Looking forward to a correction: Obstacle avoidance during online correction
Bibliographic record
Abstract
The dorsal stream codes information crucial to the planning and online control of reaches to targets in dynamic and cluttered environments. Two specific dorsally-mediated abilities are online correction for changes in target location and the avoidance of obstacles. In online correction, abrupt changes in target location occurring during a reach result in automatic corrections toward the new target location (Pisella et al., Nature Neuroscience, 2000). In obstacle avoidance, objects that interfere with a reach are automatically avoided by maximizing the distance away from them (Schindler et al., Nature Neuroscience, 2004). The current study was designed to test whether both of these abilities can be performed concurrently. Participants made reaches to a target that, on two-thirds of the trials, remained stationary and on the other third ‘jumped’ (at movement onset) to a new location (10cm further in depth and 10cm to the left or right). When present, single objects (i.e. potential obstacles) were placed in one of four positions at a depth beyond the initial target, thus causing no initial interference. Importantly, on trials when the target jumped, the objects sometimes became obstacles that could interfere with the reach. Our results confirm previous findings; first, on the no-jump trials, obstacles positioned beyond a target have no interfering effect (Chapman & Goodale, Experimental Brain Research, 2008); second, on the no-obstacle jump trials, participants made automatic online corrections. Critically, when the target jumped in such a way that an object suddenly became an obstacle, we observed clear - and rapid - avoidance behaviour, an effect that was not present when the target jumped but the objects did not become obstacles. In other words, participants showed exquisite sensitivity to obstacles in their online corrections. These findings suggest that potential as well as current obstacles are automatically coded during movement planning, presumably by the dorsal stream.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".