NCS-FO: Using computational cognitive neuroscience to predict and optimize memory

NCS-FO：利用计算认知神经科学来预测和优化记忆

• 批准号: 1631436
• 负责人: Todd Gureckis
• 金额: $ 95.49万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631436&HistoricalAwards=false
• 关键词: NCS; FO; Using; computational; cognitive

The last decade has seen an explosion of research concerning the neural processes underlying memory formation and learning. As the basic research in this field becomes more mature, exciting possibilities for application of this knowledge have begun to emerge. This proposal aims to capitalize on these findings by developing assistive learning technologies that may revolutionize the way we teach and train people. Researchers at New York University will develop automated "adaptive teaching" technologies that guide learners through material in an individualized way. The goal is to increase retention or mastery of materials by tailoring instruction to individual learners. The novel contribution of this project is to combine insights from cognitive neuroscience and machine learning in the design and operation of such technologies. Successful development of this synergistic research would be a transformative application of neuroscience to our daily lives and may lead to new commercial technologies. The research will also provide a post-doctoral training opportunity for the next generation of scientists working at the intersection of neuroscience and computer science. The award is from the Integrated Strategies for Understanding Neural and Cognitive Systems program, with funding from the EHR Core Research (ECR) program, which supports fundamental research that advances the research literature on STEM learning, and from the Behavioral and Cognitive Sciences division in the SBE directorate. The specific scientific goal is to explore novel applications of neuroscience methods (particularly fMRI) to improve how people learn. Neuroscience research has identified robust neural correlates of successful memory formation (e.g., activity in brain areas such as the medial temporal lobe and the hippocampus). The goal of this project is to use these variables to help predict the information needs of learners in an adaptive way. The project design involves scanning an individual's brain during the learning phase of a task. The research team then will identify which materials would benefit from additional study by combining computational models of the time course of learning and forgetting with theories mapping neural activation to successful memory formation. A computer algorithm then selects new materials for learners to re-study in a subsequent session. The goal is to show that generating a training sequence from a computer-based "neurofeedback" algorithm can enhance long-term memory retention more than when learners choose for themselves which items to re-study. This is a high-risk but potentially large reward project that merges basic science findings from neuroscience and cognitive science in ways that may transform the way we educate people.

在过去的十年里，关于记忆形成和学习的神经过程的研究激增。随着这一领域的基础研究越来越成熟，应用这一知识的令人兴奋的可能性已经开始出现。该提案旨在通过开发辅助学习技术来利用这些发现，这些技术可能会彻底改变我们的教学和培训方式。 纽约大学的研究人员将开发自动化的“自适应教学”技术，以个性化的方式指导学习者阅读材料。其目标是通过为个别学习者量身定制教学来提高对材料的保留或掌握。 该项目的新贡献是将认知神经科学和机器学习的见解联合收割机结合到此类技术的设计和操作中。 这种协同研究的成功发展将是神经科学在我们日常生活中的变革性应用，并可能导致新的商业技术。 该研究还将为在神经科学和计算机科学交叉领域工作的下一代科学家提供博士后培训机会。该奖项来自理解神经和认知系统计划的综合战略，由EHR核心研究（ECR）计划提供资金，该计划支持推进STEM学习研究文献的基础研究，以及SBE董事会行为和认知科学部门。具体的科学目标是探索神经科学方法（特别是fMRI）的新应用，以改善人们的学习方式。神经科学研究已经确定了成功记忆形成的强大神经相关性（例如，大脑区域如内侧颞叶和海马体的活动）。本项目的目标是使用这些变量来帮助预测学习者的信息需求的适应性方式。该项目的设计涉及在任务的学习阶段扫描个人的大脑。然后，研究小组将通过将学习和遗忘的时间过程的计算模型与将神经激活映射到成功记忆形成的理论相结合，来确定哪些材料将从额外的研究中受益。然后，计算机算法为学习者选择新的材料，以便在随后的课程中重新学习。其目标是表明，与学习者自己选择重新学习哪些项目相比，从基于计算机的“神经反馈”算法生成训练序列可以更好地增强长期记忆保留。这是一个高风险但潜在回报巨大的项目，它融合了神经科学和认知科学的基础科学发现，可能会改变我们教育人们的方式。

项目成果

A neurocognitive model for predicting the fate of individual memories

用于预测个体记忆命运的神经认知模型

• 发表时间: 2018
• 期刊: Proceedings of the 40th Annual Conference of the Cognitive Science Society.
• 作者: Tubridy, S.;Halpern, D.;Davachi, L.;Gureckis, T.M.
• 通讯作者: Gureckis, T.M.

Knowledge Tracing Using the Brain

• DOI: 10.17605/osf.io/p7yzn
• 发表时间: 2018-04
• 期刊: Construction and Building Materials
• 影响因子: 7.4
• 作者: D. Halpern;Shannon Tubridy;Hong Yu Wang;Camille Gasser;P. O. Popp;L. Davachi;T. Gureckis