Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla–Wagner error-correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla–Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial-by-trial behavioral choices of participants, rather than fitting a one-for-all model using a single set of default parameters, we show that the model accurately captures participants’ choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla–Wagner model captures language learning.

中文翻译：

---

语言学习中的纠错机制：个体建模

自从首次被采用作为语言学习的计算模型以来，越来越多的证据表明 Rescorla-Wagner 纠错学习（Rescorla & Wagner，1972）捕获了语言处理的多个方面。尽管之前的研究通过使用 Rescorla-Wagner 规则来解释参与者在一系列任务中的行为，为 Rescorla-Wagner 规则提供了普遍支持，但我们重点关注在受控自然语言学习任务中测试模型生成的预测，并对数据进行建模。个体学习者的水平。通过调整模型的参数以适应参与者逐次试验的行为选择，而不是使用一组默认参数来拟合一一对应的模型，我们表明该模型准确地捕获了参与者的选择、时间延迟和响应一致性级别。我们还表明，性别和工作记忆容量会影响 Rescorla-Wagner 模型捕获语言学习的程度。