An analysis of sequential decision-making in the videogame Frogger
Humans can employ sophisticated strategies to plan their next actions while only having limited cognitive capacity. Most of the studies investigating human behavior focus on minimal and rather abstract tasks. We provide an environment inspired by the video game Frogger, especially designed for studying how far humans plan ahead. This is described by the planning horizon, a not directly measurable quantity representing how far ahead people can consider the consequences of their actions. We treat the subjects’ eye movements as externalization of their internal planning horizon and can thereby infer its development over time.
We found that people can dynamically adapt their planning horizon when switching between tasks. While subjects employed bigger planning horizons, we could not measure any physiological indicators of stronger cognitive engagement. Subjects using larger planning horizons were able to score higher in the game. We designed neural networks for predicting the subject’s planning horizon in different situations, providing further insight on the key features needed for accurate predictions of the planning horizon. In general, models trained only on subject-specific data achieved higher accuracy than models trained on data collected from all subjects.