Evaluating Effort: Influences of Evaluation Mode on Judgments of Task‐specific Efforts
Bibliographic record
Abstract
Abstract The claim that humans adapt their actions in ways that avoid effortful processing (whether cognitive or physical) is a staple of various theories of human behavior. Although much work has been carried out focusing on the determinants of such behaviors, less attention has been given to how individuals evaluate effort. In the current set of experiments, we utilized the general evaluability theory to examine the evaluability of effort by examining subjective value functions across different evaluation modes. Individuals judged the anticipated effort of four task‐specific efforts indexed by stimulus rotation, items to be remembered, weight to be lifted, and stimulus degradation across joint (i.e., judged comparatively) and single evaluation modes (i.e., judged in isolation). General evaluability theory hypothesizes that highly evaluable attributes should be consistently evaluated (i.e., demonstrate similar subjective value functions) between the two modes. Across six experiments, we demonstrate that the perceived effort associated with items to be remembered, weight to be lifted, and stimulus degradation can be considered relatively evaluable, while the effort associated with stimulus rotation may be relatively inevaluable. Results are discussed within the context of subjective evaluation, internal reference information, and strategy selection. In addition, methodological implications of evaluation modes are considered. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".