构建了40人分10组的多模态互动数据集，支持个体、人际与群体层面的情感分析。
GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction
- 采集四人小组在四种真实任务中的生理、眼动、音频等多模态数据
- 覆盖91%生理信号窗口和98%眼动数据，任务有效性经情感操控检验
- 提供15个可评估目标，适合研究群体协作中情绪动态的学者使用
现有情感计算、社交信号处理及会议数据集虽涵盖人际互动的部分内容，但很少支持将共处群体中的情感视为个体、人际与群体层面耦合过程的分析。所需信号（个体生理、眼动、音频、自评、任务结果、人格特质）通常分散在不同数据集传统中。本文提出GroupAffect-4，一个包含40名参与者、分10组、完成四个生态化协作任务（信息整合、协商、创意生成、公共品博弈）的多模态数据集。每位参与者佩戴腕戴生理传感器、眼动追踪眼镜及近距麦克风；实验全程记录连续情感自评、任务后问卷、任务结果及大五人格评分，并统一时间对齐。数据集覆盖超过91%的生理信号窗口和98%的眼动窗口，协商环节的情感操控检验证实任务有效性。定义了15个可基准化的分析目标，涵盖个体状态、人际特质与群体动态三个层次，并报告留一组外的可行性基线，确立评估范围。数据集采用受BIDS启发的结构，附带Croissant元数据、数据表、每会话质量报告及开放处理脚本。代码与处理脚本见https://github.com/meisamjam/GroupAffect-4；数据集公开存档于https://zenodo.org/records/20037847。
原文摘要 · Abstract (English)
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.
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