Underlying cognitions in the selection of lottery tickets
Bibliographic record
Abstract
There is evidence that the faulty cognitions underlying an individual's playing behavior maintains and supports their gambling behavior. Sixty undergraduate students completed the South Oaks Gambling Screen (SOGS), a measure to assess pathological gambling, and a questionnaire ascertaining the type and frequency of their gambling activities. Sixteen Loto 6/49 tickets were presented to participants and ranked according to their perceived likelihood of being the winning ticket. The numbers on the tickets were categorized as: long sequences (e.g., 1-2-3-4-5-6), patterns and series in a pseudo-psychological order (e.g., 16-21-26-31-36-41), unbalanced (e.g., six numbers from 1-24 or 25-49), and those appearing to be random (e.g., 11-14-20-29-37-43). Verbal protocols of ticket selections were ranked into eight heuristics. Results revealed that for the entire sample the greatest percentage of tickets chosen for the first four selections were "random" tickets. Further, the most commonly cited reason for selecting and changing a lottery ticket was perceived randomness. The results are discussed with reference to the cognitions used when purchasing lottery tickets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".