Altering the Shape of Punishment Distributions Affects Decision Making in a Modified Iowa Gambling Task
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Bibliographic record
Abstract
ABSTRACT Neuroeconomics research has shown that preference for gambling is altered by the statistical moments (mean, variance, and skew) of reward and punishment distributions. Although it has been shown that altered means can affect feedback‐based decision making tasks, little is known if the variance and skew will have an effect on these tasks. To investigate, we systematically controlled the variance (high, medium, and low) and skew (negative, zero, and positive) of the punishment distributions in a modified version of the Iowa Gambling Task. The Iowa Gambling Task has been used extensively in both academic and clinical domains to understand decision making and diagnose decision making impairments. Our results show that decision making can be altered by an interaction of variance and skew. We found a significant decrease over trials in choices from the decks with high variance and asymmetrically skewed punishments and from the decks with low variance and zero skew punishments. These results indicate that punishment distribution shape alone can change human perception of what is optimal (i.e., mean expected outcome) and may help explain what guides our day‐to‐day decisions. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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 it