Hostile Attribution Biases and Evaluation of Vocally Enacted Responses to Peer Provocation in Early Adolescents
Bibliographic record
Abstract
ABSTRACT Hostile Attribution Bias (HAB) is the tendency to perceive ambiguous social information as threatening. The social information processing (SIP) model provides a theoretical framework for determining how individuals with HAB perceive, interpret, and make decisions regarding social cues. Although previous work has mapped the association between HAB and youths’ behavioral responses to peer provocation, little attention has been given to how nonverbal cues (e.g., tone of voice) shape youths’ evaluations of different response strategies. The current study explores the association between HAB and youths’ assessments of vocally enacted peer provocation scenarios and responses, focusing on both the “interpretation of cues” (i.e., how youth viewed the provocation) and “selection of response” (i.e., how they evaluated the appropriateness of various responses to the provocation) stages of the SIP model. In an online study, 129 English‐speaking 10–14‐year‐old participants (51.9% female) heard audio recordings of peer provocation and rated them on the perceived threat, hostility, and intent of the provocateur (“interpretation” stage). Additionally, participants heard pre‐recorded audio clips of other teenagers’ “hostile” and “affiliative” responses to each scenario and indicated the appropriateness of these responses for the situation (“selection of response” stage). HAB scores were associated with differential ratings of hostile‐ versus affiliative‐sounding responses, with youth with higher HAB scores rating affiliative responses as less appropriate. Moreover, attributing more hostility, threat, and intentionality to the provocateur was associated with higher ratings of appropriateness for hostile responses only. Findings highlight how youths’ HAB/interpretations of situations are associated with their evaluation of the nonverbal aspects of various responses to provocation.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| 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".