Blindsight and enumeration: A case study
Bibliographic record
Abstract
“Blindsight” is a term first coined by Weiskrantz et al. in 1974 to describe residual visual performance in the cortically blind. It has been postulated that blindsight could be due to the retinotectal pathway projecting information past V1 to later cortical structures. Our interest was in whether the pathways responsible for blindsight could also support enumeration. We tested a 53-year-old male, C.H., who had suffered a medial right occipital lobe stroke seven months previous. The stroke resulted in an upper left homonymous quadrantanopia. In order to determine whether there were any residual abilities in the blind field, we first tested basic detection and discrimination skills. C.H. was able to determine whether or not a 7° X 7° visual angle object was presented in his blind field with near perfect accuracy though his accuracy was only 90% for smaller (4.6° X 2.2°) figures. C.H. also discriminated between large X′s and O′s and horizontal and vertical bars with good accuracy when the figures were large (78% for X vs. O and 73% for horizontal vs. vertical bars). Throughout these tests C.H. staunchly maintained that he could not see the stimuli and that he was only guessing. Because it was clear that C.H. had some residual ability in his blind field, we tested his enumeration. The enumeration task involved 1-3 items. These items were 1.5° X 1.5° black diamonds, 0-2 in his blind field and 1-3 in his non-blind field. Across all conditions, C.H. was able to use the information in his blind field and identify the total number of items presented at levels of performance that were well above chance. Because C.H. appears to use blind field information to enumerate, we postulate that enumeration ability may be mediated by the same structures that support blindsight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".