Hippocampal-cingulate dynamics in the human brain link reinforcement-learning and memory
- 1Rutgers Robert Wood Johnson Medical School
Abstract
Reinforcement learning (RL) models describe how reward computations shape our choices, but whether the same computations also shape memory in the human brain is unclear. To address this question, we combined multi-areal intracranial recordings with computational modeling of reward and memory in neurosurgical patients. Patients played a gambling task in which decisions yielded monetary rewards tied to trial-unique images, followed immediately by a recognition test for those images. Model-derived positive reward prediction errors (RPEs) predicted later memory for each image. During feedback, multivariate high-frequency activity revealed the spatiotemporal evolution of RPE representations across distributed prefrontal cortex. During subsequent recognition, however, only anterior cingulate cortex (ACC) transiently reinstated RPE representations, and successful recognition specifically associated with hippocampal reinstatement of these ACC representations. Hippocampal-cingulate theta synchrony scaled with RPE during recognition, alongside hippocampal theta decoding of upcoming memory choices. Thus, the RL computations that guide decision-making also shape memory through hippocampal-cingulate circuit dynamics in the human brain.
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BibTeX
@misc{qasim2026hippocampalcingulate,
title = {{Hippocampal-cingulate dynamics in the human brain link reinforcement-learning and memory}},
author = {S. E. Qasim and F. Panov and L. Nunez and A. E. Rhone and H. Kawasaki and C. Kovach and C. Garcia and B. Dlouhy and X. Gu and I. Saez},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.10.750374},
url = {https://www.biorxiv.org/content/10.64898/2026.09.10.750374v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Hippocampal-cingulate dynamics in the human brain link reinforcement-learning and memory
AU - S. E. Qasim
AU - F. Panov
AU - L. Nunez
AU - A. E. Rhone
AU - H. Kawasaki
AU - C. Kovach
AU - C. Garcia
AU - B. Dlouhy
AU - X. Gu
AU - I. Saez
PY - 2026
DA - 2026/09/10
PB - bioRxiv
DO - 10.64898/2026.09.10.750374
UR - https://www.biorxiv.org/content/10.64898/2026.09.10.750374v1
AB - Reinforcement learning (RL) models describe how reward computations shape our choices, but whether the same computations also shape memory in the human brain is unclear. To address this question, we combined multi-areal intracranial recordings with computational modeling of reward and memory in neurosurgical patients. Patients played a gambling task in which decisions yielded monetary rewards tied to trial-unique images, followed immediately by a recognition test for those images. Model-derived positive reward prediction errors (RPEs) predicted later memory for each image. During feedback, multivariate high-frequency activity revealed the spatiotemporal evolution of RPE representations across distributed prefrontal cortex. During subsequent recognition, however, only anterior cingulate cortex (ACC) transiently reinstated RPE representations, and successful recognition specifically associated with hippocampal reinstatement of these ACC representations. Hippocampal-cingulate theta synchrony scaled with RPE during recognition, alongside hippocampal theta decoding of upcoming memory choices. Thus, the RL computations that guide decision-making also shape memory through hippocampal-cingulate circuit dynamics in the human brain.
ER -