Response Modes Influence the Accuracy of Monocular and Binocular Reaching Movements
Bibliographic record
Abstract
The authors manipulated the availability of monocular and binocular vision during the constituent planning and control stages of a goal-directed reaching task. Furthermore, trials were completed with or without online limb vision to determine whether monocular- or binocular-derived ego-motion cues influence the integration of visual feedback for online limb corrections. Results showed that the manipulation of visual cues during movement planning did not influence planning times or overall kinematics. During movement execution, however, binocular reaches--and particularly those completed with online limb vision--demonstrated heightened endpoint accuracy and stability, a finding directly linked to the adoption of a feedback-based mode of reaching control (i.e., online control). In contrast, reaches performed with online monocular vision produced increased endpoint error and instability and demonstrated reduced evidence of feedback-based corrections (i.e., offline control). Based on these results, the authors propose that the combination of static (i.e., target location) and dynamic (i.e., the moving limb) binocular cues serve to specifically optimize online reaching control. Moreover, results provide new evidence that differences in the kinematic and endpoint parameters of binocular and monocular reaches reflect differences in the extent to which the aforementioned engage in online and offline modes of movement control.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".