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Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

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基于话语和病变的语盲率估计使用机器学习.

Nicholas Riccardi¹, Satvik Nelakuditi², Dirk B den Ouden¹

¹Department of Communication Sciences and Disorders, University of South Carolina, United States.

NeuroImage. Clinical

|April 9, 2024

概括

话语分析可以使用西方阿法西亚电池修订 (WAB-R) 阿法西亚系数 (AQ) 估计阿法西亚严重程度. 这种方法提供了一种不那么资源密集的方法来评估患有失语症的个体的语言能力.

关键词:

亚法西亚 (Aphasia) 是一种语言障碍.话语制作话语制作损伤症状映射损伤症状映射机器学习机器学习一次性中风,中风.

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科学领域:

神经语言学是一种神经语言学.
计算语言学计算语言学
临床神经科学临床神经科学

背景情况:

话语的产生对沟通至关重要,并揭示了语言能力.
失言症经常会损害话语的产生,需要准确的评估.
目前的综合性失言症评估,如西方失言症电池修订 (WAB-R),是资源密集的.

研究的目的:

为了确定话语测量是否可以准确地估计WAB-R失言率 (AQ).
探索基于话语的失语评估的生态有效性和减少资源需求.
调查神经成像病变数据对语障严重程度预测的贡献.

主要方法:

使用AphasiaBank提示,从三个话语任务 (陈述,叙事,程序) 中提取语言特征.
训练有素的机器学习模型使用话语特征预测WAB-R AQ.
基于话语的模型与包含结构神经成像损伤数据的模型进行了比较.

主要成果:

基于话语的机器学习模型有效估计了WAB-R AQ.
话语模型表现优于仅基于损伤位置的模型.
整合损伤数据并没有显著提高话语模型的预测性能.
对信息特征的分析表明,不同的话语提示涉及不同的语言方面.

结论:

话语分析提供了一种可行且不那么资源密集的方法来估计失语严重程度 (AQ).
基于话语的评估提供了生态学上有效的洞察力,了解在失言症中的语言功能.
了解不同话语类型的语言需求可以完善口音障碍的评估和干预.