Does the habit theory of addictions extend to disordered gambling?
Bibliographic record
Abstract
Purpose of review: ‘Habit theory’ is a pervasive framework that describes addiction as a transition from goal-directed use (e.g. of drugs) to a habitual response, accompanied by a neurobiological shift in fronto-striatal brain circuitry. As a theory that has been explored in the context of substance addictions, this article summarizes recent work extending habit theory to gambling behavior and gambling disorder.Recent findings: Relevant research falls into two main themes. First, studies have compared behavioral markers of habit (e.g. two-step task, Pavlovian-to-Instrumental Transfer) in groups with and without gambling problems. These studies find limited direct support for the hypothesis. Second, psychological research has examined habit-like behaviors in naturalistic gambling. These studies find behavioral expressions consistent with habit formation, primarily during engagement with slot machines, but these studies are yet to test key tenets of habit theory such as outcome devaluation. Summary: Modern gambling products including slot machines and in-play sports betting involve a fast speed of betting and intense audiovisual feedback that creates a rich learning environment, which may be highly amenable to habit formation. Further research is needed to develop and validate new tools for testing habit strength and habit acquisition in the context of gambling.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".