Mandatory First Impressions: Happy Expressions Increase Trustworthiness Ratings of Subsequent Neutral Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
First impressions of traits are formed rapidly and nonconsciously, suggesting an automatic process. We examined whether first impressions of trustworthiness are mandatory, another component of automaticity in face processing. In Experiment 1a, participants rated faces displaying subtle happy, subtle angry, and neutral expressions on trustworthiness. Happy faces were rated as more trustworthy than neutral faces; angry faces were rated as less trustworthy. In Experiment 1b, participants learned eight identities, half showing subtle happy and half showing subtle angry expressions. They then rated neutral images of these same identities (plus four novel neutral faces) on trustworthiness. Multilevel modeling analyses showed that identities previously shown with subtle expressions of happiness were rated as more trustworthy than novel identities. There was no effect of previously seen subtle angry expressions on ratings of trustworthiness. Mandatory first impressions based on subtle facial expressions were also reflected in two ratings designed to assess real-world outcomes. Participants indicated that they were more likely to vote for identities that had posed happy expressions and more likely to loan them money. These findings demonstrate that first impressions of trustworthiness based on previously seen subtle happy, but not angry, expressions are mandatory and are likely to have behavioral consequences.
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.
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) | 0.024 | 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 it