Preserved Metacognition for Undetected Visuomotor Deviations
Bibliographic record
Abstract
Humans can successfully correct deviations of movements without conscious detection of such deviations, suggesting limited awareness of movement details. We ask whether such limited awareness impairs confidence (metacognition). We recorded functional magnetic resonance imaging data while 31 human female and male participants detected cursor deviations during a visuomotor reaching task and rated their confidence retrospectively. We show that participants monitor a summary statistic of the unfolding visual feedback (the peak cursor error) to detect visuomotor deviations and adjust their confidence ratings, even when they report being unaware of a deviation. Crucially, confidence ratings were as metacognitively efficient for aware and unaware deviations. At the neural level, activity in the ventral striatum tracks high confidence, whereas a broad network encodes cursor error but not confidence. These findings challenge the notion of limited conscious action monitoring and uncover how humans monitor their movements as they unfold, even when unaware of ongoing deviations. SIGNIFICANCE STATEMENTWe are unaware of the small corrections we apply to our movements as long as our goals are achieved. Here, although we replicate the finding that participants deny perceiving small deviations they correct, we show that their confidence reliably reflects the presence or absence of a deviation. This observation shows they can metacognitively monitor the presence of a deviation, even when they deny perceiving it. We also describe the hemodynamic correlates of confidence ratings. Our study questions the extent to which humans are unaware of the details of their movements; describes a plausible mechanism for metacognition in a visuomotor task, along with its neural correlates; and has important implications for the construction of the sense of self.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".