Throwing good effort after bad: Evidence for a sunk-cost effect in cognitive effort-based decision-making
Bibliographic record
Abstract
The sunk-cost effect, an influential decision-making bias elicited by the consideration of past, irrecoverable resource investments toward a choice option, is well-documented for economic decisions. Contrary, evidence for sunk-cost effects for non-monetary costs, such as cognitive effort, has been mixed. Here, we present findings from two experiments in self-reported healthy adults (Exp.1 in-person, n = 36; Exp.2 online, n = 89), in which participants performed an effort investment task designed to assess the impact of past effort exertion on current choices about continuing or abandoning a course of action. Across both experiments, we found that prior resource investment substantially increased participants' willingness to invest further resources (i.e., to exert additional cognitive effort). Moreover, when current choices required higher additional effort exertion, participants were more likely to continue a course of action following larger versus smaller prior effort investments. These findings provide compelling evidence for the existence of an effort-based sunk-cost effect. Further, they highlight the importance of previous effort investments for influencing present choice behavior, indicating the need to refine contemporary cost-benefit models of effort to account for past effort exertion.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".