Malleability of attentional bias for positive emotional information and anxiety vulnerability.
Bibliographic record
Abstract
Recent research supports a causal link between attentional bias for negative emotional information and anxiety vulnerability. However, little is known about the role of positive emotional processing in modulating anxiety reactivity to stress. In the current study, we used an attentional training paradigm designed to experimentally manipulate the processing of positive emotional cues. Participants were randomly assigned to complete a computerized probe detection task designed to induce selective processing of positive stimuli or to a sham condition. Following training, participants were exposed to a laboratory stressor (i.e., videotaped speech), and state anxiety and positive affect in response to the stressor were assessed. Results revealed that individual variability in the capacity to develop an attentional bias for positive information following training predicted subsequent emotional responses to the stressor. Moreover, individual differences in social anxiety, but not depression, moderated the effects of the attentional manipulation, such that, higher levels of social anxiety were associated with diminished attentional allocation toward positive cues. The current findings point to the potential value of considering the role of positive emotional processing in anxiety vulnerability.
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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 | 0.007 |
| 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) | 0.002 | 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".