Near-infrared spectroscopy of image clarity perception in the human brain
Bibliographic record
Abstract
The perception of blur in humans is intrinsic to our visual system, and dioptric power can improve clarity in many cases. This was evaluated experimentally to establish the best correction with dioptric power shifts. We used Near Infrared Spectroscopy (NIRS) to measure Oxy-, Deoxy- and Total-hemoglobin concentration changes in the brain while viewing images and reading a Snellen chart. Participants were tested with their usual correction (no diopter power shift (0 D)), with a 0.25 diopter power shift (0.25 D), and with a 0.5 diopter power shift (0.5 D). The concept of Approximate Entropy (AE) was applied to quantify the regularity of these hemoglobin time series of finite length. AE computations are based on the likelihood that similar templates in a time series remain similar on the next incremental comparison, so that time series with large AE have high irregular fluctuation. We found that the dioptric power shift eliciting the highest AE indicates the clearest visual condition for subjects. This technique may impact the current way in which ophthalmic lenses are prescribed.
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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 | 0.000 | 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) | 0.001 | 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".