Dimensions of early life adversity and cognitive processing of emotion in youth
Bibliographic record
Abstract
BACKGROUND: Early life adversity (ELA) is a leading risk factor for psychopathology. The Dimensional Model of Adversity and Psychopathology (DMAP) elucidates processes altered by ELA and central to this association. Specifically, DMAP posits early experiences of deprivation alter cognitive and emotional processes in ways distinct from early experiences of threat. While evidence suggests that deprivation and threat predict alterations in cognitive and emotional processes, respectively, the influence of these dimensions on cognitive processing across emotionally valenced material remains unexamined. OBJECTIVE: This work is the first to investigate associations of deprivation versus threat on cognitive processing of multiple emotions (happy, sad, angry, and neutral facial expressions) and the time course of processing in a sample of youth. PARTICIPANTS AND SETTING: = 12.85) were recruited from Vancouver. METHODS: Deprivation and threat were measured using the Traumatic Events Screening Inventory for Children (TESI-C), an interview-based measure assessing the instance and severity of 30+ experiences of ELA. Cognitive processing was measured using the Affective Posner Task, which assesses attentional biases and raw reaction times for happy, sad, angry, and neutral facial expressions. RESULTS: Interestingly, experiences of deprivation were associated with early attentional processing deficits regardless of valence, rs ≥ 0.22, ps ≤ 0.046, whereas experiences of threat were associated with late attentional biases for emotional material, Bs ≥ |4.15|, ps ≤ 0.036. CONCLUSIONS: Findings advance theoretical models of ELA by elucidating the nature and time course of cognitive and emotional alterations following deprivation and threat, and, if replicated, suggest the importance of cognitive processing of emotion in early interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".