I laterally spy with my dominant eye
Bibliographic record
Abstract
We recently reported that visual information gathered by the dominant eye is critical for online control (e.g., Manzone et al., submitted). Specifically, briefly presented target jumps were significantly corrected only when viewed by the dominant eye. Such an advantage may be due to the typical presence of the right hand in the right hemispace. To test this idea, participants performed reaching movements with the right hand while vision of the environment was restricted, using liquid-crystal goggles. Prior to each trial, participants fixated on a central cross. Their hand started either 12 cm to the right or left of that fixation point, with a binocular preview of the environment and target (30 cm away). The goggles were then occluded until the limb reached 1.0 m/s (Tremblay et al., 2013), which triggered a brief window of vision (i.e., 20 ms) providing visual information to the non-dominant eye, the dominant eye, or both eyes (i.e., binocular). During that window, the original (30 cm) or a jumped (27 cm) target was presented, with a 2:1 odds ratio. Analyses replicated our previous finding that participants exhibited significantly closer endpoints along the primary axis for the jumped target when vision was available to the dominant eye (i.e., dominant and binocular conditions). Critically, such corrections for the target jump via the dominant eye only occurred when movements began from the right starting position. As such, the results support the hemispatial functional advantage of the dominant eye and hand, for the online control of voluntary action.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.022 |
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".