Self-operated stimuli improve subsequent visual motion processing
Bibliographic record
Abstract
Proper interpretation of visual information requires capturing the structural regularities in the visual signal and this frequently occurs in conjunction with movement. Perceptual interpretation is complicated both by transient perceptual changes that accompany motor activity, and as found in audition and somatosensation, by more persistent changes that accompany the learning of new movements. Here we asked whether motor learning also results in sustained changes to visual perception. We designed a reaching task in which participants directly controlled the visual information they received, which we term self-operated stimuli. Specifically, they trained to make movements in a number of directions. Directional information was provided by the motion of an intrinsically ambiguous moving stimulus which was directly tied to motion of the hand. We find that movement training improves perception of coherent stimulus motion, and that changes in movement are correlated with the perceptual change. No perceptual changes are observed in passive observers even when they are provided with an explicit strategy to solve perceptual grouping. Comparison of empirical perceptual data with simulations based on a Bayesian generative model of motion perception suggests that movement training promotes the fine-tuning of the internal representation of stimulus geometry. These results emphasize the role of sensorimotor interaction in determining the persistent properties in space and time that define a percept.
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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 | 0.001 |
| 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) | 0.005 | 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".