发表机构
Tongji University; Tuebingen University（同济大学; 图宾根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自然语言理解中上下文语义相关性对功能磁共振成像血氧水平依赖反应的预测作用，通过分析两个数据集，用广义相加混合模型等方法，发现语义相关性是有前景的血氧水平依赖指标，扩展了语言理解计算模型。
AI 中文摘要
自然语言理解要求听众处理局部概率期望和上下文语义关系。意外性已被广泛用于量化局部单词的意外程度，但在连续理解过程中它能否可靠地预测功能磁共振成像血氧水平依赖反应的证据并不一致。本研究调查上下文语义相关性，即传入单词与其最近语义上下文的关联强度，是否能预测自然语言理解过程中的血氧水平依赖反应。分析了两个公开的功能磁共振成像数据集，用广义相加混合模型对转换后的血氧水平依赖反应进行建模，并用FIR/去卷积分析测试原始连续血氧水平依赖时间序列。在爱丽丝数据集中，语义相关性在所有12个感兴趣区域都显著，而意外性在错误发现率校正后不显著。在飞蛾数据集中，语义相关性在所有30个感兴趣区域都显示出一致的负面影响，而意外性则没有类似模式。这些发现表明语义相关性是一种有前景的对上下文语义契合敏感的血氧水平依赖指标。更广泛地说，研究结果支持这样的观点，即自然语言理解过程中缓慢的血液动力学反应可能对上下文语义整合特别敏感，而局部概率预测误差可能更难通过功能磁共振成像可靠检测。从这个意义上说，语义相关性将语言理解的计算模型从仅预测扩展到上下文敏感的语义整合。
英文摘要
Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. This study tested whether contextual semantic relevance, measuring how strongly a target word relates to its recent semantic context, is associated with fMRI BOLD responses independently of word surprisal and lexical, timing, acoustic, and prosodic controls. We analyzed two public datasets: Alice (23 participants, one narrative) and Narratives (47 participants, 185 runs, four stories) using FIR/deconvolution and generalized additive mixed models. In Alice, semantic relevance was significant across all ROIs in FIR analyses, whereas surprisal was not. In GAMMs, both predictors showed broad significance. In Narratives, both predictors showed comparable spatial prevalence across ROIs. Semantic relevance showed robust BOLD associations across both datasets, with a particularly strong advantage over surprisal in the timing-sensitive Alice FIR analysis. The regionally heterogeneous direction of semantic relevance effects, with negative effects in posterior semantic regions and positive effects in frontal integration regions, suggests involvement of functionally distinct neural processes rather than a single uniform mechanism. These findings indicate that contextual semantic fit and local probabilistic expectation make partially distinct, dataset-dependent contributions to hemodynamic responses during naturalistic listening.
