Another look at binocular vision: Contribution to online control processes.
Bibliographic record
Abstract
Ample research has investigated the advantage of binocular over monocular vision. In this study, we aimed to better understand the use of monocular vs. binocular visual feedback for the control of on-going upper-limb reaching movements. If binocular cues (e.g., binocular disparity) contribute to such online control processes, then participants should exhibit wider endpoint distributions when performing with one vs. two eyes. Twelve right-eye and right-hand dominant individuals performed reaching movements (30 cm) with counterbalanced presentation of monocular dominant, monocular non-dominant and binocular vision conditions. We analysed movement endpoint accuracy and precision. As anticipated, participants exhibited wider endpoint distributions in the primary movement axis, with both monocular conditions compared to the binocular condition. In addition, we performed contrasts between limb position at 25%, 50% and 75% of movement time and limb position at movement end. Such correlational analyses presumably reflect the extent to which movements are corrected between movement onset and offset (e.g., Heath, 2005). Further, analysis of the Fisher-z transformed R values showed that participants exhibited more stereotyped (i.e., less controlled) trajectories in the monocular dominant condition compared to the binocular vision condition. The contrast between monocular non-dominant and binocular vision failed to reach significance. These results provide evidence that individuals employ binocular cues (e.g., binocular disparity) to implement online trajectory amendments while vision with the dominant eye vs. the non-dominant eye contribute differently to the control of on-going movements. Meeting abstract presented at VSS 2014
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".