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: 2024587
• 负责人: Kenneth Norman
• 金额: $ 23万
• 依托单位: Princeton 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=2024587&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的竞争对手合作，在记忆研究方面开辟新天地。 这些竞争者不是学者，而是在记忆技巧的使用方面训练有素，因此，在一系列现实世界的任务中表现出更强的记忆力，例如记住购物清单上的物品。 所有这些技术都依赖于从业者以非常具体的方式构建先验知识，以促进新信息的整合。 通过用功能性磁共振成像（fMRI）扫描这些受过训练的记忆者的大脑，并将他们的大脑活动与第一次学习这些记忆系统的参与者进行比较，研究人员将确定最佳支架的原则：如何构建和使用先前的知识来最有效地支持新的学习？识别这些原则将提高我们对真实的世界记忆的基本理解，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，这是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）以及社会，行为和经济科学（SBE）董事会共同支持的多学科计划。 该项目的目标是扩展记忆理论，以解决人们如何最佳地使用认知地图（结构化的先验知识）来支持新的学习。强化学习算法将应用于记忆的计算模型，以预测哪些策略将导致最佳性能，并考虑人类记忆系统的生物学约束。关于最佳记忆策略的模型预测将使用来自记忆专家的功能磁共振成像数据进行测试，这些记忆专家花了数年时间优化他们将任意信息绑定到内部认知地图（“记忆宫殿”）的能力，因此他们作为优化记忆模型的独特比较组;这些受试者将与正在接受这些记忆技术培训的年轻成年受试者进行比较。研究人员开发的新的神经成像方法将使他们能够绘制出与记忆宫殿的每个房间相对应的大脑模式以及与每个人的记忆相对应的模式，然后跟踪这些模式的激活，因为受试者通过他们的宫殿使用心理行走来回忆记忆。这些分析的结果将用于测试详细的模型预测，即记忆训练将如何改变受试者认知地图的结构和使用，以及这些变化如何与记忆表现相关。作为对模型的最后一次测试，研究人员将使用个体受试者认知地图的神经测量来预测他们会回忆起哪些特定的项目。通过研究先验知识如何被部署以支持专家和新手以比以前更精细的分辨率进行学习，这项工作将为理解为什么个体之间存在记忆表现的广泛差异以及如何改善记忆提供基础，为有针对性的干预措施铺平道路，以提高记忆性能。该奖项反映了NSF的法定使命，并已被认为值得支持，使用基金会的知识价值和更广泛的影响审查标准进行评估。

项目成果

Optimal policies for free recall.

免费召回的最佳政策。

• 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.