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首页> 外文会议>International Conference on Physiological Computing Systems >Quantifying Negative Affect Usability Testing to Observe the Effect of Negative Emotions on User Productivity Through the Use of Biosignals and OCC Theory
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Quantifying Negative Affect Usability Testing to Observe the Effect of Negative Emotions on User Productivity Through the Use of Biosignals and OCC Theory

机译：量化负面影响可用性测试以观察通过使用生物信息和OCC理论来观察负面情绪对用户生产力的影响

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摘要

Humans sometimes experience negative emotions caused by electronic devices that impede their task(s). User experience researchers have examined technology-caused negative affect by collecting task performance metrics, user feedback, and/or human physiological data like skin temperature or blood pressure for more insight. Much research has been done to determine the amount of negative affect produced by the humans during these events. However, these methods usually require the user to self-report their negative feelings through Likert scales, pressure-sensitive devices or other manual methods. Task performance measures have also been used in lieu of asking a user what they feel. In this research, we adapt OCC Theory for use with physiological data for quantifying negative affect in human-computer interactions, along with asking a person how they feel about an application. In addition, we observe how negative affect amounts impact task performance measures in a usability study by adding random system delays into an application to induce negative feelings. Results from this work showed productivity does not always degrade when negative feelings are experienced by a user. In addition, some types of negative affect may have the opposite effect and allow a user to increase their performance under the right conditions.
机译：人类有时会经历由阻碍他们任务的电子设备引起的负面情绪。用户体验研究人员通过收集任务性能指标，用户反馈和/或人的生理数据，如皮肤温度或血压进行更多的洞察力，检查了技术引起的负面影响。已经完成了许多研究以确定人类在这些事件期间产生的负面影响的量。然而，这些方法通常要求用户通过李特级，压敏设备或其他手动方法自我报告它们的负面情绪。任务绩效措施也被使用代替向用户询问他们的感受。在这项研究中，我们适应了用于量化负面影响的生理数据的OCC理论，以及询问人们对应用程序的感受。此外，我们通过将随机系统延迟添加到应用程序来诱导负面情绪的应用中，观察到负面影响如何影响任务性能措施。当用户经历负面情绪时，这项工作的结果表明生产率并不总是劣化。此外，某些类型的负面影响可能具有相反的效果，并允许用户在正确的条件下提高其性能。

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