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.

翻译：现有情感计算、社会信号处理及会议语料库虽捕捉到人类互动的关键部分，却极少支持将共处群体的情感分析视为耦合的个体、人际与群体层面过程。所需信号（每位参与者的生理指标、眼动数据、音频采集、自我报告、任务结果及人格特征）通常分散于不同数据集传统中。我们提出GroupAffect-4——一个包含10组四人团队（共40名参与者）的多模态语料库，每组完成四项生态多样性协作任务（信息整合、协商谈判、创意生成与公共物品博弈）。每位参与者配备腕戴式生理传感器、眼动追踪眼镜及近讲麦克风；实验过程包含连续情感自我报告、任务后问卷、任务结果与大五人格评分，所有数据均通过共享时钟进行时间对齐。该数据集覆盖预期生理数据窗口的91%以上及眼动追踪窗口的98%以上，通过协商模块清晰的情感操纵检验验证了极强的任务效度。我们定义了覆盖三个分析层级（个体内状态、个体间特质及群体动态）的十五个可基准测试目标，并报告了留一组交叉验证可行性基线以确立该数据集的评估范围。GroupAffect-4采用BIDS启发式结构、Croissant元数据、数据表、逐会话质量报告及开源处理脚本发布。代码与处理脚本见https://github.com/meisamjam/GroupAffect-4；数据集已归档至https://zenodo.org/records/20037847。