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Vector-Based Analysis of Competitive Memory Biases in Verbal Recall

Poster Session D - Monday, March 9, 2026, 8:00 – 10:00 am PDT, Fairview/Kitsilano Ballroom

Anisha Babu¹ (), James Murray¹, Brice Kuhl¹; ¹University of Oregon

Behavioral studies of episodic memory interference traditionally focus on how similarity between events can reduce the probability of those events being successfully remembered (i.e., forgetting). However, similarity can also produce subtle biases in memory even when events are otherwise successfully remembered (Chanales et al., 2021; Drasher et al., 2022). To date, these biases have been measured using highly controlled stimuli that vary on a single dimension and memory tests that specifically orient subjects to relevant dimensions (e.g., color). To extend the generalizability of these findings, we developed an approach for measuring similarity-induced biases in verbal recall for complex, naturalistic scene images. The premise for our approach is that, using natural language processing, verbal memories can be translated to a high-dimensional space and the relationship between similar memories can then be quantified using vector geometry. In our study (planned n = 180), participants will learn associations between 12 face images (cues) and 12 scene images (associates). Scene images are drawn from 6 semantic categories (e.g., libraries), resulting in two images per category. Each subject studies the same ‘target’ image from each category along with one of 5 possible ‘competitor’ images (counterbalanced across subjects). After learning, participants are prompted with each face (cue) to recall and verbally describe the associated scene image. The critical goal for this study is to apply vector geometry to the high-dimensional verbal recall space to determine whether target memory is systematically biased toward the competitor (attraction) or away from the competitor (repulsion).

Topic Area: LONG-TERM MEMORY: Episodic