Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language
This addresses the gap in Theory of Mind benchmarks for nonverbal communication, which is incremental as it extends existing ToM evaluation to a new modality.
The paper tackles the problem of evaluating AI systems' ability to interpret mental states from nonverbal cues, presenting Motion2Mind, a video dataset with 222 cue types and 397 mind states, and finds that current AI struggles significantly with detection and over-interpretation compared to humans.
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.