Selection Mechanisms Regulating Contextual Predictions in Language

调节语言上下文预测的选择机制

• 批准号: 7998294
• 负责人: Ellen Frances Lau
• 金额: $ 4.8万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-23 至 2013-08-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7998294
• 关键词: Area; Autistic Disorder; Comprehension; Conflict (Psychology); Data; Data Set; Development; Electroencephalography; Functional Magnetic Resonance Imaging; Imaging Techniques; Impairment; Inferior; Language; Lead; Left; Linguistics; Methods; Multimodal Imaging; Patients; Population; Process; Rehabilitation therapy; Reporting; Resolution; Schizophrenia; Semantics; Solutions; Source; Staging; Techniques; Testing; Time; Work; base; language processing; lexical; neuroimaging; neuromechanism; public health relevance; relating to nervous system; spatiotemporal

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(由申请者提供)：这个项目将探索语言处理中语境预测与输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。本研究旨在解开词汇通达和词汇选择预测效应的神经机制。当预测被实现时，可能会导致更容易的词汇通达。相反，由于自上而下和自下而上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，因为它们没有得到满足。确定语境预测对获取和选择机制的影响的时间进程和大脑皮层区域对于理解理解者如何利用语境更有效地处理语言以及为什么某些群体似乎比其他群体更不能做到这一点是必要的。多通道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被用于直接集成并发的脑磁图/脑电和功能磁共振数据集。语义启动范式将被用来调查基于存储的语义联系的预测，而句子语境范式将被用来调查基于句子和语篇水平表征的预测。预测的强度和预测的实现程度将受到操纵。这些研究将检验这样一种假设，即较强的已实现预测会促进词汇通达，而较强的未实现预测会导致对词汇选择的需求增加。基于先前的电生理和神经成像工作，这些效应有望与不同的时空神经特征相关联。第三项研究将利用这些发现来检验这一假设，即在精神分裂症患者中观察到的语言语境处理障碍是由于在使用语境进行词汇选择方面的特定缺陷。多模式方法是该项目的一个关键方面。虽然EEG和MEG具有很好的时间分辨率，但目前的MEG/EEG定位技术不能很容易地解决皮质相邻区域之间的功能差异。多模式记录将使使用来自EEG和MEG的信息来限制对fMRI数据的解释成为可能，反之亦然。对于当前的项目，这种方法将允许预测性词汇激活和选择等机制成功地解开。 公共卫生相关性：使用语境信息解释即将到来的输入是成功理解语言的关键部分，据报道，许多群体在使用语境方面存在缺陷，包括自闭症和精神分裂症患者以及左下额叶受损患者。本项目使用多通道神经成像方法来研究语境预测对语言理解不同阶段的影响。更好地了解这一网络将有助于确定这种缺陷的来源，从而有助于制定更优化的康复办法。