Freaky: performing hybrid human-machine emotion
Freaky: performing hybrid human-machine emotion
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Freaky：表演混合人机情感
DOI：
10.1145/2598510.2600879
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Phoebe Sengers
中科院分区：
文献类型：
--
作者：
L. Leahu;Phoebe Sengers
本文探讨的可能性，使用统计分类的生理信号的情感类别作为资源的开放式人类的情绪解释。通常情况下，对情感的设计研究假设计算机可以客观地识别用户的情感，或者情感是完全主观的，因此完全依赖于人类的解释。通过借鉴女权主义的表演性概念，我们解释了如何将计算表征和人类行为者视为共同构建的情感。通过Freaky的案例研究，一个系统，使用这样的情感模型，以支持人类的解释，我们演示了如何机器学习模型的影响可以构建和纳入系统设计的开放式用户解释的影响。从用户部署的定性结果表明，表演的方法来建模情感是可能的。因此，我们证明了表演理论的潜力，以生成新的计算和设计实践，支持混合人机制定的情绪。
This paper explores the possibility of using statistical classification of physiological signals into emotion categories as a resource for open-ended human interpretation of emotion. Typically, design studies for affect assume either that it is possible for computers to objectively identify users' emotions, or that emotion is completely subjective and thus rely solely on human interpretation. By drawing on the feminist concept of performativity, we explain how to conceive of computational representations and human actors as co-constructing emotions. Through a case study of Freaky, a system that uses such models of emotion to sup-port human interpretation, we demonstrate how machine learning models of affect can be constructed and incorporated in systems designed for open-ended user interpretation of affect. Qualitative results from a user deployment show that a performative approach to modeling emotion is possible. We thus demonstrate the potential of performative theories to be generative of new computational and design practices that support hybrid human-machine enactments of emotion.