Stereotypes shape response competition when forming impressions
Bibliographic record
Abstract
Dynamic models of impression formation posit that bottom-up factors (e.g., a target’s facial features) and top-down factors (e.g., perceiver knowledge of stereotypes) continuously interact over time until a stable categorization or impression emerges. Most previous work on the dynamic resolution of judgments over time has focused on either categorization (e.g., “is this person male/female?”) or specific trait impressions (e.g., “is this person trustworthy?”). In two mousetracking studies—exploratory ( N = 226) and confirmatory ( N = 300)—we test a domain-general effect of cultural stereotypes shaping the process underlying impressions of targets. We find that the trajectories of participants’ mouse movements gravitate toward impressions congruent with their stereotype knowledge. For example, to the extent that a participant reports knowledge of a “Black men are less [trait]” stereotype, their mouse trajectory initially gravitates toward categorizing individual Black male faces as “less [trait],” regardless of their final judgment of the target.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".