Motor output effect of objects presented in the blindspot
Bibliographic record
Abstract
Despite the absence of retinal input within the physiological blindspot, perceptual filling of the blindspot has been consistently shown; suggesting visual perception can exist without retinal drive. Moreover, motor output does not require conscious awareness of visual input (Binsted et al. 2007). In the present investigation, two experiments were conducted to examine if the motor system has access to unconscious input from the blindspot: one examining how objects presented in the blindspot could modulate motor output (i.e. pointing) and a second examining the cortically evoked potentials associated with such subconscious inputs. In both experiments, the blindspot of the right eye was mapped using a modified protocol developed by Araragi & Nakamizo (2008). In E1 subjects pointed to objects presented either in the blindspot or outside of it (no target trials served as a control); if they saw no target they were instructed to guess. In E2 we performed a visual detection task under similar conditions while recording EEG (Brainvision DC, 64ch). Both endpoint position and variability was sensitive to the occurrence and position of a target. EEG analyses revealed deflections at visual and parietal sites (O1, PO3 and P3) independent of targets perception, but varying as a function of distance from blindspot centroid. Thus, despite the absence of conscious percept due to subthreshold retinal input, visuomotor pathways can use target location information to plan and execute actions.Acknowledgments: NSERC
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".