Revisiting the dynamics of arousal in gambling: The interplay of subjective and objective measures in a community sample with gambling involvement
Bibliographic record
Abstract
Verdejo-Garcia and colleagues (2012) developed an interoceptive-deficit hypothesis in addiction which we might be able to apply to gambling contexts by testing the coupling of physiological responses to gambling episodes with the subjective reports of these physiological responses. Here we specifically assume that the coupling of those measures changes as a function of gambling involvement. We assume that all participants will show larger mean changes in subjective experience and arousal responses to wins compared to losses. However, we predict that there will be a decoupling of physiological arousal and subjective ratings as a function of gambling involvement. This interoceptive deficit may thus contribute to more persistent gambling (i.e. loss chasing) based on perceived excitement. If we find support for this hypothesis, we further predict that losses, as an aversive outcome, will promote the decoupling between physiological arousal and subjective ratings: the more gambling involvement, the more people report to be aroused by a loss, even though their physiological responses do not support these feelings (Verdejo-Garcia et al., 2012).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".