mlsanityData to 7 Oct 2026

Understanding Human Intelligence through Human Limitations

arXiv 2009.14050 ↗First posted 29 Sep 2020cs.AI cs.LGcited by 33

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Abstract (arXiv)

Recent progress in artificial intelligence provides the opportunity to ask the question of what is unique about human intelligence, but with a new comparison class. I argue that we can understand human intelligence, and the ways in which it may differ from artificial intelligence, by considering the characteristics of the kind of computational problems that human minds have to solve. I claim that these problems acquire their structure from three fundamental limitations that apply to human beings: limited time, limited computation, and limited communication. From these limitations we can derive many of the properties we associate with human intelligence, such as rapid learning, the ability to break down problems into parts, and the capacity for cumulative cultural evolution.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 7; 2025: 12; 2026: 3.

33 papers.

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  1. 2023Cognitive Architectures for Language Agentscited by 422
  2. 2022Emergent Analogical Reasoning in Large Language Modelscited by 239
  3. 2024Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worsecited by 107
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  5. 2021Meta-Learning to Compositionally Generalizecited by 62
  6. 2023The Relational Bottleneck as an Inductive Bias for Efficient Abstractioncited by 26
  7. 2025On Benchmarking Human-Like Intelligence in Machinescited by 24
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  9. 2023Studying and improving reasoning in humans and machinescited by 14
  10. 2023AI for Mathematics: A Cognitive Science Perspectivecited by 10
  11. 2025A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experimentscited by 10
  12. 2025Levels of Analysis for Large Language Modelscited by 9
21 more
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  2. 2022Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systemscited by 7
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  4. 2025Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labscited by 6
  5. 2021Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planningcited by 5
  6. 2024Compositional Generalization Across Distributional Shifts with Sparse Tree Operationscited by 5
  7. 2025Predicting Multi-Agent Specialization via Task Parallelizabilitycited by 5
  8. 2025Evaluating Language Models' Evaluations of Gamescited by 4
  9. 2026Social physics in the age of artificial intelligencecited by 4
  10. 2023Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligencecited by 3
  11. 2024Overcoming classic challenges for artificial neural networks by providing incentives and practicecited by 3
  12. 2023Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school studentscited by 2
  13. 2025Revisiting Rogers' Paradox in the Context of Human-AI Interactioncited by 2
  14. 2024Representations as Language: An Information-Theoretic Framework for Interpretabilitycited by 1
  15. 2024Dynamic Information Sub-Selection for Decision Supportcited by 1
  16. 2025Targeted control of fast prototyping through domain-specific interfacecited by 1
  17. 2026Position: Behavioral Systems Require Behavioral Testscited by 1
  18. 2022Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?cited by 0
  19. 2025Capacity-Constrained Continual Learningcited by 0
  20. 2025Efficient Generalization via Multimodal Co-Training under Data Scarcity and Distribution Shiftcited by 0
  21. 2026SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructionscited by 0