While learning, people do retain some self-knowledge of how they remember, what they are likely to forget, and how they should reinforce what they have learned. That finding comes from research conducted by Billur Gorgulu, a PhD Candidate with the Department of Economics. A behavioural economist, Gorgulu studies how people make decisions and the factors that influence decision-making. Behavioural economics is differentiated from other branches of the discipline by the use of laboratory experiments to test theoretical models of how people will make choices given a particular situation or set of conditions.
Working out of the Toronto Experimental Economics Lab (TEEL) at Max Gluskin House, Gorgulu first developed a novel theoretical model of memory and learning that accommodates established findings from psychological research. She then conducted a series of experiments to test the predictions of the model, examining how people perceive the limits of their ability to remember information and the decisions they make about how much to study from those perceptions. The results of those experiments are now outlined in Optimal Learning When Forgetting, Gorgulu’s job market paper.
For the paper, Gorgulu explored whether individuals make deliberate decisions about how much effort to invest in retaining information, especially when they anticipate forgetting material as time passes. She then designed a lab experiment in which participants chose how much time to invest in reviewing information before taking an incentivized test. Her results show that people expect to forget more information as time passes and they respond to that expectation by allocating more effort.
“What’s new about my research is that I treat memory as endogenous, a quality that comes from within each person rather than something fixed or that is shaped by outside influences,” she said. “There hasn’t been much behavioral or experimental research directly examining how people make decisions about memory. Most existing models acknowledge that memory is limited, and that this limitation can influence decision-making, but they don’t really explore where that limitation comes from. My research asks whether memory itself is shaped by individual choices, and the results suggest that people do manage their memory effortfully and deliberately.”
Like most PhD students, Gorgulu spent years as a teaching assistant, and she is already thinking about how her study results combine with her teaching experience. The combination will influence how she designs courses and other learning experiences as her career progresses.
“Teaching similar concepts across different levels, from first-year undergraduates to PhD students, helped me understand how students with different backgrounds absorb information. My research on learning and memory has had a direct impact on how I think about teaching. I’ve read a lot about how students retain information over the long term, about how different learning schedules and strategies affect memory retention. We don’t want students to just cram for the final exam and forget everything afterward.”
This year, while she prepares for her thesis defense and job interviews, Gorgulu has dedicated herself supporting experimental research practices at the University of Toronto.
“I’ve been working as the lab manager of TEEL,” she said. “I handle the administrative side of the lab, including recruiting participants for our pool and supporting the implementation of experiments. That includes setting up sessions, managing technical logistics, and helping researchers run their studies smoothly.”
The combination of research, teaching and administrative experience will all be required as Gorgulu moves forward with her work as its implications are evident both inside and outside the classroom.
“Billur’s research integrates rigorous theory with carefully designed experiments,” explained Professor Yoram Halevy, Gorgulu’s thesis supervisor. “Her job-market paper studies how limited memory shapes optimal learning, developing a psychology and education-informed model and then testing its predictions in the lab. This agenda has implications for economics, education, and AI—including the design of more human-like large language model (LLM) based agents.”
Return to the Department of Economics website.
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