Seeing all the obstacles in your way: The effect of visual feedback on obstacle avoidance
Bibliographic record
Abstract
Human reaching behaviour displays sophisticated obstacle avoidance. Previous patient work has identified dorsal stream visuomotor processing as being integral to this ability (Schindler et al., 2004, Nature Neuroscience; Rice et al., 2006, Experimental Brain Research). Recently, we demonstrated that the obstacle avoidance system in normal participants is sensitive to both the position and size of obstacles (Chapman & Goodale, VSS 2007). A limitation in these previous studies was that reaches were performed without visual feedback, and were not made to a specific target (i.e. the target was a strip instead of a point). Many studies have shown that both the introduction of visual feedback and the order in which the feedback is received significantly alter performance in simple visuomotor tasks (e.g. Jakobson & Goodale, 1991, Experimental Brain Research). Thus, the present study was designed to compare obstacle avoidance when reaches were made to a discrete target with and without visual feedback (VF vs. NVF) under different orders of feedback availability. Twenty-four right-handed participants performed reaches in the presence of one, two, or no obstacles placed mid-reach. Three visual-feedback-order conditions were used: blocked (all VF trials occurred together, separate from NVF trials), alternating, and random. In addition to replicating the previous work, we showed that robust avoidance behaviour occurred when reaches were made to a specific target, and that visual feedback modulated this behaviour. Moreover, the order in which visual feedback was made available also had a significant impact; VF and NVF trials differed significantly across several kinematic measures but only in the blocked condition. Performance did not differ between VF and NVF trials in the alternating or random conditions, suggesting that motor strategies are automatically adjusted by recent experience (Song & Nakayama, 2007, Journal of Vision) and are not affected by explicit knowledge about feedback availability on an upcoming trial.
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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 | 0.005 |
| 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) | 0.002 | 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".