarchical plans. Yet how humans learn such abstractions remains a mystery. In this study, we show that humans spontaneously form such high-level concepts in a way that allows them to plan efficiently given the tasks, rewards, and structure of their environment. We also show that this behavior is consistent with a formal model of the underlying computations, thus grounding these findings in established computational principles and relating them to previous studies of hierarchical planning. We believe our results pave the way for future studies to investigate the neural algorithms that support this essential cognitive ability.