Malaia Evie A, Borneman Sean C, Borneman Joshua D, Krebs Julia, Wilbur Ronnie B
Department of Communicative Disorders, University of Alabama, Tuscaloosa, AL, United States.
Department of Linguistics, Purdue University, West Lafayette, IN, United States.
Front Neurosci. 2023 Oct 12;17:1218510. doi: 10.3389/fnins.2023.1218510. eCollection 2023.
Sensory inference and top-down predictive processing, reflected in human neural activity, play a critical role in higher-order cognitive processes, such as language comprehension. However, the neurobiological bases of predictive processing in higher-order cognitive processes are not well-understood.
This study used electroencephalography (EEG) to track participants' cortical dynamics in response to Austrian Sign Language and reversed sign language videos, measuring neural coherence to optical flow in the visual signal. We then used machine learning to assess entropy-based relevance of specific frequencies and regions of interest to brain state classification accuracy.
EEG features highly relevant for classification were distributed across language processing-related regions in Deaf signers (frontal cortex and left hemisphere), while in non-signers such features were concentrated in visual and spatial processing regions.
The results highlight functional significance of predictive processing time windows for sign language comprehension and biological motion processing, and the role of long-term experience (learning) in minimizing prediction error.
反映在人类神经活动中的感觉推理和自上而下的预测处理在诸如语言理解等高阶认知过程中起着关键作用。然而，高阶认知过程中预测处理的神经生物学基础尚未得到充分理解。
本研究使用脑电图（EEG）来追踪参与者对奥地利手语和反向手语视频的皮质动力学，测量视觉信号中与光流的神经相干性。然后，我们使用机器学习来评估特定频率和感兴趣区域基于熵的相关性对脑状态分类准确性的影响。
与分类高度相关的脑电图特征分布在聋人手语使用者与语言处理相关的区域（额叶皮质和左半球），而在非手语使用者中，这些特征集中在视觉和空间处理区域。
结果突出了预测处理时间窗口对手语理解和生物运动处理的功能意义，以及长期经验（学习）在最小化预测误差中的作用。
