Does saccadic adaptation transfer to non-adapted pointing movements?
Bibliographic record
Abstract
Previous research has suggested that saccadic adaptation may transfer to the manual system. This has implications for adaptation studies using a target jump paradigm where both the moving eyes and limbs are exposed to the visual perturbation. Although various studies have shown this potential transfer, methodological differences such as the use of short target distances, or only backward target jumps may limit generalization. For example, saccadic adaptation to backward and forward jumps may occur through different mechanisms, limiting conclusions about adaptation during forward jumps. The purpose of this study was to examine if amplitude adaptation occurs in saccades to forward target jumps, and if so, whether the adaptation influences pointing movements. Participants (n=20) looked and pointed to targets presented ~19° in the periphery. Saccades were monitored with use of EOG and pointing movements were measured via motion capture. A pre-post design was used to compare saccade and pointing amplitudes before and after an exposure block consisting of look-only trials. The control group looked to stationary targets during the exposure block, and the experimental group looked to targets that jumped ~3° during the saccade. Our results showed little difference in mean saccade amplitudes between pre- and post-exposure blocks during forward target jumps. Not surprisingly, there were also no pre-post differences in pointing amplitudes. This leads us to question whether concurrent look and point movements in response to forward target jumps in our task context are necessary to elicit adaptation in either saccade or manual system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".