MultiCAT: Multimodal Communication Annotations for Teams

We present a rich, multimodal dataset consisting of data from 40 teams of three humans conducting simulated urban search-and-rescue (SAR) missions in a Minecraft-based testbed, collected for the Theory of Mind-based Cognitive Architecture for Teams (ToMCAT) project. Modalities include two kinds of brain scan data—functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), as well as skin conductance, heart rate, eye tracking, face images, spoken dialog audio data with automatic speech recognition (ASR) transcriptions, game screenshots, gameplay data, game performance data, demographic data, and self-report questionnaires. Each team undergoes up to six consecutive phases: three behavioral tasks, one mission training session, and two collaborative SAR missions. As time-synchronized multimodal data collected under a variety of circumstances, this dataset will support studying a large variety of research questions on topics including teamwork, coordination, plan recognition, affective computing, physiological linkage, entrainment, and dialog understanding. We provide an initial public release of the de-identified data, along with analyses illustrating the utility of this dataset to both computer scientists and social scientists.

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Recommended citation: Adarsh Pyarelal, John M Culnan, Ayesha Qamar, Meghavarshini Krishnaswamy, Yuwei Wang, Cheonkam Jeong, Chen Chen, Md Messal Monem Miah, Shahriar Hormozi, Jonathan Tong, and Ruihong Huang. 2025. MultiCAT: Multimodal Communication Annotations for Teams. In Findings of the Association for Computational Linguistics: NAACL 2025 , pages 1077–1111, Albuquerque, New Mexico. Association for Computational Linguistics.