Implicit processing of obstacles for immediate but not delayed reaching in a case of hemianopic blindsight
Bibliographic record
Abstract
When we reach towards an object we are easily able to avoid potential obstacles located within our reach path. Previous research suggests that obstacle avoidance can operate even in the absence of visual awareness. Specifically, patients with right parietal damage who demonstrate a profound lack of awareness for the left side of space are nevertheless able to avoid obstacles they are unaware of. This suggests that obstacle avoidance is governed by the dorsal stream which regulates visuomotor control independently from the ventral stream which enables conscious visual perception. One important question that remains unanswered concerns the visual inputs necessary for obstacle avoidance to occur. Specifically, the dorsal stream receives input from primary visual cortex (i.e. V1) as well as subcortical visual pathways that bypass V1 (e.g. the retinotectopulvinar and retinopulvinar pathways). In the current study we examined obstacle avoidance in CB, a patient who suffered a right occipital stroke resulting in a dense left visual field hemianopia. In the first experiment CB was required to reach to a target region while avoiding obstacles that were located in his right (sighted) or left (blind) visual field, or both fields. The results indicated that the endpoints of CB's reaches were significantly modulated by the position of obstacles placed in his blind field. Specifically, obstacles in the blind field that were placed closer to fixation ‘pushed’ his reach endpoints further rightward compared to obstacles in his blind field that were placed further away from fixation. In a second experiment, CB's sensitivity to the same obstacles in his blind field was completely abolished when a short 2-second delay was introduced prior to reach onset (compared to healthy sighted individuals who continued to avoid the obstacles). These data provide compelling evidence that the dorsal stream controls obstacle avoidance in real-time, independent of inputs from V1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".