Collaborative Research:NCS-FO: How cognitive maps potentiate new learning: constraining a computational model by decoding the thoughts of superior memorists

合作研究：NCS-FO：认知图如何增强新学习：通过解码优秀记忆者的思想来约束计算模型

• 批准号: 2024622
• 负责人: Christopher Baldassano
• 金额: $ 51.67万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024622&HistoricalAwards=false
• 关键词: Collaborative; Research; NCS; FO; How

This project will break new ground in the study of memory by partnering with competitors in the USA Memory Championship. These competitors are not savants, but instead are well-practiced in the use of mnemonic techniques and, as a result, exhibit enhanced powers of memory on a range of real-world tasks, such as memorizing the items on a shopping list. All of these techniques rely on the practitioner structuring prior knowledge in very specific ways that facilitate the incorporation of new information. By scanning the brains of these trained memorists with functional magnetic resonance imaging (fMRI) and comparing their brain activity to participants who are learning these mnemonic systems for the first time, the researchers will identify principles for optimal scaffolding: How can prior knowledge be structured and used to most effectively support new learning? Identifying these principles will improve our fundamental understanding of real world-memory and will also lay the foundation for future educational interventions based on these principles. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (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 goal of the project is to extend theories of memory to address how people can optimally use cognitive maps (structured prior knowledge) to support new learning. Reinforcement learning algorithms will be applied to computational models of memory to make predictions about which strategies will result in the best performance, factoring in biological constraints on the human memory system. Model predictions about optimal memory strategies will be tested using fMRI data from memory experts who have spent years optimizing their ability to bind arbitrary information to an internal cognitive map (a “memory palace”), and who therefore serve as a unique comparison group for optimized memory models; these subjects will be compared to a sample of young adult subjects who are being trained to use these memorization techniques. New neuroimaging approaches developed by the researchers will allow them to map the brain patterns corresponding to each room of the memory palace and the patterns corresponding to each individual memory, and then track the activation of these patterns as subjects recall memories using mental walks through their palace. Results of these analyses will be used to test detailed model predictions about how memory training will alter the structure and use of subjects’ cognitive maps, and how these changes relate to memory performance. As a final test of the models, the researchers will use neural measurements of individual subjects’ cognitive maps to predict which specific items they will recall. By examining how prior knowledge is deployed to support learning in experts and novices at a much finer resolution than was previously possible, this work will provide the foundation for understanding why wide variations in memory performance exist across individuals and how memory can be improved, paving the way for targeted interventions to improve memory performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将通过与美国记忆锦标赛中的竞争对手合作，在记忆研究中打破新的基础。这些竞争对手不是熟练的人，而是在使用助记符技术方面做得很好，因此，在一系列实际任务（例如购物清单上的项目中记忆）上表现出增强的内存力量。所有这些技术都以非常具体的方式依赖实践者结构的先验知识，从而促进新信息的利用。通过用功能性磁共振成像（fMRI）扫描这些受过训练的记忆师的大脑，并将其大脑活动与首次学习这些助记符系统学习的参与者进行比较，研究人员将确定最佳脚手架的原则：如何结构和使用以最有效地支持新的学习新学习？确定这些原则将改善我们对现实世界中记忆的基本理解，也将为基于这些原则的未来教育干预奠定基础。该项目由理解神经和认知系统（NCS）的综合策略资助，神经和认知系统（NCS）是一项由计算机和信息科学与工程局（CISE），教育与人力资源（EHR），工程（ENG）以及社会，行为，行为和经济科学（SBE）共同支持的多学科计划。该项目的目的是扩展内存理论，以解决人们如何最佳地使用认知图（结构化的先验知识）来支持新学习。强化学习算法将应用于内存的计算模型，以预测哪些策略将导致最佳性能，并考虑到人类记忆系统的生物学约束。关于最佳内存策略的模型预测将使用来自内存专家的fMRI数据进行测试，这些记忆专家已经花费了多年的时间优化了他们将任意信息绑定到内部认知图（“存储宫殿”）的能力，因此它们是优化内存模型的独特比较组；这些受试者将与接受这些记忆技术的培训的年轻成年受试者的样本进行比较。研究人员开发的新神经影像学方法将使他们能够绘制与记忆宫的每个房间相对应的大脑模式以及与每个单独记忆相对应的模式，然后跟踪这些模式的激活，因为受试者使用宫殿的心理步行来回忆记忆。这些分析的结果将用于测试有关记忆训练如何改变受试者认知图的结构和使用以及这些变化与内存性能如何相关的详细模型预测。作为对模型的最终测试，研究人员将使用对单个受试者认知图的中性测量来预测他们将回忆的特定项目。通过检查如何以比以前的可能性更好的解决方案来支持专家和小说中的学习知识，这项工作将为理解为什么在个人之间存在广泛的记忆绩效差异，以及如何改善记忆，从而为有针对性的干预措施掩盖了为提高记忆绩效的有针对性的干预措施，以改善NSF的法定任务和综述的范围，反映了良好的依据，这是通过评估良好的依据来构成的。

项目成果

Optimal policies for free recall.

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• DOI: 10.1037/rev0000375
• 发表时间: 2022
• 期刊: Psychological Review
• 影响因子: 5.4
• 作者: Zhang, Qiong;Griffiths, Thomas L.;Norman, Kenneth A.
• 通讯作者: Norman, Kenneth A.