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Aesthetics, affect and user preference - Finding objective measures for subjective experiences

机译:美学,情感和用户偏好-寻找主观体验的客观方法

摘要

Objectives of the studyThe Human-Computer Interaction (HCI) field has long concentrated on easily measured variables such as effectiveness, adoption and ease of use to study technology usage. In order to study user preference in particular, the inclusion of more socio-cognitive variables such aesthetics and emotions is necessary. However, these subjective experiences are intrinsically harder to measure: using subjective measures like questionnaires give results that are colored by cognitive processing, whereas subjective evaluations are formed instantly after exposure. The objective is to find what the effect of aesthetics and emotions are to user preference, and to test whether eye movement could provide an objective measure to support and validate subjective measures.Academic background and methodologyBased on previous literature and studies, a model predicting user preference is developed. For eye movement tracking, modern abstract and representative art are used as test material. It is hypothesized that user preference is predicted by aesthetics, fixations (static eye movement), valence, arousal and dominance (emotions). The direct effect of these variables as well as the mediating effect of emotions is studied. The direct effect is tested by regression analysis, and the results are used for modify the model accordingly. The results are verified by path analysis which is also used to test the mediation effect of emotions and group differences of abstract and representative images.Findings and conclusionsThe research hypotheses were mainly confirmed by the study. It was found that aesthetics, valence and arousal explain and predict preference. Only dominance did not significantly predict preference. Aesthetic and affect reactions are formed instantly after viewing an object, they are involuntary in nature and the effect of these rapid evaluations is long lasting. Measuring such swift decisions is challenging, but it was found that eye movement and fixations in particular predict preference. In practical terms, it means fixations can be used along with self-reported measures to corroborate subjective evaluations.
机译:研究目标人机交互(HCI)领域长期以来一直集中在易于测量的变量上,例如有效性,采用率和易用性,以研究技术使用情况。为了特别研究用户的偏爱,必须包含更多的社会认知变量,例如美学和情感。但是,这些主观体验本质上难以衡量:使用诸如问卷之类的主观衡量方法,其结果会被认知处理所着色,而主观评估是在接触后立即形成的。目的是发现美学和情感对用户偏好的影响,并测试眼球运动是否可以提供客观的措施来支持和验证主观措施。学术背景和方法基于先前的文献和研究,一个预测用户偏好的模型被开发。对于眼动追踪,现代抽象和代表性艺术被用作测试材料。假设用户的喜好是通过美学,注视(静态眼动),效价,唤醒和优势(情感)来预测的。研究了这些变量的直接作用以及情绪的中介作用。通过回归分析测试直接效果,并将结果用于相应地修改模型。通过路径分析验证了结果,该路径分析还用于检验情绪的调解效果以及抽象图像和代表图像的群体差异。研究结果和结论本研究假设主要得到了证实。发现美学,价和唤醒能解释和预测偏好。只有支配地位不能显着预测偏好。审美和情感反应是在观察对象后立即形成的,它们本质上是非自愿的,这些快速评估的效果是持久的。测量这样的迅速决定是具有挑战性的,但是发现眼球运动和注视尤其可以预测偏好。实际上,这意味着可以将注视与自我报告的措施一起使用以证实主观评估。

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    Lipponen Satu;

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  • 年度 2013
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