ORCID
https://orcid.org/0000-0003-1701-0988
Date of Award
9-5-2023
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Cognitive control allows us to select actions based on flexible internal goals, yet its neural basis is still poorly understood. Computational models propose a crucial role for neural coding of contextual rules, correlates of which are identifiable through representational similarity analysis of non-invasive electroencephalographic (EEG) recordings. But surprisingly, this analysis approach has not been applied to the most well-studied test of cognitive control, the color-word Stroop task. In this study, we use representational similarity analysis of scalp EEG to investigate the neural coding dynamics of flexible selection in an optimized Stroop task. We show that contextual rule coding is boosted during stimulus encoding, and the magnitude of this boost predicts more efficient selection of task-relevant information on single trials, as indicated by response-time measures. Pre-stimulus neural coding of rules, evident in spatial topographies of spectral power, also predicts performance, suggesting the importance of proactive control mechanisms during extended maintenance of internal goals. Finally, behavioral evidence suggests that cognitive control mediates conflict history biases, but the role of proactive rule coding in this process remains unclear. Our findings indicate that representational similarity analysis can provide a window into the neural coding dynamics within the paradigmatic Stroop task, suggesting promise for new insight into mechanisms of cognitive control.
Language
English (en)
Chair and Committee
Todd Braver
Recommended Citation
Freund, Michael C., "Neural Coding Dynamics of Cognitive Control as Measured by Representational Similarity Analyses of Electroencephalographic Signals" (2023). Arts & Sciences Electronic Theses and Dissertations. 3157.
https://openscholarship.wustl.edu/art_sci_etds/3157