ABSTRACT How language users become able to process forms they have never encountered in input is central to our understanding of language cognition. A range of models, including rule-based models, stochastic models, and analogy-based models have been proposed to account for this ability. Despite the fact that all three models are reasonably successful, we argue that productivity in language is more insightfully captured through learnability than by rules or probabilities. Using a combination of computational modelling and behavioural experimentation we show that the basic principle of error-driven learning allows language users to detect relevant patterns of any degree of systematicity. In case of allomorphy, these patterns are found at a level that cuts across phonology and morphology and is not considered by mainstream approaches to language. Our findings thus highlight how a learning-based approach applies to phenomena on the continuum from rule-based over probabilistic to “unruly” and constrains our inferences about the types of structures that should be targeted on a cognitively realistic account of allomorphic representation.

中文翻译：

---

从暴露中学到了什么：一种错误驱动的语言生产力方法

摘要 语言用户如何能够处理他们在输入中从未遇到过的形式是我们理解语言认知的核心。已经提出了一系列模型，包括基于规则的模型、随机模型和基于类比的模型来解释这种能力。尽管所有三个模型都相当成功，但我们认为通过可学习性比通过规则或概率更深刻地捕捉语言的生产力。使用计算建模和行为实验的组合，我们表明错误驱动学习的基本原理允许语言用户检测任何程度系统性的相关模式。在异形的情况下，这些模式是在跨越音系和形态的水平上发现的，并且不被主流语言方法考虑。