Collaborative Research: How does the brain represent abstract concepts?

合作研究：大脑如何表达抽象概念？

• 批准号: 2022357
• 负责人: David Kraemer
• 金额: $ 28.36万
• 依托单位: Dartmouth College
• 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=2022357&HistoricalAwards=false
• 关键词: Collaborative; Research; How; does; brain

The ability to reason about the relations between sets of concepts—relational reasoning—gives rise to abstract thought, and has fueled some of humanity’s greatest achievements in science and technology. Although prior research has identified where in the brain relational reasoning takes place, this project pushes the research field by addressing how the brain represents abstract relations. Specifically, the project aims to address three key questions: (1) Can the brain represent an abstract idea independently of the concrete entities that comprise the content of the idea? (2) Do people represent concepts in an abstract manner only when explicitly required to do so, or are abstract relations also retrieved spontaneously? (3) What neural markers reliably predict differences in reasoning capacity between individuals? That is, do individuals whose brains represent abstract relations more readily also tend to have stronger reasoning skills, and/or to perceive meaningful connections that others miss? This project will identify the computational basis for abstract thought and reasoning, thereby creating an opportunity to refine artificial intelligence systems by providing them with more efficient learning mechanisms. This work will inform future research examining how children, and adults as lifelong learners, form representations of abstract concepts. This project integrates recent advances in multivariate fMRI, computational modeling, and behavioral methodology to discover the neurocognitive mechanisms underlying the representation of abstract relations. Research will systematically examine the neural bases of this representation, as well as the influence of task context and individual differences. First, behavioral priming and neural similarity measures, alongside metrics from a computational model of relational reasoning, will characterize the overlap in representation between pairs of concepts that are only abstractly related. Second, manipulation of task demands will determine whether the magnitude, location, and stability of neural representations vary with explicit cognitive instructions. Finally, development of a novel 'neural score' metric will determine neural markers of individual differences in relational reasoning.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.

推理概念组之间的关系的能力——关系推理——产生了抽象思维，并推动了人类在科学和技术方面的一些最伟大的成就。虽然先前的研究已经确定了大脑中关系推理发生的位置，但该项目通过解决大脑如何表示抽象关系来推动研究领域。具体来说，该项目旨在解决三个关键问题：(1)大脑能否独立于构成该概念内容的具体实体来代表一个抽象概念？(2)人们是否只在明确要求时才以抽象的方式表示概念，或者抽象关系也会自发地检索？(3)哪些神经标记可以可靠地预测个体之间推理能力的差异？也就是说，那些大脑更容易表现抽象关系的人是否也倾向于拥有更强的推理能力，和/或察觉到其他人错过的有意义的联系？该项目将确定抽象思维和推理的计算基础，从而通过为人工智能系统提供更有效的学习机制，创造一个改进人工智能系统的机会。这项工作将为未来的研究提供信息，研究儿童和成人作为终身学习者如何形成抽象概念的表征。该项目整合了多元功能磁共振成像、计算建模和行为方法论的最新进展，以发现抽象关系表征背后的神经认知机制。研究将系统地检查这种表征的神经基础，以及任务背景和个体差异的影响。首先，行为启动和神经相似性测量，以及关系推理计算模型的度量，将表征仅抽象相关的概念对之间的重叠表示。第二，任务需求的操纵将决定神经表征的大小、位置和稳定性是否随明确的认知指令而变化。最后，开发一种新的“神经评分”度量将确定关系推理中个体差异的神经标记。该奖项反映了美国国家科学基金会的法定使命，并通过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，被认为值得支持。

项目成果

Relational and lexical similarity in analogical reasoning and recognition memory: Behavioral evidence and computational evaluation

• DOI: 10.1016/j.cogpsych.2023.101550
• 发表时间: 2023-01
• 期刊: Cognitive Psychology
• 影响因子: 2.6
• 作者: Nicholas Ichien;Katherine L. Alfred;Sophia Baia;David J. M. Kraemer;K. Holyoak;S. Bunge;Hongjing Lu