RI: SMALL: Prospective and retrospective mechanisms in complex planning by humans

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Planning is an important part of everyday life: we plan how to get from A to B, how to develop our careers, how to write a text, and how to approach a sports match. In all these cases, a sequence of decisions has to be made, with many possibilities at each step. For these reasons, planning taxes people’s cognitive abilities. In recent years, the field of artificial intelligence has made impressive progress on hard planning problems, such as playing chess and go. By contrast, the field of cognitive science has lagged behind in understanding how people solve difficult planning problems. To plan well, a person has to imagine situations that might happen in the future, even though they might never have experienced those situations before. At the same time, people also use their memory of similar situations to decide what to do. The goal of this project is to understand how these two processes -- imagining the future and recalling memories -- contribute to people’s planning decisions. This project will also support associated educational activities for high school and general audiences.This project will break down the mechanisms of human decision-making in complex planning tasks into two components: mental simulation of possible futures (prospective decision-making) and learning directly from past experiences (retrospective decision-making). Through a mobile app, the investigators collected data from people performing an engaging, competitive task that requires planning. The data set consists of hundreds of millions of decisions, allowing for statistically highly robust conclusions. The investigators will analyze these data using statistical methods and computational modeling. The prospective component of the model will be based on heuristic tree search algorithms. The retrospective component will be based on repeating successful actions in previously experienced states. The researchers will compare to alternative models and aim to account for human decisions and reaction times. It is expected that this project will lead to a better fundamental understanding of the cognitive processes involved in complex planning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.