NCS-FO: Integrative Neural Approaches to Understanding Science Text Comprehension

宾夕法尼亚州立大学研究人员的这个项目的总体目标是了解学龄儿童、成人第一语言读者和成人第二语言读者对说明性科学文本阅读理解的神经认知机制。本研究整合了几个与阅读理解相关的独立研究传统的知识：(1)成人第一语言阅读理解的认知心理和教育研究，(2)儿童第一语言阅读理解的认知心理和教育研究，(3)文本理解的神经成像研究，以及(4)知识表征的图论模型。该项目的发现将对STEM教育产生重大影响。它是由理解神经和认知系统的综合战略计划资助的，该计划包括来自电子健康记录核心研究(ECR)计划和SBE行为和认知科学部门的支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。研究小组将研究阅读科学文本时的行为和神经模式，试图揭开第一语言和第二语言背后的大脑文本阅读网络，以及好读者和差读者之间的神经认知差异。它结合了功能磁共振成像、对学习者能力的认知研究以及认知建模和大脑网络中的高级数据分析技术。通过学习大脑的功能和结构路径中存在的连接来研究大脑网络，有望为个体差异、神经可塑性以及语言学习和表征的神经基础提供新的见解。将应用数据分析来探讨连接模式的动态变化。这种方法不仅可以研究学习诱导或经验依赖的神经变化，还可以研究大脑网络在学习和表征方面的个体差异(包括通过静息状态功能连接捕获的内在神经模式)。观察到的神经变化和模式将使研究人员能够预测谁可能是更成功的学习者。

The overall goal of this project from researchers at Pennsylvania State University is to understand the neurocognitive mechanisms underlying reading comprehension of expository scientific texts by school-aged children, adult first language readers, and adult second language readers. The proposed research integrates knowledge from several largely separate research traditions that are related to reading comprehension: (1) cognitive psychological and educational research in adult first language reading comprehension, (2) cognitive psychological and educational research in child first language reading comprehension, (3) neuroimaging research in text comprehension, and (4) graph-theoretical modeling of knowledge representation. Findings from this project will have significant implications for STEM education. It was funded by the Integrated Strategies for Understanding Neural and Cognitive Systems program, which included support from the EHR Core Research (ECR) program and the Behavioral and Cognitive Sciences division of SBE. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The research team will study the behavioral and neural patterns during the reading of science text, in an attempt to unravel the brain's text reading network underlying first and second languages, and the neurocognitive differences between good versus poor readers. It combines methods from functional magnetic resonance imaging, cognitive study of learner abilities, and advanced data-analytic techniques in cognitive modeling and brain networks. The study of brain networks through the connectivity that exists in the functional and structural pathways of the learning brain holds the promise of providing new insights into the neural bases of individual differences, neuroplasticity, and language learning and representation. Data analytics will be applied to probe into the dynamic changes in connectivity patterns. This approach will allow the study not only of learning-induced or experience-dependent neural changes, but also what brain networks characterize individual differences in learning and representation (including intrinsic neural patterns captured by resting-state functional connectivity). Observed neural changes and patterns will allow the researchers to predict who might be more successful learners.