Li Changye, Solinsky Jacob, Cohen Trevor, Pakhomov Serguei
Institute of Health Informatics, University of Minnesota, Minneapolis, 55455, MN, USA.
College of Pharmacy, University of Minnesota, Minneapolis, 55455, MN, USA.
Neurosci Inform. 2024 Mar;4(1). doi: 10.1016/j.neuri.2023.100155. Epub 2023 Dec 21.
While linguistic retrogenesis has been extensively investigated in the neuroscientific and behavioral literature, there has been little work on retrogenesis using computerized approaches to language analysis.
We bridge this gap by introducing a method based on comparing output of a pre-trained neural language model (NLM) with an artificially degraded version of itself to examine the transcripts of speech produced by seniors with and without dementia and healthy children during spontaneous language tasks. We compare a range of linguistic characteristics including language model perplexity, syntactic complexity, lexical frequency and part-of-speech use across these groups.
Our results indicate that healthy seniors and children older than 8 years share similar linguistic characteristics, as do dementia patients and children who are younger than 8 years.
Our study aligns with the growing evidence that language deterioration in dementia mirrors language acquisition in development using computational linguistic methods based on NLMs. This insight underscores the importance of further research to refine its application in guiding developmentally appropriate patient care, particularly in early stages.
虽然语言退行性变化已在神经科学和行为学文献中得到广泛研究，但利用计算机化语言分析方法进行退行性变化研究的工作却很少。
我们通过引入一种方法来弥合这一差距，该方法基于将预训练神经语言模型（NLM）的输出与其人工退化版本进行比较，以检查患有和未患有痴呆症的老年人以及健康儿童在自发语言任务中产生的语音转录本。我们比较了一系列语言特征，包括这些群体之间的语言模型困惑度、句法复杂性、词汇频率和词性使用情况。
我们的结果表明，健康的老年人和8岁以上的儿童具有相似的语言特征，痴呆症患者和8岁以下的儿童也是如此。
我们的研究与越来越多的证据一致，即痴呆症中的语言退化反映了使用基于NLM的计算语言学方法在发育过程中的语言习得。这一见解强调了进一步研究以完善其在指导适合发育阶段的患者护理，特别是早期护理中的应用的重要性。
