Theoretical models of episodic memory have proposed that retrieval depends on interactions between the hippocampus and neocortex, where hippocampal reinstatement of item-context associations drives neocortical reinstatement of item information. Here, we simultaneously recorded intracranial EEG from hippocampus and lateral temporal cortex (LTC) of epilepsy patients who performed a virtual reality spatial navigation task. We extracted stimulus-specific representations of both item and item-context associations from the time-frequency patterns of activity in hippocampus and LTC. Our results revealed a double dissociation of representational reinstatement across time and space: an early reinstatement of item-context associations in hippocampus preceded a later reinstatement of item information in LTC. Importantly, reinstatement levels in hippocampus and LTC were correlated across trials, and the quality of LTC reinstatement was predicted by the magnitude of phase synchronization between hippocampus and LTC. These findings confirm that episodic memory retrieval in humans relies on coordinated representational interactions within a hippocampal-neocortical network.

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent’s reactive behaviors (pre-wired reflexes), along with an Adaptive Layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.