Neural substrates of cognitive flexibility in cocaine and gambling addictions
Bibliographic record
Abstract
BACKGROUND: Individuals with cocaine and gambling addictions exhibit cognitive flexibility deficits that may underlie persistence of harmful behaviours. AIMS: We investigated the neural substrates of cognitive inflexibility in cocaine users v. pathological gamblers, aiming to disambiguate common mechanisms v. cocaine effects. METHOD: Eighteen cocaine users, 18 pathological gamblers and 18 controls performed a probabilistic reversal learning task during functional magnetic resonance imaging, and were genotyped for the DRD2/ANKK Taq1A polymorphism. RESULTS: Cocaine users and pathological gamblers exhibited reduced ventrolateral prefrontal cortex (PFC) signal during reversal shifting. Cocaine users further showed increased dorsomedial PFC (dmPFC) activation relative to pathological gamblers during perseveration, and decreased dorsolateral PFC activation relative to pathological gamblers and controls during shifting. Preliminary genetic findings indicated that cocaine users carrying the DRD2/ANKK Taq1A1+ genotype may derive unique stimulatory effects on shifting-related ventrolateral PFC signal. CONCLUSIONS: Reduced ventrolateral PFC activation during shifting may constitute a common neural marker across gambling and cocaine addictions. Additional cocaine-related effects relate to a wider pattern of task-related dysregulation, reflected in signal abnormalities in dorsolateral and dmPFC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".