Attentional biases toward real images and drawings of negative faces
Bibliographic record
Abstract
Allocation of attention is affected by internal emotional states, such as anxiety and depression. Attention captured by real images of negative faces can be quantified by emotional probe tasks. Attentional bias studies suggest that attentional bias toward negative emotional faces can occur regardless of whether the faces are photographs of real faces or line drawings. However, it is true that there are critical differences among these types of face stimuli in terms of their physical properties (e.g., exaggerated facial parts or simplified and emphasized contours) and associated facial processing. It is reasonable to assume that such differences would be reflected in the attentional biases elicited by the types of face images. The present study investigated whether attentional bias toward drawings of negative faces (line drawn faces and cartoon faces) differs from that of photographs of real faces. Non-clinical university students indicated their levels of anxiety and depression via self-report questionnaires (STAI-T and S and BDI-II) and completed a probe discrimination task under three face image conditions (the photographs of real face, line drawn face, and cartoon face conditions) in a between-participants design. We examined correlations between bias scores and self-report scores. Significant correlations were found between bias scores and scores on the self-reported STAI-S (r = .266, p = .040) and BDI-II (r = .321, p = .012) under the real-face condition. However, both line drawn faces and cartoon faces were only weakly correlated with self-report scores. The present results suggest that photographs of real faces are more likely to elicit attentional bias than non-real faces, and are thus preferable for probe tasks investigating attentional bias related to facial stimuli in non-clinical adult populations.
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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 | 0.004 |
| 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) | 0.003 | 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".