评估递归数制系统中规律性与可学习性之间的关系 using 强化学习
Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
AI总结：
本文通过强化学习方法探讨递归数制系统的规律性对可学习性的影响，发现高规律性系统更易学习，但不规则系统的学习性受信号长度影响。
摘要：
人类递归数制系统（如英语十进制计数系统）高度规律，类似于许多其他语法系统。基于先前将语言学倾向与学习偏差联系起来的研究，本文探讨是否规律性使系统更易学习。采用强化学习文献中的方法，我们确认高规律性的人类（类）系统比未证实但可能不规律的系统更容易学习。这种不对称性在自然假设下出现，即递归数制系统旨在从有限数据中泛化以精确表示所有整数。我们还发现，不自然的高不规律系统中规律性对可学习性无影响，其可学习性由信号长度决定，表明不同压力可能在不同数制空间部分影响可学习性。本研究为将可学习性与语言学普遍性联系起来的文献做出了贡献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
