Stille Catharina Marie, Bekolay Trevor, Blouw Peter, Kröger Bernd J
Department for Phoniatrics, Pedaudiology, and Communication Disorders, Medical Faculty RWTH Aachen University, Aachen, Germany.
Applied Brain Research, Waterloo, ON, Canada.
Front Robot AI. 2019 Aug 2;6:62. doi: 10.3389/frobt.2019.00062. eCollection 2019.
Many medical screenings used for the diagnosis of neurological, psychological or language and speech disorders access the language and speech processing system. Specifically, patients are asked to fulfill a task (perception) and then requested to give answers verbally or by writing (production). To analyze cognitive or higher-level linguistic impairments or disorders it is thus expected that specific parts of the language and speech processing system of patients are working correctly or that verbal instructions are replaced by pictures (avoiding auditory perception) or oral answers by pointing (avoiding speech articulation). The first goal of this paper is to propose a large-scale neural model which comprises cognitive and lexical levels of the human neural system, and which is able to simulate the human behavior occurring in medical screenings. The second goal of this paper is to relate (microscopic) neural deficits introduced into the model to corresponding (macroscopic) behavioral deficits resulting from the model simulations. The Neural Engineering Framework and the Semantic Pointer Architecture are used to develop the large-scale neural model. Parts of two medical screenings are simulated: (1) a screening of word naming for the detection of developmental problems in lexical storage and lexical retrieval; and (2) a screening of cognitive abilities for the detection of mild cognitive impairment and early dementia. Both screenings include cognitive, language, and speech processing, and for both screenings the same model is simulated with and without neural deficits (physiological case vs. pathological case). While the simulation of both screenings results in the expected normal behavior in the physiological case, the simulations clearly show a deviation of behavior, e.g., an increase in errors in the pathological case. Moreover, specific types of neural dysfunctions resulting from different types of neural defects lead to differences in the type and strength of the observed behavioral deficits.
许多用于诊断神经、心理或语言及言语障碍的医学筛查会涉及语言和言语处理系统。具体而言，会要求患者完成一项任务（感知），然后要求其口头或书面给出答案（产出）。因此，为了分析认知或更高级别的语言损伤或障碍，预计患者语言和言语处理系统的特定部分能正常工作，或者口头指令被图片取代（避免听觉感知），口头回答被指向动作取代（避免言语表达）。本文的首要目标是提出一个大规模神经模型，该模型包含人类神经系统的认知和词汇层面，并且能够模拟医学筛查中出现的人类行为。本文的第二个目标是将模型中引入的（微观）神经缺陷与模型模拟产生的相应（宏观）行为缺陷联系起来。利用神经工程框架和语义指针架构来开发大规模神经模型。模拟了两项医学筛查的部分内容：（1）一项用于检测词汇存储和词汇检索发育问题的单词命名筛查；（2）一项用于检测轻度认知障碍和早期痴呆的认知能力筛查。两项筛查都包括认知、语言和言语处理，并且对于这两项筛查，在有和没有神经缺陷的情况下（生理情况与病理情况）模拟同一个模型。虽然在生理情况下对两项筛查的模拟都产生了预期的正常行为，但模拟结果清楚地显示了行为偏差，例如在病理情况下错误增加。此外，由不同类型神经缺陷导致的特定类型神经功能障碍会导致观察到的行为缺陷在类型和程度上存在差异。
