Reach adaptation to online target error
Bibliographic record
Abstract
Magescas et al. (2009) recently suggested that online error, unlike terminal error, does not lead to reach adaptation. The present study re-examines adaptation to online target error, but uses a small target perturbation and eliminates online vision of the limb, factors that may affect adaptation. We compared 3 groups: terminal error, online error, and control. All groups completed a pretest, exposure, and posttest phase. Participants made look-and-point movements to a target and we examined how repeated rightward target perturbations during the exposure phases of the experimental groups influenced reaches to a stationary target in the posttest. Exposure phases of each group contained an equal number of interleaved look-and-point and look-only trials, the latter of which were designed to inhibit build-up of saccadic adaptation in the online error group. On look-and-point trials the target either disappeared at saccade onset and then re-appeared 3.75cm to the right when the hand landed (terminal error group), immediately jumped right by 3.75cm at saccade onset and remained lit throughout the saccade and reach (online error group), or remained lit but stationary throughout the saccade and reach (control group). In all groups, vision of the limb was only provided at the start and end of the reach. Our results show that both the terminal error and the online error groups developed significant aftereffects. It appears, therefore, that online error can produce reach adaptation.
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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 | 0.003 |
| 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 | 0.001 |
| 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".