Adaptation of Saccadic Sequences with and without Remapping
Bibliographic record
Abstract
It is relatively easy to adapt visually-guided saccades because the visual vector and the saccade vector match. The retinal error at the saccade landing position is compared to the prediction error, based on target location and efference copy. If these errors do not match, planning processes at the level(s) of the visual and/or motor vector processing are assumed to be inaccurate and the saccadic response is adjusted. In the case of a sequence of two saccades, the final error can be attributed to the last saccade vector or to the entire saccadic displacement. Here, we asked whether and how adaptation can occur in the case of remapped saccades, such as during the classic double-step saccade paradigm, where the visual and motor vectors of the second saccade do not coincide and so the attribution of error is ambiguous. Participants performed saccades sequences to two targets briefly presented prior to first saccade onset. The second saccade target was either briefly re-illuminated (sequential visually-guided task) or not (remapping task) upon first saccade offset. To drive adaptation, the second target was presented at a displaced location (backward or forward jump condition or control-no jump) at the end of the second saccade. Pre- and post-adaptation trials were identical, without the re-appearance of the target after the second saccade. For the 1st saccade endpoints, there was no change as a function of adaptation. For the 2nd saccade, there was a similar increase in gain in the forward jump condition (52% and 61% of target jump) in the two tasks, whereas the gain decrease in the backward condition was much smaller for the remapping task than for the sequential visually-guided task (41% vs. 94%). In other words, the absolute gain change was similar between backward and forward adaptation for remapped saccades. In conclusion, we show that remapped saccades can be adapted, suggesting that the error is attributed to the visuo-motor transformation of the remapped visual vector. The mechanisms by which adaptation takes place for remapped saccades may be similar to those of forward sequential visually-guided saccades, unlike those involved in adaptation for backward sequential visually-guided saccades.
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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".