What is the computational objective of imagination? While classical interpretations suggest imagination is useful for maximizing rewards, recent findings challenge this view. In this study, we propose that imagination serves to access an internal world model (IWM) and use psychological network analysis to explore IWMs in humans and large language models (LLMs). Specifically, we assessed imagination vividness ratings using two questionnaires and constructed imagination networks from these reports. Imagination networks from human groups showed correlations between different centrality measures, including expected influence, strength, and closeness. However, imagination networks from LLMs showed a lack of clustering and lower correlations between centrality measures under different prompts and conversational memory conditions. Together, these results indicate a lack of similarity between IWMs in human and LLM agents. Overall, our study offers a novel method for comparing internally-generated representations in humans and AI, providing insights for developing human-like imagination in artificial intelligence.

翻译：想象的计算目标是什么？经典解释认为想象有助于最大化奖励，但近期研究对此提出挑战。本研究提出想象的功能在于访问内部世界模型（IWM），并运用心理网络分析方法探究人类与大型语言模型（LLM）中的IWM。具体而言，我们通过两份问卷评估想象生动性评分，并基于这些报告构建想象网络。人类群体的想象网络在不同中心性度量（包括预期影响力、强度与接近度）间呈现相关性。然而，LLM生成的想象网络在不同提示与会话记忆条件下均表现出聚类缺失及中心性度量间较低的相关性。这些结果表明人类与LLM智能体的IWM缺乏相似性。总体而言，本研究提供了一种比较人类与人工智能内部生成表征的新方法，为开发类人想象的人工智能提供了理论洞见。