Understanding Changes in Hippocampal Representations by Measuring Memories with Natural Language Processing

PROJECT SUMMARY The hippocampus plays an essential role in encoding long-term episodic memories. However, because many of the experiences we encode share similar features (people, locations, objects), a critical challenge for the episodic memory system is to prevent interference or confusion between these memories. Recent human neuroimaging studies have revealed that highly similar events can trigger a “repulsion” of corresponding representations within the hippocampus such that nearly identical events are associated with markedly different activity patterns. Critically, there is evidence that hippocampal repulsion is adaptive in that it is associated with reduced memory interference. However, a fundamental open question is whether or how hippocampal repulsion impacts the actual contents of memories. Addressing this question requires methods for precisely characterizing potentially subtle differences in behavioral and neural expressions of memory content. In this proposal, I will leverage Natural Language Processing (NLP) algorithms to transform measures of verbal recall into text embeddings (i.e., numerical vectors) within a multidimensional semantic space. These text embeddings will allow me to quantify the similarity of memories for highly similar natural scene images. Additionally, I will gain new training in advanced fMRI methods and computational analyses that will allow me to characterize and relate behavioral expressions of memory to corresponding representations within the hippocampus. My central hypothesis is that repulsion of hippocampal representations will be associated with the exaggeration of differences between similar scene stimuli when they are verbally recalled. This hypothesis and the feasibility of my approach is supported by a preliminary study I have conducted which validates that NLP methods are sensitive to subtle distortions in how similar scene images are remembered. In Aim 1, using NLP methods and a behavioral memory paradigm, I will test the hypothesis that distortions in memory content are explained by a targeted “movement” of competing memories away from each other in a high-dimensional semantic space. In Aim 2, I will test the hypothesis that changes in memory content (measured by NLP methods) are predicted by the degree of repulsion of hippocampal representations. In addition to supporting my training with new neuroimaging and computational methods, this project will yield important new insight into how the hippocampus resolves interference between similar memories. Moreover, the specific combination of techniques and approaches that I will employ have the potential to open up new avenues of research in the field of episodic memory. In summary, this research will support my long-term objective of developing innovative methods to understand how the hippocampus supports the efficient storage of episodic memories.