| 摘要: |
| 教室光环境质量直接关系学生的视
觉健康与学习效率，但传统评价指标往往忽
略了精细的空间亮度分布特征。为量化亮度
空间分布与相关色温对主观偏好的协同影
响，本研究针对教室静态自习任务，构建了
36 种实验工况，收集到16 名被试的偏好评
价数据。利用最大互信息系数（MIC）识别
关联参数，并结合系统聚类剔除冗余，筛选
出相关色温（CCT）、视野亮度均值（Lm）、
正前方亮度梯度（D31） 及左下方边缘梯
度（D82）作为核心变量，建立了教室光环境
氛围喜爱度预测模型。研究发现色温对氛围
喜爱度具有主导作用，空间亮度特征值的调
节效应深度依赖于色温背景，二者存在显著
的非线性交互。研究成果为未来教室照明的
精细化设计与智能调控提供了量化依据。 |
| 关键词: 教室照明 亮度特征值 亮度分
布 相关色温 氛围喜爱度 |
| DOI：10.13791/j.cnki.hsfwest.20260515009 |
| 分类号: |
| 基金项目:国家自然科学基金项目（52078331）；天津大学自主创新基金项目（2026XJ22-0042） |
|
| A predictive model for atmosphere preference in classroom lighting environments basedon the interaction between viewfield luminance features and correlated color temperature |
|
MA Fengrui,DANG Rui,LIU Yuxing
|
| Abstract: |
| Classroom lighting profoundly affects students’ visual health, learning efficiency, and
psychological comfort, yet conventional design standards based on illuminance and correlated color
temperature (CCT) overlook the critical role of spatial luminance distribution. Since luminance
directly stimulates the retina, it better represents actual visual experience, but existing studies rarely
quantify how luminance distribution features interact with CCT to shape subjective atmosphere
preference. This study addresses this gap by systematically investigating the combined effects of
spatial luminance gradients, mean luminance, and CCT on preference ratings under static self-study
tasks, aiming to develop a predictive model that supports refined classroom lighting design. A
controlled experiment was conducted in a variable-space chamber configured as a 12×6×4 m
classroom. Eighteen LED luminaires with tunable CCT (2 700-6 000 K) and dimmable output were
installed, and wall reflectances were varied using replaceable curtain fabrics. Thirty-six experimental
conditions were generated by combining three CCT levels (3 000, 4 300, 5 500 K), three desk
illuminance levels (300, 500, 1 000 lx), and four wall reflectance coefficients (0.6-0.9). Luminance
distributions were captured at a seated student’s eye height using a Konica CA-2000 color luminance
meter. After preprocessing with a region-growing algorithm, the visual field was divided into a 5×5
grid, yielding 25 regional mean luminances. From these, 16 directional luminance gradients (D11 to
D82) were defined along eight radial directions at two distance steps, together with mean
luminance (Lm), central luminance (Lc), and standard deviation (Ld), resulting in 20 candidate physical
variables plus CCT. Subjective preference data were collected from 16 healthy university students (8
male, 8 female, aged 22-26) using a 5-point Likert scale after a 10-minute simulated reading task.
Each participant evaluated all 36 conditions in random order, producing 576 valid responses. To
identify the most influential predictors, both Pearson correlation and maximal information
coefficient (MIC) were applied. Pearson correlations showed no significant linear relationships (p>
0.05), whereas MIC values revealed strong nonlinear associations: CCT ranked highest (MIC=0.92),
followed by several gradients (D31, D41, D52, D62 at 0.53) and Lm (0.46). This clear discrepancy
confirmed that the relationship between physical parameters and psychological preference is
inherently nonlinear. To reduce multicollinearity among luminance features—inevitable due to global
dimming—hierarchical clustering was performed on the 19 non-CCT variables using average linkage.
The dendrogram identified three major clusters; after eliminating redundant members, four core
variables were retained: CCT, Lm, the forward luminance gradient (D31, center-to-front-background
difference), and the lower-left peripheral gradient (D82). These variables capture the dominant
baseline effect of CCT, the overall brightness level, the spatial contrast in the primary viewing
direction, and a secondary peripheral correction, respectively. Visualization of the data revealed strong
interactive effects. Under 3 000 K, preference ratings increased nearly linearly with L? from 40 to 100cd/cm2, indicating that higher luminance compensates for the low-CCT induced drowsiness. However, under 4 300 K and 5 500 K, preference peaked at L? ≈
70-80 cd/cm2 and then flattened or declined, demonstrating saturation and diminishing returns. Similarly, the effect of D?? reversed with CCT: positive under
3 000 K (enhancing spatial layering), neutral under 4 300 K, and negative under 5 500 K when D31 exceeded 80 cd/cm2, suggesting that high CCT combined
with strong gradients increases visual stress. These patterns confirm that luminance features do not act independently but are deeply modulated by the CCT
context. To quantitatively model these nonlinear and interactive relationships, a polynomial regression was formulated with centered CCT (to reduce
collinearity), quadratic CCT2 term, linear Lm, D31, and D82, and an interaction term CCTadj×D31. The fitted model achieved excellent performance: R2=
0.912 (adjusted R2=0.894), with overall F-test p < 0.001. The negative coefficient for CCT2 confirmed the inverted-U shape, while the significant negative
interaction coefficient (p <0.001) quantitatively proved that the contribution of D31 diminishes as CCT increases. The final equation provides a practical tool for
predicting atmosphere preference from easily measurable or designable variables. The model also enables a clear mapping from design parameters to
environmental features. CCT is primarily set by light source selection, with 4 300 K recommended as the optimal baseline. L? is controlled via dimming and
luminaire count, with a saturation point around 70-80 cd/cm2 under neutral/cool CCTs. D31 is adjusted through front wall reflectance and luminaire optics:
moderate enhancement under low CCT to improve spatial depth, but smoothing under high CCT to avoid glare and discomfort. D82, as a secondary factor, can
be managed via side-wall materials. This mapping establishes a quantitative design workflow from inputs to predicted preference, enabling iterative
optimization. In conclusion, this study demonstrates that classroom lighting preference is governed by strong nonlinear interactions between CCT and spatial
luminance distribution, not by any single linear factor. The proposed model, with 91.2% explanatory power, successfully integrates these complexities and
offers a scientific basis for intelligent, human-centric lighting control. The findings advocate for moving beyond uniform illuminance standards toward adaptive
strategies that tailor luminance gradients according to CCT, ultimately enhancing visual comfort and well-being in educational environments. |
| Key words: classroom lighting luminance characteristic values luminance distribution Correlated Color Temperature (CCT) atmosphere preference |
