Wang Lin, Nour Eddine Samer, Brothers Trevor, Jensen Ole, Kuperberg Gina R
Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA; Department of Psychology, Tufts University, Medford, MA, 02155, USA.
Department of Psychology, Tufts University, Medford, MA, 02155, USA.
Neuroimage. 2025 Mar;308:120977. doi: 10.1016/j.neuroimage.2024.120977. Epub 2024 Dec 16.
During language comprehension, the larger neural response to unexpected versus expected inputs is often taken as evidence for predictive coding-a specific computational architecture and optimization algorithm proposed to approximate probabilistic inference in the brain. However, other predictive processing frameworks can also account for this effect, leaving the unique claims of predictive coding untested. In this study, we used MEG to examine both univariate and multivariate neural activity in response to expected and unexpected inputs during word-by-word reading comprehension. We further simulated this activity using an implemented predictive coding model that infers the meaning of words from their orthographic form. Consistent with previous findings, the univariate analysis showed that, between 300 and 500 ms, unexpected words produced a larger evoked response than expected words within a left ventromedial temporal region that supports the mapping of orthographic word-forms onto lexical and conceptual representations. Our model explained this larger evoked response as the enhanced lexico-semantic prediction error produced when prior top-down predictions failed to suppress activity within lexical and semantic "error units". Critically, our simulations showed that despite producing minimal prediction error, expected inputs nonetheless reinstated top-down predictions within the model's lexical and semantic "state" units. Two types of multivariate analyses provided evidence for this functional distinction between state and error units within the ventromedial temporal region. First, within each trial, the same individual voxels that produced a larger response to unexpected inputs between 300 and 500 ms produced unique temporal patterns to expected inputs that resembled the patterns produced within a pre-activation time window. Second, across trials, and again within the same 300-500 ms time window and left ventromedial temporal region, pairs of expected words produced spatial patterns that were more similar to one another than the spatial patterns produced by pairs of expected and unexpected words, regardless of specific item. Together, these findings provide compelling evidence that the left ventromedial temporal lobe employs predictive coding to infer the meaning of incoming words from their orthographic form during reading comprehension.
在语言理解过程中，对意外输入与预期输入产生的更大神经反应通常被视为预测编码的证据——预测编码是一种特定的计算架构和优化算法，旨在近似大脑中的概率推理。然而，其他预测处理框架也可以解释这种效应，使得预测编码的独特主张未经检验。在本研究中，我们使用脑磁图（MEG）来检查在逐字阅读理解过程中，对预期和意外输入做出反应时的单变量和多变量神经活动。我们进一步使用一个已实现的预测编码模型来模拟这种活动，该模型从单词的正字法形式推断单词的含义。与先前的研究结果一致，单变量分析表明，在300到500毫秒之间，意外单词在左腹内侧颞叶区域产生的诱发反应比预期单词更大，该区域支持将正字法单词形式映射到词汇和概念表征上。我们的模型将这种更大的诱发反应解释为当先前的自上而下预测未能抑制词汇和语义“错误单元”内的活动时产生的增强的词汇语义预测误差。至关重要的是，我们的模拟表明，尽管预期输入产生的预测误差最小，但它仍然在模型的词汇和语义“状态”单元内恢复了自上而下的预测。两种类型的多变量分析为腹内侧颞叶区域内状态单元和错误单元之间的这种功能差异提供了证据。首先，在每个试验中，在300到500毫秒之间对意外输入产生更大反应的相同个体体素，对预期输入产生了独特的时间模式，类似于在预激活时间窗口内产生的模式。其次，在多个试验中，同样在300 - 500毫秒的时间窗口和左腹内侧颞叶区域内，预期单词对产生的空间模式彼此之间比预期和意外单词对产生的空间模式更相似，无论具体项目如何。总之，这些发现提供了令人信服的证据，表明左腹内侧颞叶在阅读理解过程中采用预测编码从单词的正字法形式推断传入单词的含义。
