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

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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.