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[2602.21720] Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
Summary
This article explores the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning, demonstrating that regular systems are easier to learn than irregular ones.
Why It Matters
Understanding the connection between regularity and learnability in numeral systems can inform linguistic theory and AI development, particularly in natural language processing and machine learning applications. This research contributes to the broader discourse on how human language systems evolve and are learned.
Key Takeaways
- Regular numeral systems facilitate easier learning compared to irregular systems.
- The study utilizes Reinforcement Learning methods to analyze learnability.
- Different pressures influence learnability across various numeral system types.
- Findings support the link between linguistic regularity and cross-linguistic prevalence.
- Implications extend to AI and natural language processing applications.
Computer Science > Computation and Language arXiv:2602.21720 (cs) [Submitted on 25 Feb 2026] Title:Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning Authors:Andrea Silvi, Ponrawee Prasertsom, Jennifer Culbertson, Devdatt Dubhashi, Moa Johansson, Kenny Smith View a PDF of the paper titled Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning, by Andrea Silvi and 4 other authors View PDF HTML (experimental) Abstract:Human recursive numeral systems (i.e., counting systems such as English base-10 numerals), like many other grammatical systems, are highly regular. Following prior work that relates cross-linguistic tendencies to biases in learning, we ask whether regular systems are common because regularity facilitates learning. Adopting methods from the Reinforcement Learning literature, we confirm that highly regular human(-like) systems are easier to learn than unattested but possible irregular systems. This asymmetry emerges under the natural assumption that recursive numeral systems are designed for generalisation from limited data to represent all integers exactly. We also find that the influence of regularity on learnability is absent for unnatural, highly irregular systems, whose learnability is influenced instead by signal length, suggesting that different pressures may influence learnability differently in different parts of the s...