Unconscious vision: new insights into the neuronal correlate of blindsight using diffusion tractography
Bibliographic record
Abstract
The existence of several types of unconscious vision, or 'blindsight', has convincingly been demonstrated in numerous studies, and their neuronal correlates have been hypothesized according to the nature of the residual vision observed. We used diffusion tensor imaging (DTI) tractography to demonstrate an association between the presence of 'Type I'- blindsight or 'attention blindsight' and reconstructed superior colliculi (SC) fibre tracts in hemispherectomized subjects, in support of the hypothesis that this subcortical structure plays a pivotal role in this type of blindsight. Before the DTI study, 'Type I' blindsight was identified in two of four hemispherectomized subjects by using a spatial summation effect paradigm, an indirect behavioural method, in which subjects were unaware of a stimulus presented in their blind visual field and were required to respond to an identical stimulus presented simultaneously in their intact field. SC tracts were then reconstructed in six control subjects, the two hemispherectomized subjects with blindsight and the two hemispherectomized subjects without blindsight. Whereas control subjects demonstrated mainly ipsilateral connections to visual association areas, parietal cortex, prefrontal areas and to an area close to the frontal eye fields, hemispherectomized subjects with blindsight showed ipsi- and contralateral connections from the SC to visual association areas, primary visual areas, parietal areas, prefrontal areas and to the posterior part of the internal capsule. In contrast, no projections from the SC on the hemispherectomized side were observed in hemispherectomized subjects without blindsight, in support of a key role of this structure in 'Type-I' or 'attention blindsight'.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".