Predicting Interpersonal Outcomes From Information Processing Tasks Using Personally Relevant and Generic Stimuli: A Methodology Study
Bibliographic record
Abstract
Despite evidence of differential processing of personally relevant stimuli (PR), most studies investigating attentional biases in processing emotional content use generic stimuli. We sought to examine differences in the processing of PR, relative to generic, stimuli across information processing tasks and to validate their use in predicting concurrent interpersonal functioning. Fifty participants (25 female) viewed generic and PR (i.e., their intimate partner's face) emotional stimuli during tasks assessing selective attention (using a modified version of the Spatial Cueing Task) and inhibition (using the Negative Affective Priming task) of emotional content. Ratings of relationship quality were also collected. Evidence of increased selective attention during controlled and greater avoidance during automatic stages of processing emerged when viewing PR, relative to generic, emotional faces. We also found greater inhibition of PR sad faces. Finally, male, but not female, participants who displayed greater difficulty disengaging from the sad face of their partner reported more conflict in their relationships. Taken together, findings from information processing studies using generic emotional stimuli may not be representative of how we process PR stimuli in naturalistic settings.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".