1 Department of Psychology, University of Utah.
2 Department of Psychology, University of California, Davis.
Psychol Sci. 2017 Oct;28(10):1478-1489. doi: 10.1177/0956797617714580. Epub 2017 Aug 30.
Matchmaking companies and theoretical perspectives on close relationships suggest that initial attraction is, to some extent, a product of two people's self-reported traits and preferences. We used machine learning to test how well such measures predict people's overall tendencies to romantically desire other people (actor variance) and to be desired by other people (partner variance), as well as people's desire for specific partners above and beyond actor and partner variance (relationship variance). In two speed-dating studies, romantically unattached individuals completed more than 100 self-report measures about traits and preferences that past researchers have identified as being relevant to mate selection. Each participant met each opposite-sex participant attending a speed-dating event for a 4-min speed date. Random forests models predicted 4% to 18% of actor variance and 7% to 27% of partner variance; crucially, however, they were unable to predict relationship variance using any combination of traits and preferences reported before the dates. These results suggest that compatibility elements of human mating are challenging to predict before two people meet.
婚介公司和亲密关系的理论观点表明，最初的吸引力在某种程度上是两个人自我报告的特征和偏好的产物。我们使用机器学习来测试这些措施在多大程度上可以预测人们对他人的总体浪漫欲望（演员方差）和被他人渴望的总体趋势（伴侣方差），以及人们对特定伴侣的渴望，超出演员和伴侣方差（关系方差）。在两项速配研究中，未婚的个体完成了 100 多项关于特征和偏好的自我报告措施，过去的研究人员已经确定这些特征和偏好与择偶有关。每个参与者与参加速配活动的每个异性参与者进行了 4 分钟的速配。随机森林模型预测了演员方差的 4%到 18%和伴侣方差的 7%到 27%；然而，至关重要的是，在约会之前，他们无法使用任何特征和偏好的组合来预测关系方差。这些结果表明，在两个人见面之前，人类交配的兼容性元素很难预测。
