Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
This addresses the problem of understanding cross-linguistic patterns in numeral systems for linguists and cognitive scientists, but it is incremental as it builds on prior work linking learnability to prevalence.
The study investigated whether regular recursive numeral systems, like English base-10, are more common because they are easier to learn, using Reinforcement Learning to show that highly regular systems are easier to learn than irregular ones, with the effect absent for unnatural systems where signal length matters instead.
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.