Anchoring has little effect when forming first impressions of facial attractiveness
Bibliographic record
Abstract
First impressions based on facial appearance affect our behaviour towards others. Since the same face will appear different across images, over time, and so on, our impressions may not be equally weighted across exposures but are instead disproportionately influenced by earlier or later instances. Here, we followed up on previous work which identified an anchoring effect, whereby higher attractiveness ratings were given to a person after viewing naturally varying images of their face presented in descending (high-to-low), rather than ascending (low-to-high), order of attractiveness of these images. In Experiment 1 ( n = 301), we compared these ‘descending’ and ‘ascending’ conditions for unfamiliar identities by presenting six-image sequences. Although we found higher attractiveness ratings for the ‘descending’ condition, this small effect equated to only 0.22 points on a 1–7 response scale. In Experiment 2 ( n = 307), we presented these six-image sequences in a random order and found no difference in attractiveness ratings given to these randomly ordered sequences when compared with those resulting from both our ‘descending’ and ‘ascending’ conditions. Further, we failed to detect an influence of the earlier images in these random sequences on attractiveness ratings. Taken together, we found no compelling evidence that anchoring could have an effect on real-world impression formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".