The impact of stress on the visual representation of an ethnic ingroup and outgroup
Bibliographic record
Abstract
Social stress potentiates prosocial behavior (von Dawans et al., 2012) by increasing trust, trustworthiness, and sharing behavior during social interactions. Still unknown is whether this increase in prosocial behavior is also directed towards another ethnic group. We verified if a social stress altered the visual representation of an ethnic ingroup and outgroup, and if such changes were related to racial prejudices. Participants were divided into two groups and submitted to the Trier Social Stress Test for Groups (N=20) or a control condition (N=21). Participants then completed a Reverse Correlation task (Mangini & Biederman, 2004). On each trial, two stimuli were presented, composed of the same base face on which two inversely correlated patches of noise were added. The participants had to decide which face looked most prototypical of a Caucasian or an African-American. All participants also completed an Implicit Association Test (Greenwald et al., 1998) to measure their level of ethnic bias. Individual classification images (CIs) were calculated by summing the noise added on the faces chosen as most representative of each ethnic group. Group CIs were then produced by summing the CIs of the eight participants with the highest (vs. eight with the lowest) level of prejudices, in each stress condition. In a second phase, 50 independent judges were asked to decide which of two CIs of the same ethnicity, coming from the same prejudice group but from different stress conditions, seemed the most trustworthy. For highly prejudiced individuals, stress made the visual representation of a Caucasian appear more trustworthy (p=0.007) whereas it was the opposite for African-American (p=0.007). This pattern of result was reversed in participants with low racial prejudices, suggesting that other mechanisms may be at play (p=0.007 and p=0.0003 for Caucasian and African-Americans, respectively). Meeting abstract presented at VSS 2016
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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 | 0.000 | 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) | 0.005 | 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".