Reprogramming of Interceptive Actions: Time Course of Temporal Corrections for Unexpected Target Velocity Change
Bibliographic record
Abstract
The authors investigated the time course of reprogramming of the temporal dimension of motor acts in a task requiring interception of a moving target. The target moved at a constant velocity on a monitor screen; in part of the trials, target velocity was unexpectedly increased or decreased. Those modifications were produced at different moments during target displacement, leaving periods of time from 100 to 800 ms for movement timing correction. The authors assessed the effects of probability of target velocity change (25% vs. 50%), uncertainty about direction of velocity change (unidirectional vs. bidirectional), and direction of velocity change (increase vs. decrease). Analysis of 24 participants' arm acceleration showed that fast adjustments took place between 100 and 200 ms after target velocity change similarly for all uncertainty conditions. Analysis of temporal error indicated that the combination of high probability of target velocity change and certainty on direction of target velocity change led to the most successful movement timing reprogramming. For the other experimental conditions, temporal accuracy was still poor when a period of 800 ms was available for correction. Movement reprogramming was a continuous process that was more efficient for target velocity increase than for target velocity decrease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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".