Judgements of effort as a function of post-trial versus post-task elicitation
Bibliographic record
Abstract
Cognitive effort is a central construct in our lives, yet our understanding of the processes underlying our perception of effort is limited. Performance is typically used as one way to assess effort in cognitive tasks (e.g., tasks that take longer are generally thought to be more effortful); however, Dunn and Risko reported a recent case where such "objective" measures of effort were dissociated from judgements of effort (i.e., subjective effort). This dissociation occurred when participants either made their judgements of effort after the task (i.e., reading stimuli composed of rotated words) or without ever performing the task. This leaves open the possibility that if participants made their judgements of effort more proximal to the actual experience of performing the task (e.g., right after a given trial) that these judgements might better correspond to putatively "objective" measures of effort. To address this question, we conducted two experiments replicating Dunn and Risko with additional probes for post-trial judgements of effort (i.e., a judgement of effort made right after each trial). Results provided some support for the notion that judgements of effort more closely follow reading times when made post-trial as opposed to post-task. Implications of the present work for our understanding of judgements of effort are discussed.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".