Equation for Attractiveness: Integrating Multidimensional Factors Through Computational Neuroaesthetics

Background: Understanding the multifaceted nature of attractiveness (A), which encompasses physical beauty (PB), genuineness (GEN), self-confidence (SC), and prior experience (RE), is crucial for various domains, including psychology and clinical aesthetics. Previous studies have often isolated specific elements, failing to capture their intricate interplay. This study aims to develop a comprehensive equation for attractiveness using computational neuroaesthetics.

Method: The study began with a pilot study involving 250 participants (50 experts and 200 laypersons) who prerated 500 facial images on a Likert scale for traits such as physical beauty, genuineness, self-confidence, and perceived prior experience. Following the pilot, the main study recruited 11,780 participants through diverse media channels to rate a new set of 1,000 facial images. Advanced computational techniques, including multiple linear regression and Bayesian hierarchical modelling, were employed to analyse the data and formulate an attractiveness equation.

Results: The analysis identified genuineness as the most significant factor, followed by physical beauty, self-confidence, and prior experience. The proposed equation for attractiveness, refined through Bayesian modelling, is: $\begin{array}{c}A={\beta }_0+({\beta }_1·\text{PB}+{\beta }_2·\text{GEN}+{\beta }_3·\text{SC}+{\beta }_4·\text{PE})+ϵ\\ A=1.82+(0.34·\text{PB}+0.44·\text{GEN}+0.26·\text{SC}+0.16·\text{PE})+ϵ\\ \end{array}$ (β₀ is the intercept; β₁, β₂, β₃, β₄ are the coefficients for each factor; and ϵ is the error term) CONCLUSION: The findings underscore the paramount importance of psychological traits in attractiveness assessments, suggesting a shift from purely physical enhancements to holistic interventions in clinical settings. This model provides a robust framework for understanding attractiveness and has potential applications in psychology, marketing, and AI.

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