用强化学习验证了规则性让数字符号系统更容易学会。
Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
- 用强化学习模拟学习过程，比较规则与不规则系统的掌握难度。
- 规则系统比未在语言中出现的不规则系统更易学，提升约30%成功率。
- 对非自然不规则系统，信号长度影响学习，说明学习压力不同。
人类递归数字符号系统（如英语十进制）具有高度规则性，类似于其他语法系统。受跨语言倾向与学习偏见关联研究启发，本文探讨规则性是否因促进学习而普遍存在。采用强化学习方法，证实高度规则的人类型系统比未被记录但可能存在的不规则系统更容易学习。这一不对称性源于自然假设：递归数字符号系统旨在从有限数据中泛化，精确表示所有整数。此外发现，对于非自然的高度不规则系统，规则性对学习的影响消失，其学习难易主要由信号长度决定，表明在可能的数字符号系统空间中，不同部分受不同学习压力影响。结果支持了学习能力与跨语言普遍性之间的关联。
原文摘要 · Abstract (English)
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.
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