Rational use of cognitive resources in human planning

In a series of mouse-tracking experiments, Callaway et al. show that people use planning strategies that strike a near-optimal balance between reward and computational cost. Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

Automated summary

In this study, the authors use mouse-tracking experiments to show that people seek a balance between reward and computational cost in decision-making. They propose a model of resource-constrained planning and find that people's planning strategies are broadly consistent with the optimal model but not with any single heuristic model, indicating the presence of cognitive constraints yet to be discovered.