Brent M R
Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Cognition. 1996 Oct-Nov;61(1-2):1-38. doi: 10.1016/s0010-0277(96)00779-2.
This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community, and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one or learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toc-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.
本文提供了一个关于儿童如何学习母语的计算研究的教程性介绍。其目的是让更广泛的研究群体了解近期的进展，并将这些进展置于当前理论问题的背景下。第一部分将计算研究和行为研究置于一个共同的理论框架内。接下来的两部分回顾了本卷中出现的两篇论文：一篇关于学习单词的意义，另一篇关于学习单词的发音。接下来的部分突出了一个在这两篇论文中独立出现的概念，我将其称为自主引导。经典的引导假设提出，儿童通过利用来自另一个领域（如语义）的信息，开始在特定的语言领域（如句法）中站稳脚跟。自主引导用在单个领域内应用的策略补充了经典引导的跨领域习得策略。自主引导策略通过表示部分和/或不确定的语言知识并使用它来分析输入来起作用。接下来的两部分回顾了对这个特刊的另外两篇贡献：一篇关于通过选择偏好学习单词意义，另一篇关于设置语法参数的算法。最后一部分提出了未来研究的方向。
