High-fidelity social learning via shared episodic memories can improve collaborative foraging

Published: 20 Oct 2023, Last Modified: 30 Nov 2023IMOL@NeurIPS2023EveryoneRevisionsBibTeX
Keywords: social learning, multi-agent systems, episodic control, reinforcement learning, collective foraging, cultural evolution
TL;DR: This study explores the role of episodic memory in social learning during collaborative foraging, revealing that high-fidelity social learning leads to efficient resource collection and that there's an optimal threshold for memory length
Abstract: Social learning, a cornerstone of cultural evolution, allows individuals to acquire knowledge by observing and imitating others. Central to its efficacy is episodic memory, which records specific behavioral sequences to facilitate learning. This study examines the interrelation between social learning and episodic memory in the context of collaborative foraging. Specifically, we examine how variations in the frequency and fidelity of social learning impact collaborative foraging, and how the length of behavioral sequences preserved in agents’ episodic memory modulates these factors. To this end, we deploy Sequential Episodic Control agents capable of sharing among them behavioral sequences stored in their episodic memories. Our findings indicate that high-frequency, high-fidelity social learning promotes more distributed and efficient resource collection, a benefit that remains consistent regardless of the length of the shared episodic memories. In contrast, low-fidelity social learning shows no advantages over non-social learning in terms of resource acquisition. In addition, storing and disseminating longer episodic memories contribute to enhanced performance up to a certain threshold, beyond which increased memory capacity does not yield further benefits. Our findings emphasize the crucial role of high-fidelity social learning in collaborative foraging, and illuminate the intricate relationship between episodic memory capacity and the quality and frequency of social learning. This work aims to highlight the potential of neuro-computational models like episodic control algorithms in understanding social learning and offers a new perspective for investigating the cognitive mechanisms underlying open-ended cultural evolution.
Submission Number: 12