WoNoWa数据集:研究小组互动中的交互记忆系统

Béatrice Biancardi, Lou Maisonnave-Couterou, Pierrick Renault, Brian Ravenet, M. Mancini, G. Varni
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引用次数: 6

摘要

我们提出了WoNoWa,一个新的多模态数据集,用于协作任务中的小组交互。该数据集被明确设计为引出并随着时间的推移研究一个交互记忆系统(TMS),这是一个群体的突发状态,表征了这个群体关于“谁知道什么”的元知识。从收集的视听数据中提取的一套丰富的自动特征和手动注释可根据研究目的的要求提供。特征包括个体描述符(例如,位置,运动数量,言语活动)和群体描述符(例如,f形)。此外,参与者的自我评估是可用的。探索性分析的初步结果表明,WoNoWa设计允许小组开发在任务中增加的TMS。这些结果鼓励使用WoNoWa数据集来更好地理解行为模式和经颅磁刺激之间的关系,从而有助于提高群体表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The WoNoWa Dataset: Investigating the Transactive Memory System in Small Group Interactions
We present WoNoWa, a novel multi-modal dataset of small group interactions in collaborative tasks. The dataset is explicitly designed to elicit and to study over time a Transactive Memory System (TMS), a group's emergent state characterizing the group's meta-knowledge about "who knows what". A rich set of automatic features and manual annotations, extracted from the collected audio-visual data, is available on request for research purposes. Features include individual descriptors (e.g., position, Quantity of Motion, speech activity) and group descriptors (e.g., F-formations). Additionally, participants' self-assessments are available. Preliminary results from exploratory analyses show that the WoNoWa design allowed groups to develop a TMS that increased across the tasks. These results encourage the use of the WoNoWa dataset for a better understanding of the relationship between behavioural patterns and TMS, that in turn could help to improve group performance.
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