GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction

2026-05-19

AI Summary

Existing datasets struggle to support coupled analysis of affect across individual, interpersonal, and group levels in collaborative settings, and often suffer from fragmented, misaligned multimodal signals. To address this gap, this work introduces a high-ecological-validity multimodal dataset comprising synchronized physiological, eye-tracking, audio, continuous self-reported affect, personality traits, and task performance data from 10 four-person groups (40 participants total) engaged in four distinct collaborative tasks. All signals are temporally aligned and organized following a BIDS-inspired structure with Croissant metadata specifications. The dataset achieves 91% and 98% coverage for physiological and eye-tracking signals, respectively, and includes validation via affect manipulation checks. For the first time, it integrates a three-level analytical framework and provides leave-one-group-out cross-validation baselines alongside 15 reproducible benchmark tasks, offering a standardized, high-coverage resource for group affect research.

Abstract

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.

Problem

• affective computing
• multimodal dataset
• group interaction
• collaborative tasks
• social signal processing

Innovation

• multimodal dataset
• group affect
• collaborative interaction
• time-aligned biosignals
• benchmarkable targets