Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task

TL;DR

This paper introduces an RNN-based reinforcement learning model that mimics neural activity and decision-making behaviors, providing insights into how episodic memory influences choices and learning speed.

Contribution

The study develops a novel RNN-Actor-Critic framework trained with RL that models neural and behavioral features, and explores how episodic memory retrieval affects decision efficiency.

Findings

Salient episodic memories shorten deliberation time.

Retrieval of salient events enhances learning speed.

Model reproduces neural activity patterns observed in animals.

Abstract

Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks. A key problem is that they fail to record all the relevant neurons in the animal brain. To alleviate this problem, we develop an RNN-based Actor-Critic framework, which is trained through reinforcement learning (RL) to solve two tasks analogous to the monkeys' decision-making tasks. The trained model is capable of reproducing some features of neural activities recorded from animal brain, or some behavior properties exhibited in animal experiments, suggesting that it can serve as a computational platform to explore other cognitive functions. Furthermore, we conduct behavioral experiments on our framework, trying to explore an open question in neuroscience: which episodic memory in the hippocampus should be selected to ultimately govern future decisions. We find that the retrieval of salient events sampled from episodic memories can effectively shorten deliberation time than common events in the decision-making process. The results indicate that salient events stored in the hippocampus could be prioritized to propagate reward information, and thus allow decision-makers to learn a strategy faster.