Psychophysical dissection of temporal error monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it