Neuronal and Psychological Underpinnings of Pathological Gambling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it