Date of Graduation
Doctor of Philosophy in Psychology (PhD)
Second Committee Member
brain activation, cognitive neuroscience, ERPs, N2, neural mechanisms, self-regulation, source-space analysis
Self-regulation is the cognitive process of controlling our thoughts and behaviors to be aligned with our goals. This process is used in many different contexts and has been associated with contributions from several brain regions. This research aimed to investigate differences in four prefrontal areas of the brain while participants applied four different self-regulation strategies. We recorded EEG while participants (N = 132) performed three tasks which engaged each of the four self-regulation strategies: the AX-CPT task engaged proactive and reactive control, the Go/Nogo task engaged inhibitory control, and the hybrid Flanker Global/Local task engaged the resolution of response conflict. This study used the N2 event-related potential (ERP) to capture the neural activity related to each self-regulation strategy and then source-space analyses (eLORETA) were conducted to estimate the activity in four regions of interest (ROIs): dorsolateral (DL) PFC, ventrolateral (VL) PFC, ventromedial (VM) PFC, and dorsal ACC. The dorsal ACC was most activated for proactive control, indicative of performance monitoring. The right VLPFC was indicative of conflict adaptation in reactive control and response conflict, and indicative of motor inhibition in inhibitory control. DLPFC was most active for goal maintenance during proactive and reactive control. The left VLPFC was most active during reactive control, indicating its importance in memory of goal information. These results are in line with much of the previous literature. VMPFC did not show any differences across the strategies likely due to the lack of emotional context. This study builds on the extant literature by directly comparing neural processes across four different self-regulation strategies within one large sample, highlighting the fact that various self-regulation strategies recruit unique patterns of activation and thus future research should not collapse across these strategies.
Long, S. M. (2021). An EEG Source-Space Analysis of the Neural Correlates Underlying Self-Regulation. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3953