Table 1.
Scale-set of art-judgements (targets in machine learning analysis) used in the empirical study, English version (see for German version S1 Table).
Table 2.
Scales of attributes (predictors in machine learning analysis) used in the empirical study, English version (see for German version S2 Table).
Table 3.
GBDT regressor prediction results of all art judgment ratings.
50 repetitions from cross-validation.
Fig 1.
Art-attributes importance’s for predicting aesthetically moving: Top plot shows mean absolute SHAP (SHapley Additive exPlanations) values for all 17 attributes in the model. Bottom plot displays SHAP values directly (color code: low = blue, red = high, and showing non-linearity of associations) and offers a detailed view of the individual impacts of predictors on specific predictions. **Bold and red attributes represent the most important attributes considering mean SHAP values; **red attributes significant with Bonferroni correction, however with a lesser influence considering mean SHAP values; *significant attributes before Bonferroni correction.
Fig 2.
Art-attributes importance’s for predicting liking: Top plot shows mean absolute SHAP (SHapley Additive exPlanations) values for all 17 attributes in the model. Bottom plot displays SHAP values directly (color code: low = blue, red = high, and showing non-linearity of associations) and offers a detailed view of the individual impacts of predictors on specific predictions. **Bold and red attributes represent the most important attributes considering mean SHAP values; **red attributes significant with Bonferroni correction, however with a lesser influence considering mean SHAP values; *significant attributes before Bonferroni correction.
Fig 3.
Art-attributes importance’s for predicting creativity: Top plot shows mean absolute SHAP (SHapley Additive exPlanations) values for all 17 attributes in the model. Bottom plot displays SHAP values directly (color code: low = blue, red = high, and showing non-linearity of associations) and offers a detailed view of the individual impacts of predictors on specific predictions. **Bold and red attributes represent the most important attributes considering mean SHAP values; **red attributes significant with Bonferroni correction, however with a lesser influence considering mean SHAP values; *significant attributes before Bonferroni correction.
Fig 4.
Art-attributes importance’s for predicting disturbing/irritating: Top plot shows mean absolute SHAP (SHapley Additive exPlanations) values for all 17 attributes in the model. Bottom plot displays SHAP values directly (color code: low = blue, red = high, and showing non-linearity of associations) and offers a detailed view of the individual impacts of predictors on specific predictions. **Bold and red attributes represent the most important attributes considering mean SHAP values; **red attributes significant with Bonferroni correction, however with a lesser influence considering mean SHAP values; *significant attributes before Bonferroni correction.
Fig 5.
Rank based on mean SHAP-values for each attribute in association to the art judgments; *p-value <0.05; **p-value <0.05 incl.
Bonferroni correction.