The Efficiency of Human Cognition Reflects Planned Information Processing
This provides insight into hierarchical planning and cognitive control for cognitive science and AI, but it is incremental as it builds on existing planning theories.
The authors tackled the problem of how people efficiently allocate cognitive resources for planning by formalizing it as a meta-reasoning problem with a recursive Bellman objective. They found that human reaction times reflect planned information processing, consistent with their predictions.
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their actions. Put another way, people should also "plan their plans". Here, we formulate this aspect of planning as a meta-reasoning problem and formalize it in terms of a recursive Bellman objective that incorporates both task rewards and information-theoretic planning costs. Our account makes quantitative predictions about how people should plan and meta-plan as a function of the overall structure of a task, which we test in two experiments with human participants. We find that people's reaction times reflect a planned use of information processing, consistent with our account. This formulation of planning to plan provides new insight into the function of hierarchical planning, state abstraction, and cognitive control in both humans and machines.