Information about task progress modulates cognitive demand avoidance
Bibliographic record
Abstract
People tend to avoid engaging in cognitively demanding tasks unless it is ‘worth our while’—that is, if the benefits outweigh the costs of effortful action. Yet, we seemingly partake in a variety of effortful mental activities (e.g. playing chess, completing Sudoku puzzles) because they impart a sense of progress. Here, we examine the possibility that information about progress—specifically, the number of trials completed of a demanding cognitive control task, relative to the total number of trials to be completed—reduces individuals’ aversion to cognitively effort activity, across four experiments. In Experiment 1, we provide an initial demonstration that presenting progress information reduces individuals’ avoidance of cognitively demanding activity avoidance using a variant of the well-characterized Demand Selection Task (DST). The subsequent experiments buttress this finding using a more sophisticated within-subjects versions of the DST, independently manipulating progress information and demand level to further demonstrate that, 1) people prefer receiving information about temporal progress in a task, and 2) all else being equal, individuals will choose to exert greater levels of cognitive effort when it confers information about their progress in a task. Together, these results suggest that progress information can motivate cognitive effort expenditure and, in some cases, override individuals’ default bias towards demand avoidance.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".