Examining the effects of gambling-relevant cues on gambling outcome expectancies
Bibliographic record
Abstract
There is a consensus in the addictions literature that exposure to addiction-relevant cues can precipitate a desire to engage, or actual engagement, in the addictive behaviour. Previous work has shown that exposure to gambling-relevant cues activates gamblers’ positive gambling outcome expectancies (i.e. their beliefs about the positive results of gambling). The current study examined the effects of a new, arguably more ecologically valid cue manipulation (i.e. exposure to a gambling lab environment vs. sterile lab environment) on 61 regular gamblers’ explicit and implicit gambling outcome expectancies. The authors first tested the internal consistency of their implicit reaction time measure of gambling outcome expectancies, the Affective Priming Task. Split-half reliabilities were satisfactory to high (.72 to .88), highlighting an advantage of this task over other characteristically unreliable implicit cognitive measures. Unexpectedly, no predicted between-lab condition differences emerged on most measures of interest, suggesting that peripheral environmental cues that are not the focus of deliberate attentional allocation may not activate positive outcome expectancies. However, there was some evidence that implicit negative gambling outcome expectancies were activated in the gambling lab environment. This latter finding holds clinical relevance as it suggests that presenting peripheral gambling-related cues while treating problem gamblers may facilitate processing of the negative consequences 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".