Grouping and gambling: A Gestalt approach to understanding the gambler's fallacy.
Why this work is in the frame
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Bibliographic record
Abstract
The gambler's fallacy was examined in terms of grouping processes. The gambler's fallacy is the tendency to erroneously believe that for independent events, recent or repeated instances of an outcome (e.g., a series of "heads" when flipping a coin) will make that outcome less likely on an upcoming trial. Grouping was manipulated such that a critical trial following a run of heads or tails was grouped together with previous trials (i.e., the last trial of "Block 1") or was the first trial of another group (the first trial of "Block 2"). As predicted, the gambler's fallacy was evident when the critical trial was grouped with the previous trials, but not when it was arbitrarily grouped with the next block of trials. Discussion centres on the processes underlying the gambler's fallacy and practical implications of these findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it