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Leveraging big data to understand the interaction of task and language during monologic spoken discourse in speakers with and without aphasia
Language, Cognition and Neuroscience ( IF 2.3 ) Pub Date : 2020-12-21 , DOI: 10.1080/23273798.2020.1862258
Brielle C. Stark 1, 2, 3 , Julia Fukuyama 4
Affiliation  

ABSTRACT

Monologic spoken discourse allows us to evaluate every day speech while retaining some experimental constraint. It also has clinical relevance, providing cognitive-linguistic information not measured on typical standardised tests. Here, we leverage big behavioural data (AphasiaBank) to understand how discourse genres (narrative, procedural, expositional), and unique tasks within those genres, influence microstructural elements of discourse. We compare task × microstructure interaction across speakers with and without aphasia and evaluate the influence of aphasia type and overall aphasia severity on this interaction. Using multivariate statistical methods, we find that, for both speaker groups, discourse microstructure is most similar for tasks within the same discourse genre and that microstructure is largely dissociable across discourse genres. The aphasia group had more speaker variance per task, which was partially explained by aphasia type and overall aphasia severity. Our results provide necessary information for usage and interpretation of monologic discourse in research and clinical contexts.



中文翻译:

在有或没有失语症的演讲者进行单一语言演讲时,利用大数据来理解任务和语言之间的相互作用

摘要

言语性口语话语使我们能够评估每天的演讲,同时保留一些实验性的限制。它还具有临床相关性,提供了在典型的标准化测试中无法衡量的认知语言信息。在这里,我们利用大的行为数据(AphasiaBank)来了解话语体裁(叙事,程序,论述)以及这些体裁中的独特任务如何影响话语的微观结构要素。我们比较了有失语症和无失语症时任务×微观结构的交互作用,并评估了失语症类型和总体失语症严重程度对该交互作用的影响。使用多元统计方法,我们发现,对于两个说话者组,同一演讲类型中任务的话语微观结构最相似,并且整个演讲类型中的微观结构大部分是可分离的。失语症组每项任务的说话人差异更大,部分原因是失语症类型和总体失语症严重程度。我们的结果为在研究和临床环境中使用和解释单一论语提供了必要的信息。

更新日期:2020-12-21
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