Examining children's ability to delay reward: Is the delay discounting task a suitable measure?
Bibliographic record
Abstract
Abstract Discounting the value of delayed rewards has primarily been measured in children with the delay of gratification task and in adolescents and adults with the delay discounting task. In the present study, we assessed the suitability of the delay discounting task as a measure of temporal discounting in children. A sample of 7‐ to 9‐year‐olds (N = 98) completed a delay discounting task, a delay of gratification task, a sensation seeking measure, and IQ measures. In addition, teacher‐based assessments of attention‐deficit/hyperactivity disorder traits were measured. The results indicated that the majority of children produced meaningful data on the discounting task and discounted rewards hyperbolically. Children with an elevated risk of attention‐deficit/hyperactivity disorder showed a trend towards discounting future rewards on the delay discounting task more steeply than did those at low risk. However, delay discounting was unrelated to either delay of gratification or sensation seeking. We interpret these results as providing some support for the use of delay discounting as a measure of intertemporal choice in children, although the results also suggest that delay discounting and delay of gratification tasks may tap different processes in this population.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".