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Symbolic metaprogram search improves learning efficiency and explains rule learning in humans
Joshua S Rule1, Steven T Piantadosi2, Andrew Cropper3
1Psychology, University of California, Berkeley, Berkeley, CA, 94704, USA. rule@berkeley.edu.
Nature Communications
|August 10, 2024
Summary
Humans efficiently learn complex rules using metaprograms, which are programs that revise other programs. This approach significantly enhances symbolic rule-learning, closely matching human learning efficiency with minimal computational cost.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Machine Learning
Background:
- Humans acquire complex rules for various tasks with minimal data.
- Symbolic program learning models explain rule acquisition but struggle with complexity.
- Scaling symbolic rule-learning to human-level performance remains a challenge.
Purpose of the Study:
- To investigate if metaprogramming can improve symbolic rule-learning efficiency.
- To model human rule acquisition in complex domains.
- To compare metaprogramming with existing symbolic learning methods.
Main Methods:
- Symbolic search over metaprograms (programs that revise programs).
- Evaluation on a behavioral benchmark of 100 algorithmically rich rules.
- Comparison with alternative symbolic rule-learning models.
Main Results:
- Metaprogramming significantly improved learning efficiency compared to traditional methods.
- The approach demonstrated higher accuracy in fitting human learning patterns.
- Required computation aligned with conservative estimates of human thinking time.
Conclusions:
- Metaprogram-like representations are a promising mechanism for efficient human rule acquisition.
- This method offers a scalable solution for symbolic rule-learning.
- Future research can explore metaprogramming in diverse learning domains.
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