Obstacle avoidance during online corrections
Bibliographic record
Abstract
The dorsal visual stream codes information crucial to the planning and online control of target-directed reaching movements in dynamic and cluttered environments. Two specific dorsally mediated abilities are the avoidance of obstacles and the online correction for changes in target location. The current study was designed to test whether or not both of these abilities can be performed concurrently. Participants made reaches to touch a target that, on two-thirds of the trials, remained stationary and on the other third "jumped" at movement onset. Importantly, on target-jump trials, a single object (in one of four positions) sometimes became an obstacle that interfered with the reach. When a target jump caused an object to suddenly become an obstacle, we observed clear spatial avoidance behavior, an effect that was not present when the target jumped but the object did not become an obstacle. This automatic spatial avoidance was accompanied by significant velocity reductions only when the risk for collision with the obstacle was high, suggesting an "intelligent" encoding of potential obstacle locations. We believe that this provides strong evidence that the entire workspace is encoded during reach planning and that the representation of all objects in the workspace is available to the automatic online correction system.
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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 | 0.004 |
| 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".