Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911686725730304 |
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| author | Lee, Seungbeen Jeong, Jinhong Kim, Donghyun Son, Yejin Yu, Youngjae |
| author_facet | Lee, Seungbeen Jeong, Jinhong Kim, Donghyun Son, Yejin Yu, Youngjae |
| contents | Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theory of Mind (ToM) benchmarks have primarily focused on false-belief tasks and reasoning with asymmetric information, they overlook other mental states beyond belief and the rich tapestry of human nonverbal communication. We present Motion2Mind, a framework for evaluating the ToM capabilities of machines in interpreting NVCs. Leveraging an expert-curated body-language reference as a proxy knowledge base, we build Motion2Mind, a carefully curated video dataset with fine-grained nonverbal cue annotations paired with manually verified psychological interpretations. It encompasses 222 types of nonverbal cues and 397 mind states. Our evaluation reveals that current AI systems struggle significantly with NVC interpretation, exhibiting not only a substantial performance gap in Detection, as well as patterns of over-interpretation in Explanation compared to human annotators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language Lee, Seungbeen Jeong, Jinhong Kim, Donghyun Son, Yejin Yu, Youngjae Computation and Language Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theory of Mind (ToM) benchmarks have primarily focused on false-belief tasks and reasoning with asymmetric information, they overlook other mental states beyond belief and the rich tapestry of human nonverbal communication. We present Motion2Mind, a framework for evaluating the ToM capabilities of machines in interpreting NVCs. Leveraging an expert-curated body-language reference as a proxy knowledge base, we build Motion2Mind, a carefully curated video dataset with fine-grained nonverbal cue annotations paired with manually verified psychological interpretations. It encompasses 222 types of nonverbal cues and 397 mind states. Our evaluation reveals that current AI systems struggle significantly with NVC interpretation, exhibiting not only a substantial performance gap in Detection, as well as patterns of over-interpretation in Explanation compared to human annotators. |
| title | Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.15887 |