Date of Award

Spring 5-15-2023

Author's School

Graduate School of Arts and Sciences

Author's Department


Degree Name

Doctor of Philosophy (PhD)

Degree Type



People can learn and adopt relaxed or focused control settings based on the likelihood of conflict at specific inducer locations, then flexibly retrieve those control settings at nearby unbiased diagnostic locations. Importantly, people can learn that multiple distinct contexts predict conflict, such as specific locations or regions of space (e.g., all locations in the upper left). In realistic visual scenes, meaningful boundaries that demarcate objects or areas of space serve as an important signal that the area inside the boundary is distinct from the area outside the boundary. Recent research (Colvett & Bugg, 2022) indicated a role for meaningful boundaries in the learning and retrieval of control settings. The current study tested two questions regarding meaningful boundaries. First, will people learn about the area within a meaningful boundary and adopt a control setting associated with likelihood of conflict at all locations in that meaningful area? Second, will a meaningful boundary separating an inducer location from a diagnostic location disrupt retrieval of a control setting? Experiments 1 and 2 used simple black and white maps where familiar state borders grouped locations within boundaries. However, those meaningful boundaries did not impact the learning and retrieval of control settings. Experiments 3, 4, and 5 used realistic satellite images to make the difference between meaningful areas more salient. Some evidence emerged that meaningful boundaries affected the learning of control settings at inducer locations, but no evidence emerged that transfer was enhanced by presenting diagnostic locations in the same meaningful area as inducer locations. Taken together, these findings have important implications for the flexibility of learned control settings, the role of meaningful boundaries, and the relative dominance of various contexts when participants can learn from multiple sources of information.


English (en)

Chair and Committee

Julie M. Bugg

Committee Members

Richard A. Abrams, Andrew J. Aschenbrenner, Todd S. Braver, Wouter Kool,