The effect of varying the second target location on movement integration; one-target advantage and target perturbation
Bibliographic record
Abstract
The one-target advantage results from control processes associated with the implementation of the second segment during execution of the first. Vision mediates this integration by continually monitoring segment 1; aiding in timing the implementation of segment 2 and reducing movement variability at target 1. Here, we compared the costs to movement integration associated with having to adjust pre-planned movements following an unexpected target perturbation. Participants performed two-target movements with full vision and when vision was occluded at target 1. The location of target 2 remained fixed or was perturbed at movement onset. On perturbed trials, target 2 shifted closer to or further from to the start position. Linear regressions of target 2 error versus movement time 2 on perturbed trials revealed greater y-intercepts for the vision occluded compared to the full vision condition. Hence, the cost in movement time associated with adjusting aiming trajectories following a target shift was greater when participants were denied vision. Furthermore, participants in the vision occluded condition had significantly longer RT's compared to the full vision condition. Thus, the integration of movements involving a perturbation in location of target 2 was easier when visual feedback was available throughout the entire movement. This supports the use of vision to mediate the transition between segments and also highlights continuous visual use during the second segment. However, when vision was occluded during segment 2, participants still planned and executed movements with an interdependent fashion but relied more heavily on the accurate movement planning of movement 2.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".