Bibliographic record
Abstract
Analogous to false memories for the past, gambling behavior may be influenced by the development of vivid, believed false "memories" for future gambling outcomes. We examined the degree to which believed memory-like representations for future gambling wins and losses were associated with prior substantial win experiences, frequency of gambling, risk for problem gambling, and other types of gambling expectancies. Regular gamblers with and without prior substantial wins rated the strength of a specific outcome expectancy, their belief that substantial gambling wins and losses would occur in the future, and rated the strength of "memory" for future gambling wins and losses. They then described a future win and a future loss and rated memory-like phenomenal characteristics for these events. Prior winners rated future wins as more believable relative to future losses, and rated future gambling outcomes as more memory-like than did gamblers without prior win experiences. Belief and memory for future wins correlated positively with frequency of gambling and positive response expectancies (e.g. relaxation when gambling). Belief and memory for future losses correlated with negative outcome expectancies and endorsement of problem gambling risk. Expecations about future wins and losses are likely influenced by believed memory-like representations for future wins and losses.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".