The computational role of imagination remains debated. While classical accounts emphasize reward maximization, emerging evidence suggests imagination serves a broader function: accessing internal world models (IWMs). Here, we employ psychological network analysis to compare IWMs in humans and large language models (LLMs) through imagination vividness ratings. Using the Vividness of Visual Imagery Questionnaire (VVIQ-2) and Plymouth Sensory Imagery Questionnaire (PSIQ), we construct imagination networks from three human populations (Florida, Poland, London; N=2,743) and six LLM variants in two conversation conditions. Human imagination networks demonstrate robust correlations across centrality measures (expected influence, strength, closeness) and consistent clustering patterns, indicating shared structural organization of IWMs across populations. In contrast, LLM-derived networks show minimal clustering and weak centrality correlations, even when manipulating conversational memory. These systematic differences persist across environmental scenes (VVIQ-2) and sensory modalities (PSIQ), revealing fundamental disparities between human and artificial world models. Our network-based approach provides a quantitative framework for comparing internally-generated representations across cognitive agents, with implications for developing human-like imagination in artificial intelligence systems.

翻译：想象的计算功能仍存在争议。经典理论强调奖励最大化，而新兴证据表明想象具有更广泛的功能：访问内部世界模型（IWMs）。本文采用心理网络分析方法，通过想象生动性评分比较人类与大型语言模型（LLMs）的IWMs。利用视觉想象生动性问卷（VVIQ-2）和普利茅斯感官想象问卷（PSIQ），我们构建了来自三个人类群体（佛罗里达、波兰、伦敦；N=2,743）和六种LLM变体在两种对话条件下的想象网络。人类想象网络在中心性指标（预期影响、强度、接近度）间表现出稳健的相关性，并具有一致的聚类模式，表明不同人群间IWMs存在共享的结构组织。相比之下，LLM衍生的网络即使通过操纵对话记忆，仍呈现最小化聚类和弱中心性相关性。这些系统性差异在环境场景（VVIQ-2）和感官模态（PSIQ）中持续存在，揭示了人类与人工世界模型之间的根本差异。我们基于网络的方法为比较认知智能体内部生成的表征提供了量化框架，对开发类人想象的人工智能系统具有重要启示。