Using Neural Imaging and Computational Modeling to Understand how Visuo-Spatial Processes Facilitate Mathematical Symbolization and Transfer

使用神经成像和计算模型来了解视觉空间过程如何促进数学符号化和传输

• 批准号: 1420008
• 负责人: John Anderson
• 金额: $ 138.13万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1420008&HistoricalAwards=false
• 关键词: Using; Neural; Imaging; Computational; Modeling

This project, to be conducted at Carnegie-Mellon University in Pittsburgh, Pennsylvania, will study algebra learning and problem-solving by college students and children age 12-14. The project will use behavioral and brain imaging methods to examine when problems are solved using symbols versus images. The results will be used to develop a computational model of mathematical reasoning, and to inform practices for teaching math. This project will advance the work of the REAL (Research on Education and Learning) program in studying the cognitive and neural basis of STEM (science, technology, engineering, and mathematics) learning.The project will use fMRI (functional magnetic resonance imaging) to study brain activity, focusing on brain regions including the RLPFC (rostrolateral prefrontal cortex) and AG (angular gyrus). The computational modeling will take place using the ACT-R (Adaptive Control of Thought-Rational) model. The main theoretical focus will be how symbolic and visuospatial processes are used and integrated during math learning and problem-solving.

该项目将在宾夕法尼亚州匹兹堡的伯奇-梅隆大学进行，将研究大学生和12-14岁儿童的代数学习和解决问题的能力。 该项目将使用行为和大脑成像方法来检查何时使用符号与图像解决问题。 研究结果将用于开发数学推理的计算模型，并为数学教学实践提供信息。（教育和学习研究）研究STEM认知和神经基础的计划（科学、技术、工程和数学）学习。该项目将使用功能性磁共振成像（功能性磁共振成像）来研究大脑活动，重点是包括RLPFC（吻外侧前额叶皮层）和AG（角回）在内的大脑区域。 计算建模将使用ACT-R（自适应控制的合理性）模型。 主要的理论重点将是如何在数学学习和解决问题的过程中使用和整合符号和视觉空间的过程。