NCS-FO: Neurobehavioral integration of visual and semantic number knowledge and its role for individual variation in the math ability of children and adults

NCS-FO：视觉和语义数字知识的神经行为整合及其对儿童和成人数学能力个体差异的作用

• 批准号: 1734735
• 负责人: Melissa Libertus
• 金额: $ 96.63万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734735&HistoricalAwards=false
• 关键词: NCS; FO; Neurobehavioral; integration; visual

This project, conducted by a team of researchers at the University of Pittsburgh, will address the need to improve math abilities in American children and adults. According to the 2015 National Assessment of Educational Progress, only 40% of 4th graders, and 33% of 8th graders score at, or above, proficiency level in math, and only about 30% of US adults can complete basic mathematical processes in real-world scenarios such as looking at a thermometer and figuring out the temperature. Such poor math achievement outcomes impose significant burdens, such as in securing employment, on individuals who enter adulthood without achieving basic proficiency, and challenges the capacity of the US to remain competitive in a global economy that is strongly driven by the intellectual capital of its citizens. This project will investigate a foundational skill that underlies math achievement: the ability to recognize visual number symbols by connecting them with the quantities they represent. Using functional magnetic resonance imaging (fMRI) and behavioral measures, the project team will characterize the neural constituents of number knowledge and will test for pathways within this number network that contribute to this "symbolic integration" and math ability. Finally, by studying adults and 8-year-old children, they will test whether the neural substrates of symbolic integration change with age, and if so, whether these changes correspond to shifts in the behavioral profile of symbolic integration and individual difference in math ability. Overall, by focusing on the widely used, but poorly understood, construct of symbolic integration, the proposed work will have broad impact on theories of math ability that make assumptions about these underlying processes and will inform future studies examining math learning trajectories and remediation strategies for struggling math learners. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).The overarching objective of the project is to investigate whether symbolic integration is foundational to math ability in adults and children. The project leverages the larger and more established literature on word recognition to develop and test a symbolic integration hypothesis of number processing. This model posits that formal math ability rests in part upon the integration of visual symbols (i.e., Arabic numerals) with the magnitudes they represent, via both direct (visual-semantic) and indirect (visual-verbal, visual-manual) pathways. An innovative combination of neurobehavioral measures will be used to test the model, through an individual differences study involving 100 adults and 125 8-year-old children. The project team will develop a novel neuroimaging protocol and will use cutting-edge multivariate methods to efficiently and broadly identify and characterize the neural constituents of a number processing network. A likely set of regions includes those involved in visual (fusiform gyrus), verbal (angular gyrus), manual (precental gyrus), and semantic (inferior parietal cortex) coding of number. In addition, resting state data will be acquired from each participant, and used to extract a metric of connectivity between identified visual, verbal, manual, and semantic constituents of number knowledge. A pair of behavioral tasks will measure the associative strength between visual and semantic codes for number (i.e., symbolic integration) in each participant. Using general linear models (GLM), the investigators will then test the prediction that both direct (visual-semantic) and mediated (visual-verbal-semantic; visual-manual-semantic) pathways significantly contribute to symbolic integration skill. Finally, standardized measures of math ability will be obtained from each participant. A GLM will be used to test for a predicted positive relation between individual differences in symbolic integration and math ability. Overall, the work will have a broad impact on theories of math ability and will inform future studies of math learning and intervention.

该项目由匹兹堡大学的研究人员团队进行，将解决提高美国儿童和成人数学能力的需求。根据2015年对教育进步的全国评估，只有40％的4年级学生和8年级学生在数学方面的水平水平或更高的评分，并且只有约30％的美国成年人可以在现实世界中完成基本的数学过程，例如在温度计上观察温度计并确定温度。如此糟糕的数学成就成果巨大的负担，例如在没有实现基本水平的情况下进入成年的个人，并挑战美国在全球经济中保持竞争力的能力，这是由其公民的智力驱动的。该项目将调查基本的数学成就基础：通过将视觉数字符号与所代表的数量联系起来的能力。使用功能磁共振成像（fMRI）和行为度量，项目团队将表征数字知识的神经成分，并将测试该数字网络中的途径，从而有助于这种“符号积分”和数学能力。最后，通过研究成年人和8岁的孩子，他们将测试符号整合的神经基板是否随着年龄的增长而变化，如果是，这些变化是否对应于符号整合的行为概况的变化和数学能力的个体差异。总体而言，通过关注符号整合的广泛使用但知之甚少的构建，拟议的工作将对数学能力的理论产生广泛的影响，这些理论对这些基本过程做出了假设，并将为未来的研究提供了研究数学学习轨迹和挣扎数学学习者的补救策略的未来研究。该项目由理解神经和认知系统（NSF-NCS）的综合策略提供资金，这是一个多学科计划，该计划由计算机和信息科学与工程局（CISE），教育和人力资源（EHR），工程（ENG），社会，行为，行为，行为和经济科学（SBE）共同研究，与成年人的整合目标是集合的目标，该计划是由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程和社交，行为，行为和经济能力共同支持的。该项目利用有关单词识别的更大，更既定的文献来开发和检验数字处理的符号整合假设。该模型认为，正式的数学能力部分取决于视觉符号（即阿拉伯数字）与它们所代表的大小的整合，这是通过直接（视觉语义）和间接（视觉语言，视觉，视觉，视觉手动）途径的整合。神经行为措施的创新组合将通过涉及100名成人和125个8岁儿童的个体差异研究来测试模型。项目团队将开发一种新颖的神经影像学协议，并将使用尖端的多元方法来有效，广泛地识别和表征数字处理网络的神经成分。一组可能的区域包括参与视觉（梭状回），言语（角度回），手动（小回）和语义（下顶层皮层）编码的区域。此外，将从每个参与者中获取静止状态数据，并用于在确定的视觉，口头，手动和语义构成数字知识的构成之间提取连通性的指标。一对行为任务将测量每个参与者中数字的视觉和语义代码之间的关联强度（即符号集成）。然后，研究人员将使用一般线性模型（GLM）测试直接（视觉语义）和介导的（视觉语言语义；视觉手册语义）途径的预测，这显着有助于象征性整合技能。最后，将从每个参与者中获得数学能力的标准化度量。 GLM将用于测试符号整合和数学能力的个体差异之间的预测正相关。总体而言，这项工作将对数学能力的理论产生广泛的影响，并将为未来的数学学习和干预研究提供信息。

项目成果

Numerical estrangement and integration between symbolic and non-symbolic numerical information: Task-dependence and its link to math abilities in adults

符号和非符号数字信息之间的数字疏远和整合：任务依赖性及其与成人数学能力的联系

• DOI: 10.1016/j.cognition.2022.105067
• 发表时间: 2022
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Ren, Xueying;Liu, Ruizhe;Coutanche, Marc N.;Fiez, Julie A.;Libertus, Melissa E.
• 通讯作者: Libertus, Melissa E.

Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain

• DOI: 10.1162/jocn_a_02008
• 发表时间: 2023-08-01
• 期刊: JOURNAL OF COGNITIVE NEUROSCIENCE
• 影响因子: 3.2
• 作者: Ren，Xueying;Libertus，Melissa E.
• 通讯作者: Libertus，Melissa E.