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

这个项目将在宾夕法尼亚州匹兹堡的卡内基梅隆大学进行，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脑成像方法来检查使用符号和图像解决问题的情况。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。该项目将推进REAL（教育与学习研究）项目在研究STEM（科学、技术、工程和数学）学习的认知和神经基础方面的工作。该项目将使用fMRI（功能性磁共振成像）来研究大脑活动，重点关注包括RLPFC （rostrolateral prefrontal cortex）和AG（角回）在内的大脑区域。计算建模将使用ACT-R（思维理性的自适应控制）模型进行。主要的理论焦点将是在数学学习和解决问题过程中如何使用和整合符号和视觉空间过程。

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.