PROBABILITY DISCOUNTING OF GAINS AND LOSSES: IMPLICATIONS FOR RISK ATTITUDES AND IMPULSIVITY
Bibliographic record
Abstract
Sixty college students performed three discounting tasks: probability discounting of gains, probability discounting of losses, and delay discounting of gains. Each task used an adjusting-amount procedure, and participants' choices affected the amount and timing of their remuneration for participating. Both group and individual discounting functions from all three procedures were well fitted by hyperboloid discounting functions. A negative correlation between the probability discounting of gains and losses was observed, consistent with the idea that individuals' choices on probability discounting tasks reflect their general attitude towards risk, regardless of whether the outcomes are gains or losses. This finding further suggests that risk attitudes reflect the weighting an individual gives to the lowest-valued outcome (e.g., getting nothing when the probabilistic outcome is a gain or actually losing when the probabilistic outcome is a loss). According to this view, risk-aversion indicates a tendency to overweight the lowest-valued outcome, whereas risk-seeking indicates a tendency to underweight it. Neither probability discounting of gains nor probability discounting of losses were reliably correlated with discounting of delayed gains, a result that is inconsistent with the idea that probability discounting and delay discounting both reflect a general tendency towards impulsivity.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".