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

这个项目旨在探索高阶认知过程如何促进对我们环境的表征的神经基础。到目前为止，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联系起来，如反应时间、脑电或功能磁共振测量。然而，我们在日常生活中做出的决定要复杂得多。我们所处的环境中，资源和报酬结构每天、甚至是瞬间都在动态变化。在这些环境中，认知控制是成功调整我们的目标和表征以匹配我们环境的动态所必需的。本项目期间进行的实验数据以及分析方法将被用来在基于模型的认知神经科学这一新兴领域创建一个全面的教学大纲。进行分析的材料、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。有人认为，我们对大脑中执行控制的机制的理解受到以下统计学方法的限制：(1)严格研究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)通过评估模型的适合性和复杂性来证明包含复杂机制的合理性。在本提案中，我希望使用分层(贝叶斯)联合建模技术来解决这三个限制。这项研究的层级部分专门设计用来评估认知控制执行程度上的个体差异。这项研究的联合建模部分旨在将神经生理学测量(例如，EEG、fMRI)与有关如何执行控制的认知理论联系起来。最后，为了充分检验随机控制机制最好地描述个体主题数据的程度，我将建立在我开发的现有技术的基础上，通过无似然贝叶斯方法近似这些过程。这些部分中的每一个都将被用来系统地检查各种认知任务中控制的神经基础，特别是个人能够成功执行控制的程度。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.