Measuring conscious monitoring and metacognition at the start, middle and end of a reaching movement
Bibliographic record
Abstract
The ability to monitor our arm position during goal-directed behaviour allows us to bring our limb to a target as accurately as possible. Despite our success in executing accurate movements, some work suggests that individuals have limited access to information about their limb position (Charles et al., 2020). However, contradictory evidence from metacognition research indicates that people are able to accurately monitor their movements. In these studies, individuals are asked to rate their confidence after making judgements about their movements and tend to give higher confidence ratings when they are correct, showing some capacity for self-monitoring (Arbuzova et al., 2021). These conflicting results suggest that we do not monitor an entire movement. Participants (n=43) made reaching movements toward targets on a screen. They were then presented with two movement paths: one being their actual trajectory and the other being a visually deviated version. Here, we manipulated the location that the deviation was implemented (i.e., start, middle, or end of the path). Participants were asked to determine which trajectory was their own, while also rating their confidence in their response. Overall, accuracy was lower than expected. Nevertheless, accuracy and confidence were higher when deviations occurred in the middle and end of the movement as opposed to the start, suggesting that participants were more aware of their true limb position at the middle and end of their reach. In addition, metacognitive sensitivity was greater during the middle and end implying that at these locations, individuals’ confidence ratings better discriminated between correct and incorrect responses, indicating appropriate self-monitoring. We conclude that people have a remarkable blindness to the properties of their own movements. As well, monitoring of a limb is significantly reduced at the start of a movement suggesting reduced attention to limb position at this time, possibly due to movement programming demands.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".