Psychophysically measuring the efficiency of rods
Bibliographic record
Abstract
Recent studies suggest that the efficiency of cones to detect photons can be evaluated by measuring the equivalent input noise (EIN; derived from contrast thresholds measured in the presence and absence of visual noise) under specific conditions in which the contrast threshold is limited by the variability in the number of photons detected by photoreceptors (i.e., photon noise). These conditions can be identified based on the known properties of photon noise: spatially and temporally white and inversely proportional to the luminance intensity. The present study aims to adapt this psychophysical paradigm to evaluate the efficiency of rods to detect photons. A motion direction discrimination task was used to evaluate the EIN over a wide range of luminance intensities for various spatial and temporal frequencies when the display was blue or red (to which rods have little sensitivity). The target was either a Gabor patch presented at 20 degrees of eccentricity (first experiment) or a rotating sine-wave annulus with a radius of 10 degrees of eccentricity (second experiment). In both experiments, the EIN was found to be inversely proportional to luminance intensity over a limited range of luminance intensities for both display colors. At these luminance intensities, the EIN was roughly independent of the spatial and temporal frequencies, matching the properties of photon noise. Furthermore, under these conditions, contrast thresholds were lower (i.e., better) when the display was blue rather than red, which suggests that vision was mediated by rods when the display was blue. We conclude that the efficiency of rods to detect photons can be evaluated by measuring contrast thresholds in the presence and absence of visual noise over a limited range of luminance intensities with a blue display.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".