The Efficiency of Human Cognition Reflects Planned Information Processing
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 13 Feb 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.
6 papers.
- 2023Inverse Decision Modeling: Learning Interpretable Representations of Behaviorcited by 10
- 2022On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learningcited by 6
- 2023Bayesian Reinforcement Learning with Limited Cognitive Loadcited by 4
- 2024Satisficing Exploration for Deep Reinforcement Learningcited by 4
- 2024Inverse Concave-Utility Reinforcement Learning is Inverse Game Theorycited by 2
- 2025The Alignment Bottleneckcited by 1
- 2025The Alignment Bottleneck19 Sep 2025
- 2024Satisficing Exploration for Deep Reinforcement Learning16 Jul 2024
- 2024Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory29 May 2024
- 2023Inverse Decision Modeling: Learning Interpretable Representations of Behavior28 Oct 2023
- 2023Bayesian Reinforcement Learning with Limited Cognitive Load5 May 2023
- 2022On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning30 Oct 2022