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Recommended methods for conducting human factors experiments on the subjective evaluation of colour rendition
Lighting Research & Technology ( IF 2.5 ) Pub Date : 2021-06-02 , DOI: 10.1177/14771535211019864
M Royer 1, 2 , K Houser 1, 2 , D Durmus 1, 3 , T Esposito 4 , M Wei 5
Affiliation  

This article explores the best practices for conducting psychophysical experiments that investigate how colour rendition influences the perception of architectural environments. We offer guidance that covers all stages of research from preliminary development to publication, focusing especially on experiments that investigate qualities such as perceived naturalness, vividness, preference or acceptability in response to changes in the spectral power distribution of light sources. This article is intended to be a consolidated guide for researchers and reviewers of this type of research. Key recommendations include: (1) New work should be motivated by clearly expressed research questions and, when possible, explicit hypotheses that build on the existing body of knowledge, (2) visual stimuli comprising spectral power distributions and visual targets should be deliberately engineered to probe the research questions, (3) experiments should be designed to lessen potential biases, (4) reporting of experimental conditions and statistical analyses should be thorough and (5) Results should be contextual, resisting overgeneralization that cannot be supported by the data. Our motivation is to encourage high-quality research that is credible and discourage poor quality research that slows scientific progress and misuses resources.



中文翻译:

对色彩再现主观评价进行人因实验的推荐方法

本文探讨了进行心理物理学实验的最佳实践,这些实验研究色彩再现如何影响建筑环境的感知。我们提供涵盖从初步开发到出版的所有研究阶段的指导,特别侧重于研究诸如感知自然度、生动性、偏好或可接受性等质量的实验,以响应光源光谱功率分布的变化。本文旨在为此类研究的研究人员和审阅者提供综合指南。主要建议包括: (1) 新工作应以明确表达的研究问题为动力,并在可能的情况下,建立在现有知识体系基础上的明确假设,(2) 应有意设计包含光谱功率分布和视觉目标的视觉刺激来探索研究问题,(3) 应设计实验以减少潜在偏差,(4) 报告实验条件和统计分析应彻底和 (5) ) 结果应该是上下文的,抵制数据不能支持的过度概括。我们的动机是鼓励可信的高质量研究,并阻止会减缓科学进步和滥用资源的低质量研究。抵制数据无法支持的过度概括。我们的动机是鼓励可信的高质量研究,并阻止会减缓科学进步和滥用资源的低质量研究。抵制数据无法支持的过度概括。我们的动机是鼓励可信的高质量研究,并阻止会减缓科学进步和滥用资源的低质量研究。

更新日期:2021-06-03
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