Kissing Right? Absence of Rightward Directional Turning Bias During First Kiss Encounters Among Strangers
Bibliographic record
Abstract
When leaning toward a partner for a kiss, the direction that individuals turn their head when planting the kiss is found to vary based on the kiss’s context; romantic kissing between adult couples is consistently directed rightward, though recently, a non-romantic kiss between parent–child couples was observed to be leftward. The current study further examines the lateral head-turning direction between non-romantic couples using a novel context: a first kiss between strangers. Observing strangers kissing was feasible due to a unique social media phenomenon; since 2014, 23 “First Kiss” online videos have emerged which depict kisses facilitated by the video’s director between consenting strangers. The turning direction of 230 kissing couples were coded from the 23 First Kiss videos, and the proportion of right to left turns were almost equal; 51% of couples displayed a right-turn kiss, and 49% conveyed a left-turn kiss. Further, the proportion of right and left turns observed from our sample of strangers kissing were compared to Güntürkün’s (in Nature 421:711, https://doi.org/10.1038/421711a , 2003) original study that examined authentic kissing between adult couples. A significantly different turning bias was exhibited. Because the kissing criterion was parallel between these studies, our study demonstrates that the context influenced the direction of bias, namely, that of a non-romantic kiss. We discuss the potential role of context and emotional lateralization on kissing laterality, and propose future directions to test these predictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".