Dupre Gabe
Department of Philosophy, University of California, Davis, United States of America.
Cognition. 2024 Jun;247:105771. doi: 10.1016/j.cognition.2024.105771. Epub 2024 Mar 19.
Standard computational models of language acquisition treat acquiring a language as a process of inducing a set of string-generating rules from a collection of linguistic data assumed to be generated by these very rules. In this paper I give theoretical and empirical arguments that such a model is radically unlike what a human language learner must do to acquire their native language. Most centrally, I argue that such models presuppose that linguistic data is directly a product of a grammar, ignoring the myriad non-grammatical systems involved in the use of language. The significance of these non-target systems in shaping the linguistic data children are exposed to undermines any simple reverse inference from linguistic data to grammatical competence.
标准的语言习得计算模型将习得一门语言视为从假定由这些规则生成的语言数据集合中归纳出一组字符串生成规则的过程。在本文中,我给出了理论和实证论据,表明这样的模型与人类语言学习者习得母语的方式截然不同。最核心的是,我认为这样的模型预先假定语言数据直接是语法的产物,而忽略了语言使用中涉及的无数非语法系统。这些非目标系统在塑造儿童接触到的语言数据方面的重要性,破坏了从语言数据到语法能力的任何简单反向推理。