Preliminary evidence that alcohol-related cues enhance facial emotion recognition speed and accuracy in polysubstance users
Bibliographic record
Abstract
Purpose: There is evidence of impaired facial emotion recognition (FER) in individuals with substance use disorder (SUD). While previous studies have primarily examined group differences in FER performance, the influence of contextual factors remains poorly understood. This study investigates how alcohol-related stimuli (ARS) influence FER in polysubstance users, aiming to uncover potential mechanisms that could contribute to relapse in alcoholassociated environments. Methods: Eighty-two patients with polysubstance use with cocaine and alcohol as primary drugs receiving treatment at a public addiction service and 45 control participants completed two sequentially tasks administered in a counterbalanced order: a FER task (EMO) and a recognition task (ALC) incorporating ARS. Clinical data - including comorbidities, medications, substance use patterns and craving mesures - were collected through routine clinical documentation, complemented by standardized instruments: the Toronto Alexithymia Scale (TAS-20), Profile of Mood States (POMS), Alcohol Use Disorders Identification Test (AUDIT), and Inventory of Interpersonal Problems (IIP-47) Findings: Patients demonstrated significantly faster reaction times (RTs) for disgust, and enhanced anger recognition accuracy following ARS exposure compared to controls . These results suggest alcohol-context modulation of FER in polysubstance users. The authors discuss the implications and relevance of these findings for relapse prevention.
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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) | 0.004 | 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".