Cue Awareness in Avoiding Effortful Control (in press)
Bibliographic record
Abstract
Based on a cue-based metacognitive account, cognitive effort is the result of an inferential evaluation made over explicitly available cues. Following from this account, we present here a pre-registered experiment that tested the specific hypothesis that explicit awareness of cues that are aligned with cognitive demand is a prerequisite in avoiding effortful lines of action. We attempted to modulate levels of effort avoidance behavior by introducing an incentive (between-subjects) to monitor two lines of action that, unbeknownst to individuals, varied in the probability of a task switch. Importantly, previous research has demonstrated that the difference in these probabilities is relatively opaque to individuals. We did not find strong evidence for our incentive manipulation having an effect on demand avoidance as indexed by individuals’ choices in a block of the task where avoiding effort was instructed. However, when considering awareness generally, we do find that being aware of the task-switching cue appears to increase the likelihood of demand avoidance. We consider these results within the context of the metacognition of cognitive effort.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".