This Research Topic aims to explore the intricate relationship between hippocampal function and reinforcement learning within the context of cognitive neuroscience. It seeks to decode the neural mechanisms by which the hippocampus contributes to the acquisition, processing, and integration of reinforcement signals, shedding light on its role in decision-making, memory consolidation, and adaptive behavior.

The primary objective is to elucidate how the hippocampus participates in the encoding and retrieval of experiences related to rewards and punishments, thereby influencing the decision-making process. By investigating the neural underpinnings of reinforcement learning, we aim to enhance our understanding of the broader cognitive and behavioral functions guided by this learning process.

This Research Topic holds significant relevance in cognitive neuroscience, as it bridges the gap between two fundamental aspects of brain function: learning and memory processes represented by the hippocampus and the adaptive decision-making guided by reinforcement learning. The findings can potentially contribute to advancements in understanding cognitive disorders and in designing interventions for conditions where reinforcement learning mechanisms are impaired.

• Neural Circuitry: Examining the specific neural pathways and interactions within the hippocampus that are crucial for reinforcement learning.

• Memory Consolidation: Investigating how the hippocampus encodes reward-related memories and how they influence subsequent behavior.

• Temporal Dynamics: Exploring the time course of hippocampal involvement in reinforcement learning over short and long timescales.

• Cognitive Flexibility: Investigating how hippocampal damage or dysfunction impacts adaptive decision-making and cognitive flexibility.

• Computational Models: Developing and refining computational models that simulate the integration of reinforcement signals within the hippocampus.

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Frontiers in Computational Neuroscience

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