| Literature DB >> 36248445 |
Chenchen Yao1, Tian Tian1, Cai Gao1, Shuangping Zhao2, Qingyan Liu2.
Abstract
Humans have been exploring colors since ancient times, but relatively complete color systems appeared one after another in the twentieth century. Even without language and other information exchanges, colors can still convey information and stimulate emotions. Therefore, color can have both physical and psychological effects on people. In this context, this paper studies the visual representation of painting colors based on psychological factors. The article studies the theory of personality traits and introduces the related content of visual psychology. To explore the relationship between each variable and color psychology and the visual representation of painting colors, a binary logistic regression analysis is performed. The colors in the post-impressionist paintings of Van Gogh and Gauguin is contrasted, and experiments on psychological factors and color research is conducted. The factors that affect the color tone of the picture and the influence of psychological factors on the judgment of color brightness are investigated. Finally, the correlation analysis of personality trait dimension and irrational behavior is carried out. The experimental results of the article show that after the analysis of variance, the significance levels of regression model 1 and model 2 both reach 0.000, and the adjusted R squares are 0.319 and 0.356, respectively. In this study, regression model 2 was selected as the final model. According to Model 2, the standardized regression coefficients of agreeableness and neuroticism are 0.438 and -0.251, respectively, and the significance of the regression coefficients are 0.000 and 0.021, respectively. The research on the visual performance of painting colors based on psychological factors has been well completed.Entities:
Keywords: color psychology; painting color; psychological factors; variance; visual performance
Year: 2022 PMID: 36248445 PMCID: PMC9556761 DOI: 10.3389/fpsyg.2022.966571
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
FIGURE 1Flow chart of particle swarm algorithm.
FIGURE 2Colors in Van Gogh’s paintings.
FIGURE 3Colors in Gauguin’s paintings.
FIGURE 4Comparison of warm and cool colors in painting.
FIGURE 5Color rendering index of common indoor artificial light sources.
FIGURE 6The illuminance contrast of the position when the uniformity U0 is 0.3.
FIGURE 7Illumination comparison before and after the position when the uniformity U0 is 0.4.
FIGURE 8Illumination comparison before and after the position when the uniformity U0 Is 0.5.
FIGURE 9The influence of psychological factors on the judgment of color brightness.
Personality trait dimension-irrational behavior correlation analysis results.
| Nervous | Rigorism | Agreeableness | Openness | Extroversion | ||
| Overconfidence | Pearson correlation | −0.252 | 0.438 | −0.075 | 0.475 | 0.736 |
| 0.023 | 0.000 | 0.504 | 0.000 | 0.000 | ||
| 81 | 81 | 81 | 81 | 81 | ||
| Disposal effect | Pearson correlation | 0.118 | −0.176 | −0.277 | 0.055 | 0.085 |
| 0.294 | 0.117 | 0.012 | 0.625 | 0.451 | ||
| 81 | 81 | 81 | 81 | 81 | ||
| Herd effect | Pearson correlation | −0.486 | 0.318 | 0.572 | −0.144 | −0.088 |
| 0.000 | 0.004 | 0.000 | 0.198 | 0.436 | ||
| 81 | 81 | 81 | 81 | 81 |
The symbols * and ** refer to the magnitude of statistical difference.
ANOVA results.
| ANOVA | ||||||
| Model | Sum of squares | Free degree | Mean square |
| Significance | |
| 1 | Regression | 206.717 | 1 | 206.717 | 38.451 | 0.000 |
| Residual | 424.715 | 79 | 5.376 | |||
| Amount | 631.432 | 80 | ||||
| 2 | Regression | 235.048 | 2 | 117.524 | 23.126 | 0.000 |
| Residual | 396.385 | 78 | 5.082 | |||
| Amount | 631.432 | 80 | ||||
The symbol * refers to statistical difference.
Significance test results.
| Coefficient | ||||||||
| Model | Non-standardized coefficient | Standardization coefficient |
| Significance | Collinearity statistics | |||
|
|
| |||||||
| B | Standard error | Beta | Tolerance | VIF | ||||
| 1 | (Constant) | −0.432 | 1.102 | −0.392 | 0.696 | |||
| Agreeableness | 0.191 | 0.031 | 0.572 | 6.201 | 0.000 | 1.000 | 1.000 | |
| 2 | (Constant) | 3.184 | 1.869 | 1.704 | 0.092 | |||
| Agreeableness | 0.146 | 0.036 | 0.438 | 4.116 | 0.000 | 0.712 | 1.404 | |
| Nervous | −0.087 | 0.037 | −0.251 | −2.361 | 0.021 | 0.712 | 1.404 | |
Summary of regression models.
| Model summary | |||||||||
| Model |
| Adjusted R-square | Standard estimation error | R-square variation | F variation | Change statistical degrees of freedom | Freedom | Significant f change | |
| 1 | 0.572 | 0.327 | 0.319 | 2.31865 | 0.327 | 38.451 | 1 | 79 | 0.000 |
| 2 | 0.610 | 0.372 | 0.356 | 2.25430 | 0.045 | 5.575 | 1 | 78 | 0.021 |
Common factor variance.
| Variable | A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | A9 | A10 | A11 |
| Initial | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Extract | 0.612 | 0.836 | 0.741 | 0.812 | 0.764 | 0.779 | 0.734 | 0.815 | 0.67 | 0.741 | 0.702 |
| Variable | A12 | A13 | A14 | A15 | A16 | A17 | A18 | A19 | A20 | A21 | – |
| Initial | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | – |
| Extract | 0.717 | 0.63 | 0.624 | 0.69 | 0.661 | 0.73 | 0.767 | 0.67 | 0.741 | 0.672 | – |
Total variance explained.
| Component | Total | Total initial eigenvalue | Extract sum of squares load | Rotation sum of squares loading | |||||
| Variance | Cumulative | Total | Variance | Cumulative | Total | Variance | Cumulative | ||
| 1 | 5.307 | 32.854 | 32.854 | 5.307 | 32.854 | 32.854 | 3.872 | 23.971 | 23.971 |
| 2 | 3.503 | 21.684 | 54.538 | 3.503 | 21.684 | 54.538 | 3.263 | 20.202 | 44.174 |
| 3 | 2.083 | 12.897 | 67.435 | 2.083 | 12.897 | 67.435 | 2.905 | 17.983 | 62.156 |
| 4 | 1.107 | 6.851 | 74.287 | 1.107 | 6.851 | 74.287 | 1.631 | 10.096 | 72.251 |
| 5 | 1.008 | 6.237 | 80.525 | 1.008 | 3.237 | 80.525 | 1.336 | 8.273 | 80.525 |
| 6 | 0.837 | 2.740 | 83.265 | – | – | – | – | – | – |
| 7 | 0.713 | 1.346 | 84.610 | – | – | – | – | – | – |
| 8 | 0.688 | 1.270 | 85.880 | – | – | – | – | – | – |
| 9 | 0.642 | 1.161 | 87.041 | – | – | – | – | – | – |
| 10 | 0.621 | 1.147 | 88.188 | – | – | – | – | – | – |
| 11 | 0.519 | 1.134 | 89.322 | – | – | – | – | – | – |
| 12 | 0.505 | 1.123 | 90.445 | – | – | – | – | – | – |
| 13 | 0.494 | 1.111 | 91.555 | – | – | – | – | – | – |
| 14 | 0.463 | 1.099 | 92.654 | – | – | – | – | – | – |
| 15 | 0.443 | 1.087 | 93.741 | – | – | – | – | – | – |
| 16 | 0.420 | 1.075 | 94.816 | – | – | – | – | – | – |
| 17 | 0.388 | 1.062 | 95.879 | – | – | – | – | – | – |
| 18 | 0.358 | 1.050 | 96.928 | – | – | – | – | – | – |
| 19 | 0.336 | 1.037 | 97.965 | – | – | – | – | – | – |
| 20 | 0.309 | 1.024 | 98.990 | – | – | – | – | – | – |
| 21 | 0.255 | 1.012 | 100.000 | – | – | – | – | – | – |