Author ORCID Identifier

https://orcid.org/0000-0003-0205-3088

Date of Award

Fall 2026

Document Type

Thesis (Ph.D.)

Department or Program

Psychological & Brain Sciences

First Advisor

Jeremy R. Manning

Abstract

A growing body of recent work highlights the importance of studying learning and memory in “naturalistic” contexts that reflect how these processes function in our day-to-day lives. Yet the incredible complexity of the world around us, and of the idiosyncratic ways we learn from and remember it, make characterizing the dynamics of learning and memory for real-world experiences challenging. I present a computational framework that leverages the embedding representations of language models to cast experiences and memories as high-dimensional trajectories unfolding through a common representation space, enabling them to be directly related and compared via their latent semantic geometries. I apply this framework first to characterize how we preserve and distort the dynamic content of a real-world experience when encoding it into memory, and identify networks of brain regions that underlie these transformations. I then extend this framework to characterize how our stored memories are further transformed over time, and what it means for a memory of an experience to “fade.” Finally, I adapt this framework to model individuals’ conceptual knowledge and how it changes as they learn, and develop an approach for mapping out a learner’s knowledge that can predict their success on individual assessment questions. Collectively, my work demonstrates the insights that may be gained by considering the geometric form of our experiences, knowledge, and memories, and offers a generalizable approach that researchers, teachers, and other practitioners can leverage to create tools and techniques that more meaningfully capture learning and memory in real-world scenarios.

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