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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".