Context Effects in Cognitive Effort Evaluation
Bibliographic record
Abstract
When given a choice, people will avoid cognitively effortful courses of action because the experience of effort is evaluated as aversive and costly. At the same time, the subjective values of goods, actions, and experiences often depend on context in which they are evaluated. To probe the extent to which evaluation of cognitive effort is also context-dependent, we had participants learn associations between unique stimuli and subjective demand levels across low-demand and high-demand contexts and probed their subsequent preferences subjective ratings for these stimuli. We examined demand preferences using a forced-choice paradigm as well subjective effort ratings, taken both online and offline, across the low-demand and high-demand contexts. When choosing between two stimuli objectively identical in terms of demand, participants showed a clear preference for the stimulus learned in the low- versus high-demand context, and rated this stimulus as more subjectively effortful than the low-demand context in on-line, but not off-line ratings, suggesting an assimilation effect. Finally, we observed that the extent to which individual participants who exhibited stronger assimilation effects in off-line demand ratings were more likely to manifest an assimilation effect in demand preferences. Broadly, our findings suggest that effort evaluations occur in a context-dependent manner and are specifically assimilated to the broader context in which they occur.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".