Adaptation of reactive and voluntary saccades: different patterns of adaptation revealed in the antisaccade task
Bibliographic record
Abstract
Sensorimotor adaptation restores and maintains the accuracy of goal-directed movements. It remains unclear whether these adaptive mechanisms modify actions by controlling peripheral premotor stages that send commands to the effectors and/or earlier processing stages involved in registration of target location. Here, we studied the effect of adaptation of saccadic eye movements, a well-established model of sensorimotor adaptation, in an antisaccade task. This task introduces a clear spatial dissociation between the actual target direction and the requested saccade direction because the correct movement direction is in the opposite direction from the target location. We used this requirement of a vector inversion to assess the level(s) of saccadic adaptation for two different types of adapted saccades. In two different experiments, we tested the transfer to antisaccades of the adaptation in one direction of reactive saccades to jumping targets and of scanning voluntary saccades within a target array. In the first experiment, we found that adaptation of reactive saccades transferred only to antisaccades in the adapted direction. In contrast, in the second experiment, adaptation of scanning voluntary saccades transferred to antisaccades in both the adapted and non-adapted directions. We conclude that adaptation of reactive saccades acts only downstream of the vector inversion required in the antisaccade task, whereas adaptation of voluntary saccades has a distributed influence, acting both upstream and downstream of vector inversion.
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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 | 0.001 |
| 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".