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Does the DMPFC Prioritize Consolidating Unpredictable Social Information at Rest?

Poster Session C - Sunday, March 30, 2025, 5:00 – 7:00 pm EDT, Back Bay Ballroom/Republic Ballroom

Courtney Jimenez1, Meghan Meyer1; 1Columbia University

The quality of our social connections determine the quality of our lives. Given that our social worlds are incredibly rich, complex, and dynamic, navigating them proves a computationally intensive task. It is possible that unpredictable social content is prioritized for learning via social consolidation mechanisms at rest in the DMPFC and this relates to subjective social connection. This is consistent with prior work that shows prediction errors generated during a reinforcement learning task ‘tag’ content for consolidation and relate to future behavior (Momennejad et al., 2018). Further, other work suggests social information is prioritized for learning via temporal dynamics of pattern reinstatement in the DMPFC. In this study, participants will view naturalistic video clips from a reality television series with characters who show variability in the predictability of their behavior while undergoing fMRI. After each clip, participants will be asked if they want to be socially connected to a specific character. Participants will rest and then be presented with a surprise memory test and rate subjective social connection. A reinforcement learning model will be fit to participants’ choice behavior to generate prediction errors for the task. Neural pattern reinstatement methods will be used to investigate consolidation mechanisms of unpredictable and predictable social events during post-encoding rest. We hypothesize that unpredictable social information will be consolidated earlier than predictable social information at rest in the DMPFC. Further, we hypothesize that neural pattern reinstatement for unpredictable social information in the DMPFC at rest may meaningfully predict changes in subjective social connection ratings.

Topic Area: LONG-TERM MEMORY: Episodic

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March 29–April 1  |  2025

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