Bibliographic record
Abstract
The recent line of research robustly demonstrated that humans and rodents can keep track of the magnitude and direction of timing errors, composing a temporal error monitoring ability (TEM). However, the degree of dissociation between these two measures of TEM has not been investigated at the level of the underlying mental magnitude metrics. Specifically, we do not know whether the two behavioral manifestations of TEM differentially rely on subjective vs. objective time, whether the discriminability of time intervals relies on ratio and absolute differences, respectively. To this end, we first tested whether behavioral manifestations of TEM depend on relative (cognitive timing) or absolute timing errors (sensorimotor timing). In light of our earlier findings showing differential metacognitive processing of timing errors as a function of different levels of agency, we also tested whether the potential information processing differences in TEM measures differ across different levels of agency of timing errors? In two different datasets, we found that magnitude and direction monitoring of timing errors relied on the absolute (i.e., arithmetic/linear) and relative (i.e., ratio) distances, respectively. These effects were more pronounced for owned versus unowned errors for timing error magnitude monitoring and timing error direction monitoring, respectively. Together, this study demonstrated that the timing error direction monitoring relies more on cognitive timing, whereas error magnitude monitoring relies more on sensorimotor timing.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".