A Proof of Concept Study to Assess the Imbalance of Self-Reported Wanting and Liking as a Predictor of Problematic Addictive Behaviors
Bibliographic record
Abstract
Abstract Tolerance, one of the key features of addiction, is a highly debated criterion for behaviors, considered controversial and difficult to assess. The Incentive-Sensitization Theory of Addiction (IST) provides a robust empirical background on the dynamics of the motivational and hedonic systems underlying addiction, reflecting tolerance. The aim of this proof-of-concept study was to introduce wanting and liking as an IST-based measure of tolerance. Survey data were analyzed on two potentially problematic substance use behaviors (alcohol and nicotine use) and seven potentially problematic behaviors (eating, gaming, pornography use, social media use, internet use, television series watching, and working) of 774 participants (517 women, Mage = 35.8 years, SD = 11.84), using linear regression models. The models describing the relationship between usage frequency and the difference between self-reported wanting and liking were significant for all of the investigated substance use and potential behavioral addictions. As a general pattern, the balance of wanting and liking was disrupted with increasing usage/behavior frequency, with a steady increment in wanting in all investigated cases. The findings indicate that the proposed approach holds promise as an empirically robust tool for addiction research, offering the potential to compare substance and behavioral addictions on a unified dimension.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".