Attentional Bias Deficits in Adolescent Suicide Attempters During an Emotional Stroop Task: An ERP Study
Bibliographic record
Abstract
There is increasing evidence that, in adolescence, attentional bias plays a critical role in the vulnerability for suicidal behaviour. No studies to date have investigated the neurophysiological correlates of attentional bias in adolescent suicidality. The present study uses event-related potentials (ERPs) to investigate such processing in inpatient adolescents admitted for an acute suicide crisis using an Emotional Stroop Task (EST). In this task, participants are asked to name the colour of words varying in emotional valence (positive, negative, neutral, suicide-related). Suicidal individuals are hypothesised to be more preoccupied by the context of the suicide-related stimuli, which may interfere with their ability to perform the colour naming task. Seventeen adolescents with acute suicidal behaviour and 17 age- and gender-matched healthy controls performed an EST while ERPs were recorded. Suicide attempters showed increased reaction times to suicide-related words compared to other emotion categories, while the controls did not. The amplitude of the early posterior negativity (EPN) was not significantly different across groups or emotional valence. A double peak P3 (early-P3 and late-P3) was observed in both groups. Both the early- and late-P3 were significantly reduced in amplitude in the suicide attempter group compared to the control group, regardless of emotional valence. The late-P3 latency was also significantly delayed in the suicide attempters compared to controls. The behavioural findings support the attentional bias theories of suicide attempters and extend these findings to adolescents. Furthermore, large early- and late-P3 provide evidence that cognitive strategies employed by two groups did markedly differ.
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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) | 0.001 | 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".