Selection Mechanisms Regulating Contextual Predictions in Language

描述（由申请人提供）：本项目将探索语言处理中的上下文预测与传入信息相结合的神经机制。预测机制为语言输入带来的挑战提供了一个重要的解决方案，语言输入通常是嘈杂的、快速的和可变的。最近的许多研究表明，语言理解者使用语境来进行预测。这些预测可能会影响语言处理的多个阶段。本项目旨在解离词汇获取和词汇选择预测效应的神经机制。当预测实现时，很可能导致便利的词汇访问。相反，由于自上而下和自下而上的信息提供的证据之间的冲突，当预测无法实现时，可能会导致对选择机制的要求增加。确定上下文预测对获取和选择机制影响的时间过程和皮层区域，对于理解理解者如何利用上下文更有效地处理语言，以及为什么有些人群似乎比其他人更不能够做到这一点，是必要的。多模态成像技术将用于在空间和时间上分离预测对词汇获取和词汇选择的影响。源定位的最小规范方法将用于直接整合并发的MEG/EEG和fMRI数据集。语义启动范式将用于研究基于存储语义关联的预测，而句子上下文范式将用于研究基于句子和话语级表示的预测。预测强度和预测实现的程度将被操纵。这些研究将检验强实现预测导致词汇获取便利和强未实现预测导致词汇选择需求增加的假设。基于先前的电生理和神经成像工作，这些效应预计与不同的时空神经特征有关。第三项研究将利用这些发现来检验一个假设，即在精神分裂症中观察到的语言语境处理障碍是由于词汇选择使用语境的特定缺陷造成的。多式联运方法是该项目的一个关键方面。虽然脑电信号和脑电信号具有良好的时间分辨率，但目前的脑电信号定位技术无法很好地解决相邻皮质区域之间的功能差异。多模态记录将有可能使用脑电图和脑磁图的信息来约束对功能磁共振成像数据的解释，反之亦然。对于当前的项目，这种方法将允许预测词汇激活和选择等机制被成功地解开。

DESCRIPTION (provided by applicant): This project will explore the neural mechanisms by which contextual predictions in language processing are integrated with incoming information. Predictive mechanisms provide an important solution to the challenges presented by linguistic input, which is often noisy, rapid, and variable. Much recent work suggests that language comprehenders use context to make predictions. These predictions are likely to impact multiple stages of language processing. This project aims at dissociating the neural mechanisms underlying predictive effects on lexical access and lexical selection. Predictions are likely to result in facilitated lexical access when fulfilled. Conversely, predictions may lead to increased demands on selection mechanisms when they are not fulfilled, due to the conflict between the evidence provided by top-down and bottom-up information. Determining the time course and cortical areas underlying the impacts of contextual prediction on access and selection mechanisms is necessary for understanding how comprehenders use context to process language more efficiently and why some populations seem less able to do this than others. Multimodal imaging techniques will be used to spatially and temporally dissociate the effects of prediction on lexical access and lexical selection. Minimum Norms methods of source localization will be used to directly integrate concurrent MEG/EEG and fMRI datasets. A semantic priming paradigm will be used to investigate predictions based on stored semantic associations, while a sentence context paradigm will be used to investigate predictions based on sentence- and discourse-level representations. Prediction strength and the degree to which predictions are fulfilled will be manipulated. These studies will test the hypothesis that strong fulfilled predictions result in facilitated lexical access and that strong unfulfilled predictions result in increased demands on lexical selection. Based on prior electrophysiological and neuroimaging work, these effects are expected to be associated with distinct spatiotemporal neural signatures. A third study will use these findings to test the hypothesis that the impairments in contextual processing in language that have been observed in schizophrenia are due to a specific deficit in the use of context for lexical selection. The multimodal approach is a critical aspect of the project. While EEG and MEG have excellent temporal resolution, functional distinctions between neighboring areas of cortex cannot be easily resolved by current MEG/EEG localization techniques. Multimodal recordings will make it possible to use information from EEG and MEG to constrain the interpretation of the fMRI data, and vice versa. For the current project, this approach will allow mechanisms such as predictive lexical activation and selection to be successfully disentangled. PUBLIC HEALTH RELEVANCE: Using contextual information to interpret upcoming input is a critical part of successful language comprehension, and deficits in use of language context have been reported in a number of groups, including patients with autism and schizophrenia and patients with damage to left inferior frontal areas. This project uses multimodal neuroimaging methods to investigate the effects of contextual prediction on different stages of language comprehension. A better understanding of this network will aid in determining the source of such deficits and thus will help lead to development of more optimal rehabilitation approaches.