Attentional biases for dynamic stimuli in emerging adults with anxiety: A preliminary eye-tracking study
Bibliographic record
Abstract
While attentional biases towards negative stimuli have previously been linked to the development and maintenance of anxiety disorders, a current limitation of this research involves the use of static images for stimuli, as they cannot adequately depict the dynamic nature of real-life interactions. Since attentional biases in those with elevated anxiety remain understudied using more naturalistic stimuli, such as dynamic social videos, the purpose of this explorative study was to use novel dynamic stimuli and modern eye-tracking equipment to further investigate negative attentional biases in anxious emerging, female adults. Non-clinical participants (N = 62; mean age = 20.44 years; biologically female) completed validated questionnaires regarding their anxiety symptoms and completed a free-viewing task by watching 30-s video clips while having their eye movements tracked. The video clips were shown in side-by-side pairs (i.e., positive-neutral, negative-neutral, and positive-negative) on a split screen without audio. Overall, participants fixated more quickly on emotional videos (i.e., positive and negative) over neutral ones, with more anxious participants orienting their gaze faster to the videos, regardless of content. Moreover, individuals with greater self-reported anxiety spent more time gazing at negative videos in negative-neutral pairings, highlighting that emerging female adults with increased anxiety symptoms may show a negative attention bias when viewing social interactions. Importantly, by incorporating novel, dynamic stimuli, we expand upon prior research on attentional biases, with the potential to adapt this approach for novel interventions that may ultimately help those suffering from anxiety.
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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 | 0.000 | 0.002 |
| 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".