Table 4.

Summary of findings and significance across the four studies presented in the current research.

Study	Finding	Significance
1	Face structure, color, and texture, and their weighted combination are reliable predictors of facial affect; each metric varies by gender in an expected manner	Validates model; extends previous work showing gender differences in facial structure to texture and color as well
2	All three metrics correlate with, and in some cases, mediate the relationship between face gender and human impressions	Provides correlational evidence that the metrics used by machine learning to predict emotions relates to human impressions in an expected manner
3	Algorithmically-derived impressions of dominance and affiliation are related to human impressions of dominance and trustworthiness	Demonstrates that higher-order impressions can be derived from machine learning output trained on emotions
4	Algorithmically-derived impressions can be used to reverse-engineer important structure, color, and texture features in neutral faces	Experimentally demonstrates that metrics machine learning models use to predict emotions are also used by humans to form impressions