Do reward-processing deficits in schizophrenia-spectrum disorders promote cannabis use? An investigation of physiological response to natural rewards and drug cues
Bibliographic record
Abstract
BACKGROUND: Dysfunctional reward processing is present in individuals with schizophrenia-spectrum disorders (SSD) and may confer vulnerability to addiction. Our objective was to identify a deficit in patients with SSD on response to rewarding stimuli and determine whether this deficit predicts cannabis use. METHODS: We divided a group of patients with SSD and nonpsychotic controls into cannabis users and nonusers. Response to emotional and cannabis-associated visual stimuli was assessed using self-report, event-related potentials (using the late positive potential [LPP]), facial electromyography and skin-conductance response. RESULTS: Our sample comprised 35 patients with SSD and 35 nonpsychotic controls. Compared with controls, the patients with SSD showed blunted LPP response to pleasant stimuli (p = 0.003). Across measures, cannabis-using controls showed greater response to pleasant stimuli than to cannabis stimuli whereas cannabis-using patients showed little bias toward pleasant stimuli. Reduced LPP response to pleasant stimuli was predictive of more frequent subsequent cannabis use (β = -0.24, p = 0.034). LIMITATIONS: It is not clear if the deficit associated with cannabis use is specific to rewarding stimuli or nonspecific to any kind of emotionally salient stimuli. CONCLUSION: The LPP captures a reward-processing deficit in patients with SSD and shows potential as a biomarker for identifying patients at risk of heavy cannabis use.
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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 | 0.000 | 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) | 0.002 | 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".