Testing the Effect of Incentives on Effort Intensity Using Real-Effort Tasks
Bibliographic record
Abstract
We identify seven factors researchers should consider when designing a real-effort task to capture effort intensity (i.e., an effort-intensive task): (1) intrinsic motivation; (2) task-specific skill; (3) task strategies beyond exerting effort; (4) fine unit of performance; (5) within-round task experience across participants; (6) performance trends over time; and (7) task difficulty across rounds. With these factors in mind, we design an experiment to test for incentive effects on effort and performance using three effort-intensive tasks: the decode task, the letter search task, and the slider task. Contrary to our expectation, we find significant variation across tasks in our ability to detect incentive effects and limit the effects of the seven factors above, with the strongest evidence coming from the slider task. Our list of factors helps researchers design effort-intensive tasks that allow them to conduct a more effective test of theory. At the same time, our results highlight the difficulty in effectively designing effort-intensive tasks.
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How this classification was reachedexpand
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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".