Artificial theory of mind in large language models: Evidence, conceptualization, and challenges

doi:10.3724/SP.J.1042.2025.2027

Abstract

Abstract:

In recent years, the rapid development of artificial intelligence (AI) has continuously reshaped our understanding of its capability boundaries. Evaluating the theory of mind capabilities of large language models (LLMs) has received significant attention within the research community. Recent studies suggest that LLMs can successfully complete tasks traditionally used to assess theory of mind in humans. However, controversial questions remain: Do LLMs possess theory of mind? What are the essential differences between artificial theory of mind and human theory of mind? Therefore, this systematic review synthesizes the performance of LLMs on theory of mind tasks, reveals essential differences in the internal processes between human theory of mind and artificial theory of mind, refines the conceptual definition of artificial theory of mind, and outlines key challenges in this field.

Specifically, we systematically synthesize research on artificial theory of mind from the objects of evaluation and the characteristics of tasks. Following the developmental sequence of core theory of mind sub-abilities in humans, we evaluate the task performance of GPT-4. Results indicate that GPT-4 can consistently pass false-belief tasks and various higher-order theory of mind tasks, suggesting a simulation of human-like theory of mind performance. Nevertheless, recognizing that behavioral task accuracy may be insufficient to reflect true capability, we specifically examine cases in which model performance is limited. Our analysis identifies critical limiting factors including external factors (e.g., limitations in test items, prompt design and human baselines) and intrinsic limitations of LLMs (e.g., hallucinations, hyperconservatism, commonsense errors, heuristics or spurious correlations, and spurious causal inference). This suggests that performance fluctuations may not stem from a lack of artificial theory of mind. Consequently, by integrating GPT-4's high accuracy with its intrinsic and extrinsic factors limiting its performance, we demonstrate that GPT-4 has developed artificial theory of mind that is similar in performance to human theory of mind.

The advanced capabilities of LLMs underscore the importance of studying the internal processes of theory of mind. Multiple lines of evidence suggest fundamental differences in the internal processes between human theory of mind and artificial theory of mind. To fully distinguish them, we conduct a comparison of the neural foundations and developmental factors supporting both. We highlight that their neural mechanisms exhibit distinct complexity across multiple dimensions, and the factors influencing theory of mind acquisition and development differ between humans and LLMs. This reveals the root cause of their internal process differences. Based on this, we define artificial theory of mind as: the simulation of human theory of mind performance exhibited by LLMs during text generation, achieved through recognizing and matching statistical patterns in the text after receiving a theory of mind task prompts.

However, as an emerging field, existing research faces three main challenges: lack of standardized evaluation protocols, unclear mechanisms of artificial theory of mind, and theory of mind alignment issues. Regarding evaluation, issues in experimental design, prompt design, and scoring methods can lead to divergent results. Regarding mechanisms, while internal processes are key to distinguishing between theory of mind and artificial theory of mind, current research has neither fully elucidated these processes nor sufficiently addressed how humans attribute mental states to LLMs. Regarding alignment, LLMs simulate human theory of mind performance without achieving genuine theory of mind reasoning. Based on this, we propose corresponding research directions.

In conclusion, this study reveals that LLMs possess an artificial theory of mind that is similar in performance to human theory of mind but different in internal processes, refines its conceptual definition, and clarifies key field challenges. It identifies critical issues and delineates future research directions, offering a foundation for leveraging artificial theory of mind research to advance our understanding of human theory of mind emergence and internal processes.

Key words: large language models, theory of mind, artificial theory of mind, artificial intelligence

CLC Number:

B842

DU Chuanchen, ZHENG Yuanxia, GUO Qianqian, LIU Guoxiong. Artificial theory of mind in large language models: Evidence, conceptualization, and challenges[J]. Advances in Psychological Science, 2025, 33(12): 2027-2042.

Figures/Tables 4

References 120

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文献	LLMs	人类被试	定制任务	模态	ATOMS心理状态
文献	LLMs	人类被试	定制任务	模态	一阶信念	高阶信念	行动意图	交流意图	愿望	情感	知识	感知	非字面交际
Sap et al., 2022	GPT-3			文本	√
Trott et al., 2023	GPT-3	√		文本	√
Van Duijn et al., 2023	GPT-3, 3.5, 4; Llama等	√		文本	√	√		√					√
Shapira et al., 2023	GPT3			文本	√								√
He et al., 2023	GPT-3.5, 4		√	文本	√	√
Kim et al., 2023	GPT-3.5, 4; Llama-2等	√	√	文本	√	√					√
Li et al., 2023	GPT-3.5, 4		√	文本	√	√					√	√
Sileo & Lernould, 2023	GPT-3, 3.5, 4	√	√	文本	√	√
Ma et al., 2023	GPT-3.5, 4			文本	√	√
Ullman, 2023	GPT-3.5			文本	√
Gandhi et al., 2023	GPT-3.5, 4; Claude等		√	文本	√	√			√			√
Brunet-Gouet et al., 2023	GPT-3.5, 4			文本	√	√		√					√
Jones et al., 2024	GPT-3	√		文本	√	√		√		√	√		√
Verma et al., 2024	GPT-3.5, 4	√	√	视觉; 文本		√					√	√
Wilf et al., 2024	GPT-3.5, 4; Llama-2等		√	文本	√	√			√			√
Shapira et al., 2024	GPT-3.5, 4; Flan等		√	文本	√	√	√			√			√
Strachan et al., 2024	GPT-3.5, 4; Llama-2	√		文本	√	√		√					√
Chen et al., 2024	GPT-4	√	√	文本	√	√		√	√	√	√		√
Jin et al., 2024	GPT-3.5, 4; Llama-2等	√	√	视觉; 文本	√	√	√
Xu et al., 2024	GPT-3.5, 4; Mixtral等		√	文本	√	√	√		√	√		√
Yongsatianchot et al., 2024	GPT-4; Claude3等		√	文本	√	√		√	√	√
Amirizaniani et al., 2024	GPT-4; Llama-2等	√	√	文本				√		√
Attanasio et al., 2024	GPT-3.5, 4	√		文本						√			√
Kosinski, 2024	GPT-1, 2, 3, 3.5, 4等			文本	√
Nickel et al., 2024	Llama-2; Mixtral等		√	文本	√	√
Ünlütabak & Bal, 2025	GPT-3.5, 4			文本	√	√

文献	LLMs	人类被试	定制任务	模态	ATOMS心理状态
文献	LLMs	人类被试	定制任务	模态	一阶信念	高阶信念	行动意图	交流意图	愿望	情感	知识	感知	非字面交际
Sap et al., 2022	GPT-3			文本	√
Trott et al., 2023	GPT-3	√		文本	√
Van Duijn et al., 2023	GPT-3, 3.5, 4; Llama等	√		文本	√	√		√					√
Shapira et al., 2023	GPT3			文本	√								√
He et al., 2023	GPT-3.5, 4		√	文本	√	√
Kim et al., 2023	GPT-3.5, 4; Llama-2等	√	√	文本	√	√					√
Li et al., 2023	GPT-3.5, 4		√	文本	√	√					√	√
Sileo & Lernould, 2023	GPT-3, 3.5, 4	√	√	文本	√	√
Ma et al., 2023	GPT-3.5, 4			文本	√	√
Ullman, 2023	GPT-3.5			文本	√
Gandhi et al., 2023	GPT-3.5, 4; Claude等		√	文本	√	√			√			√
Brunet-Gouet et al., 2023	GPT-3.5, 4			文本	√	√		√					√
Jones et al., 2024	GPT-3	√		文本	√	√		√		√	√		√
Verma et al., 2024	GPT-3.5, 4	√	√	视觉; 文本		√					√	√
Wilf et al., 2024	GPT-3.5, 4; Llama-2等		√	文本	√	√			√			√
Shapira et al., 2024	GPT-3.5, 4; Flan等		√	文本	√	√	√			√			√
Strachan et al., 2024	GPT-3.5, 4; Llama-2	√		文本	√	√		√					√
Chen et al., 2024	GPT-4	√	√	文本	√	√		√	√	√	√		√
Jin et al., 2024	GPT-3.5, 4; Llama-2等	√	√	视觉; 文本	√	√	√
Xu et al., 2024	GPT-3.5, 4; Mixtral等		√	文本	√	√	√		√	√		√
Yongsatianchot et al., 2024	GPT-4; Claude3等		√	文本	√	√		√	√	√
Amirizaniani et al., 2024	GPT-4; Llama-2等	√	√	文本				√		√
Attanasio et al., 2024	GPT-3.5, 4	√		文本						√			√
Kosinski, 2024	GPT-1, 2, 3, 3.5, 4等			文本	√
Nickel et al., 2024	Llama-2; Mixtral等		√	文本	√	√
Ünlütabak & Bal, 2025	GPT-3.5, 4			文本	√	√

固有局限	表现形式	形成原因
幻觉 (hallucinations)	答案包含无法从任务叙述中推断出的或与叙述相矛盾的信息; 编造不符合事实的细节和意图以补全推理过程	源于模型获取能力过程中的多方面因素, 贯穿数据层面、训练过程和推理阶段
超保守主义 (hyperconservatism)	过于谨慎而拒绝对任务中角色的信念做出推断	模型难以自发地减少推理中的不确定性; 避免提供错误答案
常识错误 (commonsense errors)	生成违背常识的回答, 或不具备某些常识	模型对常识的掌握与其在复杂推理中有效运用常识的能力割裂
捷径/伪相关 (heuristics/spurious correlations)	利用任务描述中简单的、表面的模式解决任务	模型倾向于生成续写内容; 表现出过度配合性, 即假设所有输入细节都重要
时间无知 (lack of temporal information)	对任务中主人公行为序列的时序理解存在偏差	模型尚未形成对时序关系的实质性理解
虚假因果推断 (spurious causal inference)	仅基于问题的表层模式做出错误或误导性的因果推断	模型难以把握文本间统计关联背后的深层逻辑
长时程上下文 (long-horizon contexts)	回答问题时忽略距离过远的上文内容	模型处理长文本的能力有限
推理深度不足 (insufficient reasoning depth)	跳过一些必要的推理步骤, 将高阶问题转化为低阶问题	训练数据中包含较多的简单模式; 模型难以有效保持并整合多步骤信息

固有局限	表现形式	形成原因
幻觉 (hallucinations)	答案包含无法从任务叙述中推断出的或与叙述相矛盾的信息; 编造不符合事实的细节和意图以补全推理过程	源于模型获取能力过程中的多方面因素, 贯穿数据层面、训练过程和推理阶段
超保守主义 (hyperconservatism)	过于谨慎而拒绝对任务中角色的信念做出推断	模型难以自发地减少推理中的不确定性; 避免提供错误答案
常识错误 (commonsense errors)	生成违背常识的回答, 或不具备某些常识	模型对常识的掌握与其在复杂推理中有效运用常识的能力割裂
捷径/伪相关 (heuristics/spurious correlations)	利用任务描述中简单的、表面的模式解决任务	模型倾向于生成续写内容; 表现出过度配合性, 即假设所有输入细节都重要
时间无知 (lack of temporal information)	对任务中主人公行为序列的时序理解存在偏差	模型尚未形成对时序关系的实质性理解
虚假因果推断 (spurious causal inference)	仅基于问题的表层模式做出错误或误导性的因果推断	模型难以把握文本间统计关联背后的深层逻辑
长时程上下文 (long-horizon contexts)	回答问题时忽略距离过远的上文内容	模型处理长文本的能力有限
推理深度不足 (insufficient reasoning depth)	跳过一些必要的推理步骤, 将高阶问题转化为低阶问题	训练数据中包含较多的简单模式; 模型难以有效保持并整合多步骤信息

提示框架	简要叙述	文献
思维链 (chain of thought)	通过向模型提供包含输入、中间推理步骤和输出的示例, 提示模型生成类似的中间步骤, 从而逐步解决问题	Chen et al., 2024; Shapira et al., 2024
显式信念表征 (explicit belief representations)	提示模型实时存储与任务相关的关键信念, 并在推理过程中整合心理状态	Amirizaniani et al., 2024; Li et al., 2023
模拟心理理论 (simulated theory of mind)	提示模型首先根据目标角色已知的信息对上下文进行筛选, 然后再回答关于其心理状态的问题	Wilf et al., 2024; Xu et al., 2024
自我问答 (self-ask prompting)	提示模型在回答初始问题前, 自省式地生成并回答后续问题, 直至得出确定结论	Press et al., 2023; Zhou et al., 2023
预见与反思 (foresee and reflect)	提示模型先基于观察预测潜在未来事件, 再反思不同行动选择如何帮助角色应对潜在挑战, 最终确定最优方案	Zhou et al., 2023
符号心理理论 (symbolic theory of mind)	提示模型进行显式符号表征, 并对任务中多角色信念状态进行多级推理	Sclar et al., 2023
时间心理理论 (time theory of mind)	为任务文本添加时序维度来构建时空体系, 提示模型依据各角色在时间线上感知的事件构建时空信念状态链	Hou et al., 2024
离散世界模型 (discrete world models)	将任务文本分割成多个序列块, 在每块后插入要求描述角色当前信念的提示, 最后提示模型生成答案	Huang et al., 2024