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Publication Detail
Distinct replay signatures for prospective decision-making and memory preservation
  • Publication Type:
    Journal article
  • Authors:
    Wimmer GE, Liu Y, McNamee DC, Dolan RJ
  • Publisher:
    Proceedings of the National Academy of Sciences
  • Publication date:
  • Journal:
    Proceedings of the National Academy of Sciences
  • Volume:
  • Issue:
  • Article number:
  • Status:
  • Print ISSN:
  • Language:
  • Keywords:
    planning, memory, replay, hippocampus, decision-making
  • Notes:
    Copyright © 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Theories of neural replay propose that it supports a range of functions, most prominently planning and memory consolidation. Here, we test the hypothesis that distinct signatures of replay in the same task are related to model-based decision-making (“planning”) and memory preservation. We designed a reward learning task wherein participants utilized structure knowledge for model-based evaluation, while at the same time had to maintain knowledge of two independent and randomly alternating task environments. Using magnetoencephalography and multivariate analysis, we first identified temporally compressed sequential reactivation, or replay, both prior to choice and following reward feedback. Before choice, prospective replay strength was enhanced for the current task-relevant environment when a model-based planning strategy was beneficial. Following reward receipt, and consistent with a memory preservation role, replay for the alternative distal task environment was enhanced as a function of decreasing recency of experience with that environment. Critically, these planning and memory preservation relationships were selective to pre-choice and post-feedback periods, respectively. Our results provide support for key theoretical proposals regarding the functional role of replay and demonstrate that the relative strength of planning and memory-related signals are modulated by ongoing computational and task demands.
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