Accuracy and bias in first impressions of attachment style from faces
Bibliographic record
Abstract
OBJECTIVE: People gather important social information from subtle nonverbal cues. Given that one's attachment style can meaningfully affect the quality of one's relationships, we investigated whether people could perceive men's and women's attachment styles from photos of their neutral faces. METHOD: In two studies, we measured targets' attachment styles then asked participants (total N = 893) to judge the male and female targets' attachment anxiety and avoidance from photos of their neutral faces (total N = 331) and to report their own attachment anxiety and avoidance. RESULTS: Participants detected men's attachment style from face photos significantly better than chance in an initial exploratory study and in a preregistered replication but did not consistently detect women's attachment style from their face photos. Moreover, participants' own attachment style biased these first impressions: Individuals with greater attachment anxiety viewed others as more anxiously attached. CONCLUSIONS: People can detect some hints of unacquainted others' attachment styles from their faces but their own anxious attachment can bias these judgments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".