Assessing mentalization in humans and large language models
Mentalization - the ability to infer others' beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social interactions. Large language models (LLMs) demonstrate behaviour consistent with humans on theory-of-mind tasks, yet whether these models can guide adaptive behaviour through mentalization is unknown. Here we use two economic games with cognitive computational modeling to uncover the latent strategies underlying mentalization in LLMs. We tested individual LLM agents across four model families, DeepSeek, GPT-4.1, GPT-5 and Gemini 2.0 Flash (N = 2,099), against opponents of varying sophistication and examined whether a prompting strategy design...
| Authors | Aamir Sohail, Xintong Zhong, Arkady Konovalov et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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