Social threat attentional bias in childhood: Relations to aggression and hostile intent attributions
Bibliographic record
Abstract
The goal of this study was to examine the ways attentional bias to social threat-measured across multiple attentional processes-is related to both child aggression and a well-established cognitive correlate of aggression (namely, hostile intent attributions). A community sample of 211 children (51% male; 9-12 years; 55% Caucasian) participated in our cross-sectional correlational design. Social threat attentional bias was measured through task performance on dot-probe, attentional shifting, and temporal order judgment tasks; each task measured different attentional processes. Aggression was measured by parent- and child-report. Hostile intent attributions were measured through child responses to vignettes involving peer conflict or rejection. Attentional bias to social threat within early phases of attentional processing (i.e., attentional prioritization; stimuli presented for <200 ms in temporal order judgment task) was significantly and positively related to both aggression and hostile intent attributions. Attentional bias to social threat within attentional orienting (stimuli presented for 500 ms in dot-probe task) was positively and significantly related to hostile intent attributions. Attentional bias to social threat within attentional shifting (stimuli presented for multiple seconds) was not significantly related to aggression or hostile intent attributions. Higher levels of aggression and of hostile intent attributions were associated with an attentional bias to social threat within early, but not later, phases of attentional processing. These results suggest specificity in identifying dysfunctional attentional processes that may underlie aggression and aggression-related cognitive biases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".