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The role of working memory in syntactic sentence realization: A modeling & simulation approach

机译:工作记忆在句法实现中的作用:一种建模与仿真方法

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This paper examines the effects of working memory size in incremental grammatical encoding during language production. Our experiment tests different variants of a computational-cognitive model that combines an empirically validated framework of general cognition, ACT-R, with a linguistic theory, Combinatory Categorial Grammar. The model is induced from a corpus of spoken dialogue. This methodology facilitates comparison of different strategies and working memory capacities according to the similarity of the model's produced sentences to the corpus sentences. The experiment presented shows that while having more working memory available improves performance, using less working memory during realization does as well, even after controlling sentence length. Sentences realized with a more incremental strategy also appear to more closely track the naturalistic data. As high incrementality is correlated with low working memory usage, this study offers a possible mechanism by which syntactic incrementality can be explained. Finally, this paper proposes a multi-disciplinary modeling and simulation-based approach to empirical psycholinguistic inquiry. (C) 2019 Elsevier B.V. All rights reserved.
机译:本文研究了语言生成过程中工作内存大小在增量语法编码中的影响。我们的实验测试了计算认知模型的各种变体,该变体结合了经过实践验证的一般认知框架ACT-R和语言理论组合分类语法。该模型是从口头对话的语料库中得出的。根据模型所产生的句子与语料库句子的相似度,此方法有助于比较不同的策略和工作记忆能力。提出的实验表明,尽管拥有更多的工作内存可以提高性能,但即使在控制句子长度之后,在实现过程中使用更少的工作内存也可以提高性能。用更多增量策略实现的句子似乎也可以更紧密地跟踪自然数据。由于较高的增量与较低的工作内存使用量相关,因此本研究提供了一种可能的机制,可以以此来解释语法增量。最后,本文提出了一种基于多学科建模和模拟的经验心理语言探究方法。 (C)2019 Elsevier B.V.保留所有权利。

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