Neuronal and Psychological Underpinnings of Pathological Gambling
Bibliographic record
Abstract
Like in the case of drugs, gambling hijacks reward circuits in a brain which is not prepared to receive such intense stimulation. Dopamine is normally released in response to reward and uncertainty in order to allow animals to stay alive in their environment – where rewards are relatively unpredictable. In this case, behavior is regulated by environmental feedbacks, leading animals to persevere or to give up. In contrast, drugs provide a direct, intense pharmacological stimulation of the dopamine system that operates independently of environmental feedbacks, and hence causes “motivational runaways”. With respect to gambling, the confined environment experienced by gamblers favors the emergence of excitatory conditioned cues, so that positive feedbacks take over negative feedbacks. Although drugs and gambling may act differently, their abnormal activation of reward circuitry generates an underestimation of negative consequences and promotes the development of addictive/compulsive behavior. In Parkinson’s and Huntington’s disease, dopamine-related therapies may disrupt these feedbacks on dopamine signalling, potentially leading to various addictions, including pathological gambling. The goal of this Research Topic is to further our understanding of the neurobiological mechanisms underlying the development of pathological gambling. This eBook contains a cross-disciplinary collection of research and review articles, ranging in scope from animal behavioral models to human imaging studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".