Measuring delay discounting in a crowdsourced sample: An exploratory study
Bibliographic record
Abstract
Delay discounting is a measure of preferences for smaller immediate rewards over larger delayed rewards. Discounting has been assessed in many ways; these methods have variably and inconsistently involved measures of different lengths (single vs. multiple items), forced-choice methods, self-report methods, online and laboratory assessments, monetary and non-monetary compensation. The majority of these studies have been conducted in laboratory settings. However, over the past 20 years, behavioral data collection has increasingly shifted online. Usually, these experiments involve completing short tasks for small amounts of money, and are thus qualitatively different than experiments in the lab, which are typically more involved and in a strongly controlled environment. The present study aimed to determine how to best measure future discounting in a crowdsourced sample using three discounting measures (a single shot measure, the 27-item Kirby Monetary Choice Questionnaire, and a one-time Matching Task). We examined associations of these measures with theoretically related variables, and assessed influence of payment on responding. Results indicated that correlations between the discounting tasks and conceptually related measures were smaller than in prior laboratory experiments. Moreover, our results suggest providing monetary compensation may attenuate correlations between discounting measures and related variables. These findings suggest that incentivizing discounting measures changes the nature of measurement in these tasks.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".