Predictably confirmatory: The influence of stereotypes during decisional processing
Bibliographic record
Abstract
Stereotypes facilitate the processing of expectancy-consistent (vs expectancy-inconsistent) information, yet the underlying origin of this congruency effect remains unknown. As such, here we sought to identify the cognitive operations through which stereotypes influence decisional processing. In six experiments, participants responded to stimuli that were consistent or inconsistent with respect to prevailing gender stereotypes. To identify the processes underpinning task performance, responses were submitted to a hierarchical drift diffusion model (HDDM) analysis. A consistent pattern of results emerged. Whether manipulated at the level of occupational (Expts. 1, 3, and 5) or trait-based (Expts. 2, 4, and 6) expectancies, stereotypes facilitated task performance and influenced decisional processing via a combination of response and stimulus biases. Specifically, (1) stereotype-consistent stimuli were classified more rapidly than stereotype-inconsistent stimuli; (2) stereotypic responses were favoured over counter-stereotypic responses (i.e., starting-point shift towards stereotypic responses); (3) less evidence was required when responding to stereotypic than counter-stereotypic stimuli (i.e., narrower threshold separation for stereotypic stimuli); and (4) decisional evidence was accumulated more efficiently for stereotype-inconsistent than stereotype-consistent stimuli and when targets had a typical than atypical facial appearance. Collectively, these findings elucidate how stereotypes influence person construal.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".