Personality-Driven Modulation of Episodic Memory in Artificial Agents

Abstract

Episodic memory (EM) may be key to intelligent systems, assisting them in daily tasks and personalizing interactions. Yet, existing EM implementations lack the flexibility of human EM, limiting their potential. Indeed, while in humans, EM storage and retrieval are shaped by personality, which influences the meaning and feelings associated with memories, the robotic literature does not provide models that explore how personality affects EM processes or its impact on agent behavior. To address this gap, we propose a flexible EM model, where episodes are recombined through a Large Language Model (LLM). We have integrated the model with a cognitive architecture for robotic personality, capable of managing Conscientiousness, Extroversion, and Agreeableness (CEA) traits in robots and digital humans. This ultimately provides a tool for examining how robotic personality influences memory encoding, storage, and retrieval. The system has been evaluated in a controlled environment, involving a digital human and ten simulated personas in multiple interaction scenarios. We compared seven conditions, including a no-personality baseline and the six extreme poles of the CEA traits, drawing valuable insights into the influence of personality on EM storage, task completion, information encoding, and EM significance.