Humans can monitor trial-based but not global timing errors: Evidence for relative judgements in temporal error monitoring
Bibliographic record
Abstract
Humans can monitor the magnitude and direction of their temporal errors in individual trials. Based on the predictions of our model of temporal error monitoring that rely on a relative comparison of internal clock readings, we predict that participants would monitor their timing errors in individual trials, but not the direction of their global timing errors without external feedback. One study has indeed found that accurate self-monitoring of average timing biases required external feedback with directional information. The current study investigates how different sources of feedback (i.e., internal or external) affect performance in the self-monitoring of average timing bias. Four groups of participants were tested in a temporal reproduction task. Participants in the self-evaluation condition evaluated the direction and size of their time reproduction errors in individual trials. In the accurate feedback condition, participants received explicit trial-based feedback regarding the direction of their error while participants in the partially accurate feedback condition received trial-based feedback according to the accuracy of short-long judgements of another participant in the self-evaluation condition. Participants in the control condition reproduced only the target duration without making any judgements regarding their reproduction performance or receiving any external feedback about it. Results showed that while participants accurately monitor timing errors in individual trials, in none of the experimental conditions were they more accurate than the chance level in terms of evaluating the direction of their average temporal bias. We discuss these results in terms of the temporal error monitoring model introduced by Akdoğan and Balcı. Thus, our findings suggest that external directional feedback does not have any informational value for global temporal bias judgements above and beyond internal self-monitoring.
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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 | 0.007 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".