做情感计算、社交信号处理或小组协作研究的团队终于有了一个覆盖个体、人际和群体三层次的高质量多模态数据集，数据完整性和任务效度都经过验证，可以直接用于训练和评估模型。
现有情感计算与社交信号处理数据集难以支持对同地小组中个体、人际和群体层面的情感耦合分析。研究者推出了GroupAffect-4，包含10组共40名参与者在四种协作任务（信息汇集、谈判、创意生成、公共物品博弈）中的多模态数据。每位参与者佩戴腕式生理传感器、眼动追踪眼镜和近讲麦克风，并收集连续情感自评、任务后问卷、任务结果和大五人格评分，所有数据通过共享时钟对齐。数据集覆盖91%以上的预期生理窗口和98%的眼动窗口，谈判任务的情感操纵检验验证了任务有效性。它定义了15个基准目标，涵盖个体内状态、个体间特质和群体动态三个分析层次，并提供了留一组交叉验证的可行性基线。数据集以BIDS风格结构、Croissant元数据、数据表、每会话质量报告和开源处理脚本发布。
GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction
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.