The Inhibition of Socially Rejecting Information Among People with High Versus Low Self-Esteem: The Role of Attentional Bias and the Effects of Bias Reduction Training
Bibliographic record
Abstract
In two studies, we examined the inhibition of rejection information. In Study 1, we developed a Rejection Stroop task with the purpose of measuring an attentional bias to rejection words hypothesized to characterize individuals with low self-esteem. Results indicated that people with low self-esteem experienced significantly more interference on rejection words than on acceptance words, whereas for people with high self-esteem there was no such difference. In Study 2, we developed a task to train the response of inhibiting rejection information by repeatedly identifying the smiling/accepting face in a 4 × 4 matrix of frowning faces. Results showed that after this inhibition training, people with chronic low self-esteem experienced significantly less interference on rejection words on the Rejection Stroop than their counterparts in the control condition. People with high self-esteem, on the other hand, did not exhibit different amounts of interference on rejection or acceptance words between conditions. The present findings suggest that it is possible to measure people's attentional bias to rejection and teach people skills that help them deal with negative social information.
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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 | 0.003 |
| 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) | 0.001 | 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".