NCS-FO: Uncovering the cognitive and neural fingerprints that make each of us unique

试图理解支持心智能力的大脑系统的努力通常假设所有人都有一个共同的认知和神经“模板”，只是人与人之间存在细微和随机的差异。然而，许多经典的研究表明，不同群体的心智和大脑存在很大差异。左撇子和右撇子的语言依赖于不同的大脑系统。那些在阅读方面有困难的学生与那些阅读流畅的学生相比，表现出了不同的神经连接模式。数学专家甚至比大学本科生更能看出简单整数之间的不同关系。该项目将结合联合收割机新的计算见解，功能性脑成像和脑刺激来表征支持感知，语言，社会认知和高阶思维的心理和神经结构的变化。这项工作有可能影响人类健康和脑损伤的恢复模式，典型和非典型人群的教育方法，以及其他可以从发现模式化变异中受益的科学领域。科学目标包括开发新的理论和算法，用于在行为和神经数据中发现信号，并使用这些方法来识别个体在心理表征结构和相应的神经基础上的共性和差异。对于大脑和行为，结构估计需要大量数据，而单个参与者生成的数据通常是嘈杂的。为了解决这个问题，经典的方法将每个参与者的数据视为从一个群体的所有成员共同的基础系统中抽取的样本。实际上，个体差异被视为噪音。相反，我们的方法假设个体在产生数据的心理和神经结构上可能存在显着差异，但这种差异的结构方式有助于信号发现。该项目将在结构估计的计算方法中形式化这些想法，建立模型样本复杂性的界限，并开发实用算法，将该方法应用于行为和脑成像数据。与此计算流并行，研究人员将收集行为数据，功能性大脑图像和大脑刺激数据，用于挖掘认知的不同方面的几项任务。这些实验，结合新的计算方法，将通过实验确定人们在心理和神经表征结构上的差异，这可能会改变我们对大脑产生思想的不同方式的基本理解。 该项目由理解神经和认知系统（NCS）计划的综合战略资助，该计划由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）和社会，行为，经济科学（SBE）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

Efforts to understand the brain systems that support mental abilities often assume that all people share a common cognitive and neural “template” that differs only subtly and randomly from person to person. Yet many classic studies have shown substantial variation in both mind and brain across different groups. Language relies on different brain systems for left handers compared to right handers. Students who struggle with reading show different patterns of neural connectivity than do fluent readers. Math experts see different kinds of relationships amongst even simple integers than do college undergraduates. This project will combine new computational insights, functional brain imaging, and brain stimulation to characterize variability in the mental and neural structures that support perception, language, social cognition, and high-order thought. This work has potential to impact human health and patterns of recovery from brain damage, approaches to education for typical and atypical populations, and other fields of science that can benefit from discovery of patterned variation.The scientific goals involve developing new theory and algorithms for signal discovery in behavioral and neural data, and using these methods to identify commonalities and differences across individuals in the structure of their mental representations and the corresponding neural underpinnings. For both brain and behavior, structure estimation requires a lot of data, and data generated by a single participant is typically noisy. To address this, classic approaches treat data from each participant as a sample drawn from an underlying system common to all members of a population. In effect, individual differences are treated as noise. Our approach instead assumes that individuals can differ significantly in the mental and neural structures that generate data, but that such variation is structured in ways that can aid in signal-discovery. This project will formalize these ideas in a computational approach to structure estimation, establish bounds on sample complexity for the model, and develop practical algorithms for applying the approach to behavioral and brain imaging data. In parallel with this computational stream, the investigators will collect behavioral data, functional brain images, and brain-stimulation data for several tasks tapping different aspects of cognition. These experiments, combined with the new computational methods, will experimentally determine how people differ in the structure of their mental and neural representations—potentially changing our basic understanding of the different ways brains can give rise to minds. This project is funded by the Integrated Strategies for Understanding Neural and Cognitive Systems (NCS) program, which is 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).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.