On the influence of evaluation context on judgments of effort.
Bibliographic record
Abstract
Cognitive effort is a central construct in our lives, yet our understanding of the processes underlying our judgments of effort are limited. Recent work has suggested that our judgments of effort are sensitive to the context in which they are made (i.e., the judgment context). Using a cognitive task and stimulus set that has produced a reliable dissociation between judgments of effort and cognitive demand (as measured by performance and other indirect measures of demand), we examined whether evaluation context might be able to eliminate this dissociation (i.e., bring judgments of effort more in line with measures of cognitive demand). To address this question, we conducted four experiments manipulating a number of aspects of the judgment context including, principally, a manipulation of joint versus separate evaluation; whether the judgment was prospective, or retrospective; and whether prospective judgments were made with or without having experienced the cognitive task. Additionally, we collected objective demand measures and examined participants' self-reported reasons for their judgments of effort across the joint and separate evaluation contexts. Results demonstrated that evaluation context has a marked effect on judgments of effort; however, no judgment context appeared to yield a pattern more similar to what is found using measures of cognitive demand. Moreover, the reasons individuals cited for their judgments varied across evaluation contexts. Implications of the present work for our understanding of judgments of effort are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.008 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".