通过脑电与视觉运动特征的时空一致性，解码聋人手语理解中的预测性推理机制。
Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence
- 利用脑电与光流运动特征的时空相干性构建预测神经动态表征。
- 发现左半球和额叶低频相干性是语言理解的关键神经标志。
- 揭示了经验依赖的神经特征与年龄相关，适合研究感知生成模型。
人类语言处理依赖大脑的预测推理能力。本文提出一种机器学习框架，用于解码聋人对动态视觉语言刺激的脑电（EEG）响应。通过神经信号与光流提取的运动特征之间的相干性，构建预测神经动态的时空表征。基于熵的特征选择方法识别出区分可理解语言输入与语言破坏（时间反转）刺激的频率特异性神经标志。结果表明，分布式左半球及额叶低频相干性是语言理解的关键特征，且经验依赖的神经特征与年龄相关。该工作展示了探测大脑经验驱动感知生成模型的新颖多模态方法。
原文摘要 · Abstract (English)
Human language processing relies on the brain's capacity for predictive inference. We present a machine learning framework for decoding neural (EEG) responses to dynamic visual language stimuli in Deaf signers. Using coherence between neural signals and optical flow-derived motion features, we construct spatiotemporal representations of predictive neural dynamics. Through entropy-based feature selection, we identify frequency-specific neural signatures that differentiate interpretable linguistic input from linguistically disrupted (time-reversed) stimuli. Our results reveal distributed left-hemispheric and frontal low-frequency coherence as key features in language comprehension, with experience-dependent neural signatures correlating with age. This work demonstrates a novel multimodal approach for probing experience-driven generative models of perception in the brain.
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