Judging endpoint bias: The need for voluntary control
Bibliographic record
Abstract
Reaching to objects involves comparing limb and target positions, such that online judgements of undershooting or overshooting contribute to the accurate completion of the movement. In a recent study, participants were asked to judge whether they undershot or overshot the target after performing a reaching movement. Participants were given vision throughout the movement or during a 20 ms window of vision, which was presented when the limb reached various velocities, ranging between 0.03 and 1.4 m/s. Participants were significantly better in judging endpoint bias when vision was available throughout the movement compared to all 20 ms window conditions (and there were differences in endpoint judgements across the window conditions). To test if the ability to judge endpoint bias during a movement is primarily based on visual information, we employed a similar protocol with the addition of an observer. Participants were seated across from each other while the actor reached towards a target as quickly and as accurately as possible. Both the actor and the observer were given vision throughout the movement or during a 20 ms window of vision that began when the actor’s limb reached 0.03, 0.6, 1.0, or 1.4 m/s. After each trial, they were asked to privately indicate whether the final limb position had undershot or overshot the target. In both the acting and observing task, participants exhibited better endpoint bias judgements when given vision of the limb throughout the movement than in all window conditions. More importantly, the actor exhibited a significantly greater ability to judge endpoint bias compared to the observer, across all window conditions. Thus, performing the actual movement appears to be more important than the visual information when making online endpoint bias judgements, highlighting the important role for an efference copy and/or proprioception in forming judgements about the accuracy of an action.Acknowledgments: NSERC (Natural Sciences and Engineering Research Council of Canada), CFI (Canada Foundation for Innovation) and ORF (Ontario Research Fund)
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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 | 0.019 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".