The conceptual structure of face impressions
Bibliographic record
Abstract
Humans seamlessly infer the expanse of personality traits from others' facial appearance. These facial impressions are highly intercorrelated within a structure known as "face trait space." Research has extensively documented the facial features that underlie face impressions, thus outlining a bottom-up fixed architecture of face impressions, which cannot account for important ways impressions vary across perceivers. Classic theory in impression formation emphasized that perceivers use their lay conceptual beliefs about how personality traits correlate to form initial trait impressions, for instance, where trustworthiness of a target may inform impressions of their intelligence to the extent one believes the two traits are related. This considered, we explore the possibility that this lay "conceptual trait space"-how perceivers believe personality traits correlate in others-plays a role in face impressions, tethering face impressions to one another, thus shaping face trait space. In study 1, we found that conceptual and face trait space explain considerable variance in each other. In study 2, we found that participants with stronger conceptual associations between two traits judged those traits more similarly in faces. Importantly, using a face image classification task, we found in study 3 that participants with stronger conceptual associations between two traits used more similar facial features to make those two face trait impressions. Together, these findings suggest lay beliefs of how personality traits correlate may underlie trait impressions, and thus face trait space. This implies face impressions are not only derived bottom up from facial features, but also shaped by our conceptual beliefs.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".