Negligible evidence that people desire partners who uniquely fit their ideals

A Critical Review of the Literature Regarding the Selection of Long-Term Romantic Partners

Research regarding how people choose their long-term romantic partners is extensive, but the understanding of the psychological processes behind these choices, and predicting who people choose, is elusive. This review attempts to examine potential reasons for this elusive nature by first outlining the current state of the literature and then highlighting issues within the current paradigm. First among these issues is a focus on singular perspectives and little attempt to integrate these perspectives with others. Second, many studies focus on increasingly complex designs to explore the predictive utility of trait preferences, attempts which have had only limited success. Third, novel findings appear to be unintegrated with established findings, leaving the potential combination of these ideas unrealized. Finally, long-term romantic partner selection is a complex psychological phenomenon, but current theory and research methodologies are not sufficiently addressing this complexity. This review concludes with suggestions for future research direction, including a focus on the psychology behind the partner selection process and the potential of qualitative enquiry to reveal novel pathways behind these psychological processes. There is a need for an integrative framework that permits the coexistence of established and novel ideas, and multiple perspectives, from both current and future research paradigms.

Predicting romantic interest during early relationship development: A preregistered investigation using machine learning

There are massive literatures on initial attraction and established relationships. But few studies capture early relationship development: the interstitial period in which people experience rising and falling romantic interest for partners who could—but often do not—become sexual or dating partners. In this study, 208 single participants reported on 1,065 potential romantic partners across 7,179 data points over 7 months. In stage 1, we used random forests (a type of machine learning) to estimate how well different classes of variables (e.g., individual differences vs. target-specific constructs) predicted participants’ romantic interest in these potential partners. We also tested (and found only modest support for) the perceiver × target moderation account of compatibility: the meta-theoretical perspective that some types of perceivers experience greater romantic interest for some types of targets. In stage 2, we used multilevel modeling to depict predictors retained by the random-forests models; robust (positive) main effects emerged for many variables, including sociosexuality, sex drive, perceptions of the partner’s positive attributes (e.g., attractive and exciting), attachment features (e.g., proximity seeking), and perceived interest. Finally, we found no support for ideal partner preference-matching effects on romantic interest. The discussion highlights the need for new models to explain the origin of romantic compatibility.