GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913146301579264 |
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| author | Seikavandi, Meisam Jamshidi Modica, Alice Obara, Anna Shaffi, Shan Ahmed Narcizo, Fabricio Batista Ignatenko, Tanya Vucurevich, Ted Haddad, Karim Barratt, Daniel Overholt, Daniel Boldt, Jesper Bunsow Burelli, Paolo Dittberner, Andrew Burke |
| author_facet | Seikavandi, Meisam Jamshidi Modica, Alice Obara, Anna Shaffi, Shan Ahmed Narcizo, Fabricio Batista Ignatenko, Tanya Vucurevich, Ted Haddad, Karim Barratt, Daniel Overholt, Daniel Boldt, Jesper Bunsow Burelli, Paolo Dittberner, Andrew Burke |
| contents | Existing affective-computing, social-signal-processing, and meeting corpora capture important parts of human interaction, but they rarely support analysis of affect in co-located groups as a coupled individual, interpersonal, and group-level process. The required signals (per-participant physiology, eye movement, audio, self-report, task outcomes, and personality) are usually fragmented across separate dataset traditions. We introduce GroupAffect-4, a multimodal corpus of 40 participants in 10 four-person groups, each completing four ecologically varied collaborative tasks spanning information pooling, negotiation, idea generation, and a public-goods game. Each participant is instrumented with a wrist-worn physiology sensor, eye-tracking glasses, and a close-talk microphone; sessions include continuous affect self-reports, post-task questionnaires, task outcomes, and Big-Five personality scores, all time-aligned to a shared clock. The dataset covers over 91% of expected physiology windows and 98% of eye-tracking windows, with strong task validity confirmed by a clear affective manipulation check across the negotiation block. We define fifteen benchmarkable targets spanning three analysis levels -- within-person state, between-person traits, and group dynamics -- and report leave-one-group-out feasibility baselines establishing the dataset's evaluative scope. GroupAffect-4 is released with a BIDS-inspired structure, Croissant metadata, a datasheet, per-session quality reports, and open processing scripts. Code and processing scripts are available at https://github.com/meisamjam/GroupAffect-4; the dataset is publicly archived at https://zenodo.org/records/20037847. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19765 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction Seikavandi, Meisam Jamshidi Modica, Alice Obara, Anna Shaffi, Shan Ahmed Narcizo, Fabricio Batista Ignatenko, Tanya Vucurevich, Ted Haddad, Karim Barratt, Daniel Overholt, Daniel Boldt, Jesper Bunsow Burelli, Paolo Dittberner, Andrew Burke Artificial Intelligence Databases Existing affective-computing, social-signal-processing, and meeting corpora capture important parts of human interaction, but they rarely support analysis of affect in co-located groups as a coupled individual, interpersonal, and group-level process. The required signals (per-participant physiology, eye movement, audio, self-report, task outcomes, and personality) are usually fragmented across separate dataset traditions. We introduce GroupAffect-4, a multimodal corpus of 40 participants in 10 four-person groups, each completing four ecologically varied collaborative tasks spanning information pooling, negotiation, idea generation, and a public-goods game. Each participant is instrumented with a wrist-worn physiology sensor, eye-tracking glasses, and a close-talk microphone; sessions include continuous affect self-reports, post-task questionnaires, task outcomes, and Big-Five personality scores, all time-aligned to a shared clock. The dataset covers over 91% of expected physiology windows and 98% of eye-tracking windows, with strong task validity confirmed by a clear affective manipulation check across the negotiation block. We define fifteen benchmarkable targets spanning three analysis levels -- within-person state, between-person traits, and group dynamics -- and report leave-one-group-out feasibility baselines establishing the dataset's evaluative scope. GroupAffect-4 is released with a BIDS-inspired structure, Croissant metadata, a datasheet, per-session quality reports, and open processing scripts. Code and processing scripts are available at https://github.com/meisamjam/GroupAffect-4; the dataset is publicly archived at https://zenodo.org/records/20037847. |
| title | GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction |
| topic | Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2605.19765 |