Rahman Eqram, Esfahlani Shabnam Sadeghi, Rao Parinitha, Webb William Richard
Research and Innovation Hub, Innovation Aesthetics, London, WC2H 9JQ, UK.
Medical Technology Research Centre (MTRC), School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK.
Aesthetic Plast Surg. 2025 Feb;49(3):841-861. doi: 10.1007/s00266-024-04304-7. Epub 2024 Aug 26.
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.
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.
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: (β is the intercept; β, β, β, β 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.
This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
理解吸引力（A）的多面性至关重要，它涵盖了外在美（PB）、真诚（GEN）、自信（SC）和过往经历（RE）等方面，这对包括心理学和临床美学在内的各个领域都很关键。以往的研究常常孤立地看待特定元素，未能捕捉到它们之间复杂的相互作用。本研究旨在运用计算神经美学开发一个全面的吸引力方程。
该研究首先进行了一项试点研究，涉及250名参与者（50名专家和200名普通人），他们根据李克特量表对500张面部图像的外在美、真诚度、自信程度和感知到的过往经历等特质进行了预评分。试点研究之后，主要研究通过多种媒体渠道招募了11,780名参与者，让他们对一组新的1000张面部图像进行评分。采用了包括多元线性回归和贝叶斯层次建模在内的先进计算技术来分析数据并制定吸引力方程。
分析确定真诚是最重要的因素，其次是外在美、自信和过往经历。通过贝叶斯建模优化后的吸引力方程为：（β为截距；β、β、β、β为各因素的系数；ϵ为误差项）结论：研究结果强调了心理特质在吸引力评估中的至关重要性，表明在临床环境中应从单纯的身体改善转向整体干预。该模型为理解吸引力提供了一个强大的框架，在心理学、市场营销和人工智能领域具有潜在应用价值。
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