Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

认知神经科学的许多研究都支持预测作为跨领域认知的规范计算。这种预测编码是通过来自高阶域通用电路的反馈来实现的，还是在特定域电路中本地实现的？使用哪些信息源来生成这些预测？这项研究在语言处理的背景下解决了这两个问题。我们提出了来自自然理解范式的功能磁共振成像证据（1）大脑对语言反应的预测编码是特定领域的，（2）这些预测对本地单词共现模式和层次结构都很敏感。使用最近开发的连续时间反卷积回归技术，支持从响应自然刺激的连续 BOLD 信号波动中发现数据驱动的血流动力学响应函数，我们发现预测措施在语言网络中的影响，但在通用多需求领域中没有发现网络，支持执行控制过程，之前曾与语言理解有关。此外，在语言网络内，表面水平和结构预测效果是可分离的。语言网络中的可预测性效果非常显着，该模型捕获了保留数据中超过 37% 的可解释方差。这些发现表明，人类句子处理机制使用对层次结构敏感且专门用于语言处理的认知过程来生成对即将出现的单词的预测，而不是通过高级执行控制机制的反馈。