CAREER: On the Neural and Mechanistic Bases of Higher-order Cognition

职业：高阶认知的神经和机械基础

• 批准号: 1847603
• 负责人: Brandon Turner
• 金额: $ 80万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1847603&HistoricalAwards=false
• 关键词: CAREER; Neural; Mechanistic; Bases; Higher

This project aims to explore the neural basis of how higher-order cognitive processes facilitate a representation of our environment. To date, the vast majority of decision-making research uses relatively simple stimuli to systematically relate independent variables within an experiment to collected dependent variables such as response time, EEG, or fMRI measures. However, the decisions that we make in our everyday lives are significantly more complicated. We exist in environments where resource and reward structures change dynamically on a daily, even momentary, basis. In these environments, cognitive control is necessary to successfully adjust our goals and representations to match the dynamics of our environment. Data from the experiments conducted during this project, as well the analytic methods, will be used to create a comprehensive syllabus in the emerging field of model-based cognitive neuroscience. The materials, data, and code for performing the analyses will also be made available to the broader scientific community, so that other universities may adopt the course sequence.Cognitive control is essential to successfully adjust our goals and representations to match dynamic changes in the environment. It is argued that our understanding of the mechanisms by which control is carried out in the brain is limited by the lack of statistical methodology to (1) rigorously investigate cognitive models equipped with mechanisms of control, (2) relate mechanisms of control to brain data, and (3) justify the inclusion of complicated mechanisms by assessing model fit and complexity. In this proposal, I hope to use hierarchical (Bayesian) joint modeling techniques to address each of these three limitations. The hierarchical component of this research is specifically designed to assess individual variation in the degree to which cognitive control is executed. The joint modeling component of this research is designed to link neurophysiological measures (e.g., EEG, fMRI) to cognitive theories about how control is executed. Finally, to fully examine the degree to which stochastic mechanisms of control may best describe individual subject data, I will build on existing techniques I have developed for approximating these processes through likelihood-free Bayesian methodology. Each of these components will be used collectively to systematically examine the neural basis of control in various cognitive tasks, and specifically the degree to which individuals are capable of successfully executing said control.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.

该项目旨在探索高阶认知过程如何促进我们对环境的表征的神经基础。迄今为止，绝大多数决策研究使用相对简单的刺激来系统地将实验中的自变量与收集的因变量（如反应时间、脑电图或功能磁共振成像测量）联系起来。然而，我们在日常生活中所做的决定要复杂得多。我们所处的环境中，资源和奖励结构每天甚至是瞬间都在动态变化。在这些环境中，认知控制对于成功调整我们的目标和表征以适应环境的动态是必要的。在这个项目中进行的实验数据，以及分析方法，将用于创建基于模型的认知神经科学这一新兴领域的综合教学大纲。执行分析的材料、数据和代码也将提供给更广泛的科学界，以便其他大学可以采用课程顺序。认知控制对于成功调整我们的目标和表征以适应环境的动态变化至关重要。本文认为，由于缺乏统计方法，我们对控制在大脑中进行的机制的理解受到限制(1)严格调查配备控制机制的认知模型，(2)将控制机制与大脑数据联系起来，以及(3)通过评估模型拟合和复杂性来证明包含复杂机制的合理性。在这个建议中，我希望使用分层（贝叶斯）联合建模技术来解决这三个限制。本研究的层次结构部分是专门设计来评估个体在认知控制执行程度上的差异。本研究的联合建模部分旨在将神经生理学测量（如脑电图、功能磁共振成像）与关于控制如何执行的认知理论联系起来。最后，为了充分研究随机控制机制在多大程度上可以最好地描述个体主体数据，我将利用我已经开发的现有技术，通过无似然贝叶斯方法来近似这些过程。这些组成部分中的每一个都将被用于系统地检查各种认知任务中控制的神经基础，特别是个体能够成功执行所述控制的程度。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

Real-time Adaptive Design Optimization Within Functional MRI Experiments

功能 MRI 实验中的实时自适应设计优化

• DOI: 10.1007/s42113-020-00079-7
• 发表时间: 2020
• 期刊: Computational Brain & Behavior
• 作者: Bahg, Giwon;Sederberg, Per B.;Myung, Jay I.;Li, Xiangrui;Pitt, Mark A.;Lu, Zhong-Lin;Turner, Brandon M.
• 通讯作者: Turner, Brandon M.

Gaussian process linking functions for mind, brain, and behavior

• DOI: 10.1073/pnas.1912342117
• 发表时间: 2020-11-24
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Bahg, Giwon;Evans, Daniel G.;Turner, Brandon M.
• 通讯作者: Turner, Brandon M.

A regularization method for linking brain and behavior.

连接大脑和行为的正则化方法。

• DOI: 10.1037/met0000387
• 发表时间: 2022
• 期刊: Psychological Methods
• 影响因子: 7
• 作者: Kang, Inhan;Yi, Woojong;Turner, Brandon M.
• 通讯作者: Turner, Brandon M.

Distributed Neural Systems Support Flexible Attention Updating during Category Learning

分布式神经系统支持类别学习期间灵活的注意力更新

• DOI: 10.1162/jocn_a_01882
• 发表时间: 2022
• 期刊: Journal of Cognitive Neuroscience
• 影响因子: 3.2
• 作者: Weichart, Emily R.;Evans, Daniel G.;Galdo, Matthew;Bahg, Giwon;Turner, Brandon M.
• 通讯作者: Turner, Brandon M.

As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

• DOI: 10.1037/rev0000381
• 发表时间: 2022-10
• 期刊: PSYCHOLOGICAL REVIEW
• 影响因子: 5.4
• 作者: Weichart, Emily Ruth;Galdo, Matthew;Sloutsky, Vladimir;Turner, Brandon
• 通讯作者: Turner, Brandon