Self-control is a crucial factor in maintaining an established romantic relationship, but its role in relationship formation is understudied. The current study tested whether trait self-control is related to a more selective approach toward romantic partners. Over 4 years, we organized 11 speed-date events at which a total of 342 single, heterosexual participants met potential partners. Our results indicated that there was no main effect of self-control on selectivity. However, there was an interaction between self-control and sociosexual orientation (SOI) in predicting selectivity. Specifically, self-control was positively related to selectivity for people with a restricted SOI (i.e., interested in a long-term, stable relationship). For people with an unrestricted SOI (i.e., interested in a short-term, sexual relationship), however, self-control was related to lower selectivity. Our findings point to the flexibility of self-control in facilitating goal progress, stimulating people to refrain from—or act on—their impulses, depending on their own personal mating goals.

There is evidence that testosterone and cortisol levels are related to the attraction of a romantic partner; testosterone levels relate to a wide range of sexual behaviors and cortisol is a crucial component in the response to stress. To investigate this, we conducted a speed-dating study among heterosexual singles. We measured salivary testosterone and cortisol changes in men and women (n = 79) when they participated in a romantic condition (meeting opposite-sex others, i.e., potential romantic partners), as well as a control condition (meeting same-sex others, i.e., potential friends). Over the course of the romantic speed-dating event, results showed that women's but not men's testosterone levels increased and cortisol levels decreased for both men and women. These findings indicate that men's testosterone and cortisol levels were elevated in anticipation of the event, whereas for women, this appears to only be the case for cortisol. Concerning the relationship between attraction and hormonal change, four important findings can be distinguished. First, men were more popular when they arrived at the romantic speed-dating event with elevated cortisol levels. Second, in both men and women, a larger change in cortisol levels during romantic speed-dating was related to more selectivity. Third, testosterone alone was unrelated to any romantic speed-dating outcome (selectivity or popularity). However, fourth, women who arrived at the romantic speed-dating event with higher testosterone levels were more selective when their anticipatory cortisol response was low. Overall, our findings suggest that changes in the hormone cortisol may be stronger associated with the attraction of a romantic partner than testosterone.

We present MatchNMingle, a novel multimodal/multisensor dataset for the analysis of free-standing conversational groups and speed-dates in-the-wild. MatchNMingle leverages the use of wearable devices and overhead cameras to record social interactions of 92 people during real-life speed-dates, followed by a cocktail party. To our knowledge, MatchNMingle has the largest number of participants, longest recording time and largest set of manual annotations for social actions available in this context in a real-life scenario. It consists of 2 hours of data from wearable acceleration, binary proximity, video, audio, personality surveys, frontal pictures and speed-date responses. Participants' positions and group formations were manually annotated; as were social actions (eg. speaking, hand gesture) for 30 minutes at 20fps making it the first dataset to incorporate the annotation of such cues in this context. We present an empirical analysis of the performance of crowdsourcing workers against trained annotators in simple and complex annotation tasks, founding that although efficient for simple tasks, using crowdsourcing workers for more complex tasks like social action annotation led to additional overhead and poor inter-annotator agreement compared to trained annotators (differences up to 0.4 in Fleiss' Kappa coefficients). We also provide example experiments of how MatchNMingle can be used.