Judgments of effort and associated cues are influenced by stimulus context
Bibliographic record
Abstract
The experience of cognitive effort is ubiquitous as well as influential; however, our understanding of how judgments of effort are influenced by contextual change is currently limited. Recent work has suggested that explicit cue reports immediately after provision of judgments of effort are sensitive to the evaluation context in which the judgment is made (Ashburner & Risko, 2022). We extend this research here by examining whether a "mixed" vs. "pure" stimulus context (i.e., experience with multiple stimulus types vs. a single stimulus type) would also influence judgments of effort. Furthermore, using explicit cue reports, we investigated whether the cues used to make these judgments were likewise influenced by the stimulus context. Results demonstrated that the pattern of effort judgments and the explicit cue reports changed markedly across stimulus context. Implications of these results in terms of better understanding how individuals make judgments of effort are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".