Working memory is widely assumed to underlie multi-step planning, where representations of possible future actions and rewards are iteratively updated before determining a choice. But most working memory research focuses on a context where stimuli are presented simultaneously and the value of encoding each stimulus is independent of others. To bridge this gap, we developed a task in which participants sequentially observe the reward at each node in a decision tree before selecting a path that maximizes cumulative rewards. We characterize the optimal encoding and maintenance strategy for this task, which trades off the cost of storing information with the potential benefit of informing later choices. The model encodes rewards in choice-relevant paths more often, in particular, rewards on the best and (to a lesser extent) worst paths. Our participants show the same pattern in the accuracy of their explicit recall. Our study thus establishes an empirical and theoretical foundation for models of how people encode and maintain information while planning.

Authors:

Zhuojun Ying: UCSD; Frederick Callaway: New York University; Anastasia Kiyonaga: UC San Diego; Marcelo G Mattar: New York University