Generalized anxiety disorder and selective attention: An unsuccessful replication of Yiend et al., (2015) in a student population
Bibliographic record
Abstract
Generalized Anxiety Disorder (GAD) is an anxiety disorder that is believed to affect attention (Stein, M. B., & Sareen, J. (2015). Generalized anxiety disorder. New England Journal of Medicine, 373(21), 2059–2068. https://doi.org/10.1056/NEJMcp1502514; Yiend, J., Mathews, A., Burns, T., Dutton, K., Fernández-Martín, A., Georgiou, G. A., Luckie, M., Rose, A., Russo, R., & Fox, E. (2015). Mechanisms of selective attention in generalized anxiety disorder. Clinical Psychological Science, 3(5), 758–771. https://doi.org/10.1177/2167702614545216). Previous literature has found that selective attention is changed when someone perceives threatening stimuli, such as an angry face, and that those with anxiety disorders, may have a heightened or delayed response to threatening stimuli (Richards, H. J., Benson, V., Donnelly, N., & Hadwin, J. A. (2014). Exploring the function of selective attention and hypervigilance for threat in anxiety. Clinical Psychology Review, 34(1), 1–13. https://doi.org/10.1016/j.cpr.2013.10.006; Stevens, C., & Bavelier, D. (2012). The role of selective attention on academic foundations: A cognitive neuroscience perspective. Developmental Cognitive Neuroscience, 2, S30–S48. https://doi.org/10.1016/j.dcn.2011.11.001), which may alter how fast a presented task is completed (Yiend et al., 2015). The present study aimed to reproduce findings by Yiend et al. (2015), which identified an unexpected pattern in those with GAD: faster disengagement from angry faces compared to positive (happy, neutral) faces. The present study recruited a larger (nonclinical) sample from a student population to achieve greater statistical power. None of the findings reported by Yiend and colleagues (Experiment 1; 2015) were replicated in a student sample. The implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".