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Selection Mechanisms Regulating Contextual Predictions in Language

调节语言上下文预测的选择机制

基本信息

批准号：
8133538
负责人：
Ellen Frances Lau
金额：
$ 5.11万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-23 至 2012-08-22
项目状态：
已结题

项目摘要

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.

描述（由申请人提供）：本项目将探索神经机制，通过该机制，语言处理中的上下文预测与输入信息相结合。预测机制为语言输入带来的挑战提供了重要的解决方案，这些挑战通常是嘈杂的，快速的和可变的。最近的许多研究表明，语言预测者使用上下文来进行预测。这些预测可能会影响语言处理的多个阶段。本研究旨在分离词汇通达和词汇选择预测效应背后的神经机制。预测很可能会导致促进词汇的访问时，履行。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，预测可能会导致对选择机制的需求增加。确定语境预测对获取和选择机制的影响的时间过程和皮层区域对于理解认知者如何使用语境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。多模态成像技术将被用来在空间和时间上分离预测对词汇通达和词汇选择的影响。源定位的最小范数方法将用于直接集成并发MEG/EEG和fMRI数据集。语义启动范式将被用来调查基于存储的语义关联的预测，而句子上下文范式将被用来调查基于句子和语篇级表征的预测。预测强度和预测实现的程度将被操纵。这些研究将测试的假设，即强大的实现预测的结果，促进词汇的访问和强大的未实现的预测，导致增加对词汇选择的要求。基于先前的电生理学和神经成像工作，预期这些效应与不同的时空神经特征相关。第三项研究将利用这些发现来检验这样的假设：在精神分裂症中观察到的语言上下文处理障碍是由于使用上下文进行词汇选择的特定缺陷造成的。多式联运方式是该项目的一个重要方面。虽然EEG和MEG具有良好的时间分辨率，但通过当前的MEG/EEG定位技术不能容易地解决相邻皮层区域之间的功能区分。多模态记录将使人们有可能使用来自EEG和MEG的信息来限制对fMRI数据的解释，反之亦然。对于目前的项目，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。公共卫生关系：使用上下文信息来解释即将到来的输入是成功的语言理解的关键部分，并且在一些群体中已经报道了语言上下文使用的缺陷，包括自闭症和精神分裂症患者以及左下额叶区域受损的患者。本研究采用多模态神经影像学方法探讨语境预测对语言理解不同阶段的影响。更好地了解这个网络将有助于确定这种赤字的来源，从而有助于开发更优化的康复方法。