Results of first questionnaire for Experiment 3.

Luminosity thresholds define the luminance boundary at which a surface color shifts in appearance from being perceived as an illuminated surface to appearing self-luminous. Previous research suggests that the human visual system infers these thresholds based on internal references of physically realizable surface colors under a given illumination, referred to as the physical gamut. A surface is perceived as self-luminous when its luminance exceeds the upper limit of this empirically internalized gamut. However, the precise structure and boundaries of these gamuts remain uncertain. Optimal colors, which represent theoretical surface reflectances under specific illuminants, have been shown to provide an effective model for visualizing and computing the physical gamuts. In prior studies, optimal colors have successfully predicted luminosity thresholds; however, these findings were limited to highly simplified, abstract stimuli. Whether this framework generalizes to more naturalistic viewing conditions has remained an open question. In the present study, we demonstrate that the theory of an internal reference in the form of an empirically constructed physical gamut, visualized through optimal colors, remains valid under more natural conditions. Our results confirm that optimal colors can still accurately predict luminosity thresholds in such settings. Moreover, our findings suggest that the luminosity thresholds encompass both self-luminosity and naturalness concepts. Consequently, this may imply that the notion of physical gamut could encompass both concepts as well and could be defined as “all physically possible colors in a scene for an object that does not emit light.” These insights have profound potential implications for applied fields (e.g., XR or projection mapping). For example, they provide a theoretical framework that specifies the luminance and color boundaries virtual objects should not exceed to remain realistically integrated into AR/MR scenes. In fundamental science, these findings can improve our understanding of the perception of naturalness in images.

亮度阈值（Luminosity thresholds）定义了表面外观从被感知为受照表面转变为自发光表面的亮度边界。此前研究表明，人类视觉系统会基于给定照明环境下物理可实现表面颜色的内部参照来推断此类阈值，该参照被称为物理色域（physical gamut）。当表面的亮度超出该经验内化色域的上限时，其会被感知为自发光表面。然而，此类色域的精确结构与边界仍未明确。最优色（Optimal colors）指特定照明条件下的理论表面反射率，已有研究表明其可作为可视化与计算物理色域的有效模型。在过往研究中，最优色已成功预测了亮度阈值，但此类研究结论仅局限于高度简化的抽象视觉刺激。该框架能否推广至更贴近真实自然的观看场景，仍是一个待解的问题。本研究证实，以经验构建的物理色域为内部参照形式、并通过最优色实现可视化的理论，在更自然的场景下依然成立。本研究结果确认，在此类场景中最优色仍可准确预测亮度阈值。此外，本研究结果表明，亮度阈值同时涵盖了自发光与自然性两类概念。据此可推知，物理色域的概念或许也可涵盖这两类概念，其可被定义为“场景中不发光物体的所有物理可能颜色”。此类研究结论在应用领域（如扩展现实（XR）、投影映射）中具有深远的潜在应用价值。例如，本研究为虚拟物体设定亮度与颜色边界提供了理论框架，可确保虚拟物体在增强现实（Augmented Reality, AR）/混合现实（Mixed Reality, MR）场景中实现逼真的融合效果。在基础科学领域，此类研究结果可加深我们对图像自然性感知的理解。