Breaking the bias: integrating physiological and self-reported data to improve UX researchers' accuracy and empathy

IF 4.9 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports Pub Date : 2025-06-12 DOI:10.1016/j.chbr.2025.100723

Pascal Snow , Alejandra Ruiz-Segura , Pierre-Majorique Léger , Sylvain Sénécal , Constantinos Coursaris , Romain Pourchon , Sarah Cosby , Ariane Beauchesne

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引用次数: 0

Abstract

User experience (UX) research aims to optimize digital products by tackling users' needs and motivations. Traditional self-reported measures, while cost-effective and accessible, are limited by cognitive biases and fail to capture the multidimensional nature of emotions. This exploratory study investigates whether integrating physiological data alongside self-reported measures during usability testing enhances UX researchers' inferential accuracy and perceived empathy. Specifically, it examines whether visualizations of users' physiological trends and self-reported scales lead to improvements in a researcher's ability to identify usability issues and foster empathy.

Twenty-two UX researchers were randomly assigned to two conditions: one received combined self-reported and physiological data visualizations, while the other received only self-reported data. Participants analyzed simulated user journeys, identified usability challenges, and completed a survey on empathy in design. Results showed that participants in the physiological and self-reported data condition demonstrated significantly higher inferential accuracy (63 % vs 47 %, p < 0.10) and greater empathy across both cognitive and emotional dimensions (p < 0.05).

Findings suggest that combining self-reported and physiological measures leads to richer insights into the users' emotional journeys, improving decision-making in UX research contexts. Visually mapping emotional valence and arousal data in real time enabled researchers to link usability challenges to user experiences with precision, facilitating targeted follow-up. Simplified data visualizations proved effective in enhancing workflow efficiency and fostering empathy.

This study underscores the value of multimethod approaches in UX testing, advocating for tools that integrate and represent diverse data sources. Future research should explore scalability and application in naturalistic settings to advance UX practices further.

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打破偏见：整合生理和自我报告的数据，以提高UX研究人员的准确性和同理心

用户体验（UX）研究旨在通过解决用户的需求和动机来优化数字产品。传统的自我报告测量方法虽然成本效益高且容易获得，但受到认知偏见的限制，无法捕捉情绪的多维性。这项探索性研究探讨了在可用性测试中整合生理数据和自我报告的测量是否能提高UX研究人员的推理准确性和感知同理心。具体来说，它检验了用户生理趋势的可视化和自我报告的量表是否能提高研究人员识别可用性问题和培养同理心的能力。22名UX研究人员被随机分配到两种情况：一种情况下接受自我报告和生理数据可视化的结合，而另一种情况下只接受自我报告的数据。参与者分析了模拟的用户旅程，确定了可用性挑战，并完成了一项关于设计中的同理心的调查。结果显示，生理和自我报告数据条件下的参与者表现出显著更高的推断准确性(63% vs 47%, p <；0.10)以及在认知和情感维度上更强的同理心(p <；0.05)。研究结果表明，将自我报告和生理测量相结合，可以更深入地了解用户的情感历程，从而改善用户体验研究背景下的决策。实时绘制情感效价和唤醒数据，使研究人员能够精确地将可用性挑战与用户体验联系起来，从而促进有针对性的随访。简化的数据可视化被证明在提高工作流程效率和培养同理心方面是有效的。这项研究强调了用户体验测试中多方法方法的价值，提倡使用集成和表示不同数据源的工具。未来的研究应该探索在自然环境中的可扩展性和应用，以进一步推进用户体验实践。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers in human behavior reports Psychology (General)

CiteScore

7.80

自引率

0.00%

发文量