Drawn to Distraction: Anxiety Impairs Neural Suppression of Known Distractor Features in Visual Search
Bibliographic record
Abstract
When searching for a target, it is possible to suppress the features of a known distractor. This suppression may prevent distractor processing altogether or only after the distractor initially captures attention (i.e., search and destroy). However, suppression may be impaired in individuals with attentional control deficits, such as in high anxiety. In this study (n = 48), we used ERPs to examine the time course of attentional enhancement and suppression when participants were given pretrial information about target or distractor features. Consistent with our hypothesis, we found that individuals with higher levels of anxiety had lower neural measures of suppressing the template-matching distractor, instead showing enhanced processing. These findings indicate that individuals with anxiety are more likely to use a search-and-destroy mechanism of negative templates-highlighting the importance of attentional control abilities in distractor-guided search.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".