The influence of distant motion signals on fast reaching movements to a stationary object
Bibliographic record
Abstract
Purpose: Visual motion is a special kind of information: since it loses its behavioral relevance quickly, it is crucial for time-constrained behavior. For this reason, one might expect that visual motion should be rapidly available to motor systems as well as to perception. We examined whether visual motion information in one region of the visual field influences fast reaching movements to, as well as perception of, an object in another part of the field and compared the time courses for these two measurements. Methods: A square wave grating translated vertically on a monitor and then reversed direction. A brief stationary flash was presented next to the grating (separated by ∼10 deg) at various times before or after the grating reversed direction. The endpoint accuracy of reaching movements to the flash was measured and compared to perceptual localization of the flash using the same stimulus. Results: Perceptual task: The flash appeared shifted in the direction of the nearby grating's motion. Motor task: Reaching endpoints were also shifted in the direction of the nearby motion. The time course of the reaching mislocalizations was consistent with that of the perceptual illusion. Conclusions: The motion of one object influenced reaching movements to a spatially-separated stationary object, showing that, unlike many other kinds of visual information, motion signals over large regions of the visual field are taken into account before manual localization takes place. The mechanism(s) subserving perceptual and motor localization share a common motion input. Supported by NIH.
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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 | 0.000 | 0.002 |
| 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".