Generalized anxiety disorder and selective attention: An unsuccessful replication of Yiend et al., (2015) in a student population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it