A Targeted Review of the Neurobiology and Genetics of Behavioural Addictions: An Emerging Area of Research
Bibliographic record
Abstract
This review summarizes neurobiological and genetic findings in behavioural addictions, draws parallels with findings pertaining to substance use disorders, and offers suggestions for future research. Articles concerning brain function, neurotransmitter activity, and family history and (or) genetic findings for behavioural addictions involving gambling, Internet use, video game playing, shopping, kleptomania, and sexual activity were reviewed. Behavioural addictions involve dysfunction in several brain regions, particularly the frontal cortex and striatum. Findings from imaging studies incorporating cognitive tasks have arguably been more consistent than cue-induction studies. Early results suggest white and grey matter differences. Neurochemical findings suggest roles for dopaminergic and serotonergic systems, but results from clinical trials seem more equivocal. While limited, family history and genetic data support heritability for pathological gambling and that people with behavioural addictions are more likely to have a close family member with some form of psychopathology. Parallels exist between neurobiological and genetic and family history findings in substance and nonsubstance addictions, suggesting that compulsive engagement in these behaviours may constitute addictions. To date, findings are limited, particularly for shopping, kleptomania, and sexual behaviour. Genetic understandings are at an early stage. Future research directions are offered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".