Attentional bias for threat information and anxiety sensitivity in a nonclinical sample
Bibliographic record
Abstract
This study investigated the effects of Anxiety Sensitivity-(AS) on performance of the Stroop task using several distinct word categories. The conditions included words chosen to represent the following word categories: neutral, positive, depressive, physical threat, cognitive threat, and social threat. In addition, participants had one condition where they simply read color words presented in black ink, another where they named the color of color patches, and the traditional Stroop condition where they were required to name the color in which color words were presented. Participants were divided into low, medium, and high AS groups based on quartile splits from the ASI-R, and it was predicted that an ASI by word category interaction would occur on the Stroop task with groups differing in the color naming task only on the 3 anxiety threat categories, and not on neutral, positive, or depressive word categories. The dependent variables were either simple reading latencies, or indexes that were meant to controlfor general reading, proficiency (the black color word reading condition) or color naming proficiency (the condition where color patches were named). (Abstract shortened by UMI.)
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".