# Understanding Human Intelligence through Human Limitations

arXiv: https://arxiv.org/abs/2009.14050 · HTML: https://mlsanity.com/paper/2009.14050/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 30 of 33)

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- [The Relational Bottleneck as an Inductive Bias for Efficient Abstraction](https://mlsanity.com/paper/2309.06629/) (2023)
- [On Benchmarking Human-Like Intelligence in Machines](https://mlsanity.com/paper/2502.20502/) (2025)
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- [Studying and improving reasoning in humans and machines](https://mlsanity.com/paper/2309.12485/) (2023)
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- [Levels of Analysis for Large Language Models](https://mlsanity.com/paper/2503.13401/) (2025)
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- [Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning](https://mlsanity.com/paper/2102.00521/) (2021)
- [Compositional Generalization Across Distributional Shifts with Sparse Tree Operations](https://mlsanity.com/paper/2412.14076/) (2024)
- [Predicting Multi-Agent Specialization via Task Parallelizability](https://mlsanity.com/paper/2503.15703/) (2025)
- [Social physics in the age of artificial intelligence](https://mlsanity.com/paper/2603.16900/) (2026)
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- [Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence](https://mlsanity.com/paper/2310.12994/) (2023)
- [Overcoming classic challenges for artificial neural networks by providing incentives and practice](https://mlsanity.com/paper/2410.10596/) (2024)
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- [Revisiting Rogers' Paradox in the Context of Human-AI Interaction](https://mlsanity.com/paper/2501.10476/) (2025)
- [Representations as Language: An Information-Theoretic Framework for Interpretability](https://mlsanity.com/paper/2406.02449/) (2024)
- [Dynamic Information Sub-Selection for Decision Support](https://mlsanity.com/paper/2410.23423/) (2024)
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- [Position: Behavioral Systems Require Behavioral Tests](https://mlsanity.com/paper/2608.18081/) (2026)
- [Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?](https://mlsanity.com/paper/2205.06149/) (2022)

## 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.