Date of Award
Master of Arts (AM/MA)
Conflict-modulated cognitive control accounts posit that control processes adjust attention based on the probability of conflict associated with a given context (e.g., list of items, a particular item within a list, etc.). However, within these accounts, it is not yet fully understood how the control system learns about the probability of conflict. A specific question I address in the present research is how far back does the control system look to learn about the probability of conflict? In other words, what is the time scale of conflict learning for the control system? I use a statistical model recently developed by Aben et al. (2017) that captured the time scale of conflict learning for list-level control processes in a flanker task. The set of analyses I present shows that this model reliably captures the time scale of conflict learning for task-general, list-level control processes (Analysis 1 and Analysis 2). In addition, I also demonstrate that there are no differences in the time scale of conflict learning for differentially conflicting items within a list which are thought to engage item-level control (Analysis 3 and Analysis 4). I discuss potential reasons for the time scale patterns and the implications they may have for extant theories of cognitive control.
Chair and Committee
Todd Braver David Balota
Dey, Abhishek, "Learning from Past Conflict: Investigating the Time Scale of Conflict Learning for Cognitive Control Processes" (2019). Arts & Sciences Electronic Theses and Dissertations. 1755.