The neural correlates of marijuana addiction: differences in the processing of drug-related and emotional pictures between addicted versus healthy controls
Bibliographic record
Abstract
This project aims to understand the electrophysiology of emotion and drug stimulus processing in marijuana addicts relative to healthy participants. A literature review of emotion processing is provided, and roles for modifications of the Stroop task in exploring this phenomenon are discussed. Current findings related to understanding the neural correlates of addiction behaviour are also reviewed and the structure and function of the OFC and ACC are summarized. This review provides a basis for the current study, where EEG is used in conjunction with a modified Stroop paradigm to understand the timing of neural events associated with cue reactivity to salient visual stimuli. The method of the current study is presented, and the results of the current project are described. Behavioural data regarding Stroop interference produced by the various categories of stimuli and the degree of self-reported craving experienced by participants during the paradigm are examined, as well as the electrophysiological data obtained from both groups. Lastly, the implications of these findings and future directions that will help to better understand the electrophysiology of addiction are outlined.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".