Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
arXiv:2602.21720v1 Announce Type: new 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 space of poss
arXiv:2602.21720v1 Announce Type: new 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 space of possible numeral systems. Our results contribute to the body of work linking learnability to cross-linguistic prevalence.
Executive Summary
This article explores the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning. The study finds that highly regular human-like systems are easier to learn than irregular systems, suggesting that regularity facilitates learning. The research contributes to the understanding of why regular systems are common across languages, providing insight into the link between learnability and cross-linguistic prevalence. The findings have implications for the design of numeral systems and the study of language acquisition.
Key Points
- ▸ Regularity in numeral systems facilitates learning
- ▸ Reinforcement Learning methods are used to evaluate learnability
- ▸ The influence of regularity on learnability varies across different types of numeral systems
Merits
Methodological Innovation
The use of Reinforcement Learning methods to study learnability in numeral systems is a novel and effective approach.
Demerits
Limited Generalizability
The study's focus on numeral systems may limit the generalizability of the findings to other areas of language or cognition.
Expert Commentary
This study makes a significant contribution to our understanding of the complex relationship between regularity and learnability in numeral systems. The use of Reinforcement Learning methods provides a nuanced and quantitative perspective on this issue, shedding light on the cognitive and linguistic factors that underlie language acquisition. The findings have important implications for the design of more effective numeral systems and language instruction methods, and highlight the need for further research into the cognitive and neural mechanisms underlying numerical cognition.
Recommendations
- ✓ Future studies should investigate the generalizability of these findings to other areas of language or cognition
- ✓ The development of more sophisticated Reinforcement Learning models to capture the complexities of human language acquisition
