Metaphoric Language In Romance

Here is a paper appropriate for Valentine's Day.  Abstract:

Language plays an important role in romantic attachment. However, it is unclear whether the structure and topic of language use might influence potential mate choice. We investigated 124 female students' preference for compliments paid by males incorporating either literal or metaphoric (conventional/novel) language and targeting their appearance or possessions (house) throughout their menstrual cycle. Male faces paired with novel metaphorical compliments were rated as more attractive by women than those paired with literal ones. Compliments targeting appearance increased male attractiveness more than possessions. Interestingly, compliments on appearance using novel metaphors were preferred by women in a relationship during the fertile phase but by single women during the luteal phase. A similar pattern of altered face attraction ratings was subsequently shown by subjects in the absence of the verbal compliments and even though they were unable to recognize the faces. Thus the maintained attraction bias for faces previously associated with figurative language compliments appears to be unconscious. Overall this study provides the first evidence that women find men who typically use novel metaphorical language to compliment appearance more attractive than those using prosaic language or complimenting possessions. The evolutionary significance for such a language use bias in mate selection is discussed.

That is an interesting study in human psychology and mate attraction.

Romantic Matchmaking By Machine?

By Chordboard - Self, from material in my possession., Public Domain, https://commons.wikimedia.org/w/index.php?curid=4310843

For Valentine’s Day: an analysis of the shortcomings of machine learning (for now) to gauge two people’s romantic attraction to each other.  The authors divide romantic attraction into three components. First, the overall tendency of someone to desire others (actor variance), the tendency of that someone to be desired by others (partner variance) and, finally, a third component (relationship variance) that captures desire not characterized by the other two components (one may term this, I guess, “the mysteries of love”).  While machine leaning can predict some fraction of actor and partner variances, it was unable to model the more elusive relationship variance using traits and preferences reported before dates.  So far, the fine details of human romantic interest cannot be fully captured by machine learning. However, that may change in time.  Abstract:

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.