The computational role of imagination remains debated. While classical accounts emphasize reward maximization, emerging evidence suggests it accesses internal world models (IWMs). We employ psychological network analysis to compare IWMs in humans and large language models (LLMs) via imagination vividness ratings, distinguishing offline world models (persistent memory structures accessed independent of immediate goals) from online models (task-specific representations). Analyzing 2,743 humans across three populations and six LLM variants, we find human imagination networks exhibit robust structural consistency, with high centrality correlations and aligned clustering. LLMs show minimal clustering and weak correlations with human networks, even with conversational memory, across environmental and sensory contexts. These differences highlight disparities in how biological and artificial systems organize internal representations. Our framework offers quantitative metrics for evaluating offline world models in cognitive agents.

翻译：想象的计算作用仍存争议。传统理论强调奖励最大化，而新兴证据表明其通过访问内部世界模型实现。我们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的内部世界模型，区分离线世界模型（独立于即时目标访问的持久记忆结构）与在线模型（任务特定表征）。通过分析三个群体共2,743名人类被试及六种LLM变体，发现人类想象网络具有稳健的结构一致性，表现出高中心性相关与对齐的聚类特征。LLM则呈现最小化聚类模式，其与人类网络的相关性微弱——即使在具备对话记忆的情况下，跨环境与感官语境均保持此差异。这些差异揭示了生物系统与人工系统在组织内部表征方面的本质区别。本框架为评估认知智能体中的离线世界模型提供了量化指标体系。