Age-related decline in motion contrast sensitivity due to lower absorption rate of cones and calculation efficiency
Bibliographic record
Abstract
Motion perception is affected by healthy aging, which impairs the ability of older adults to perform some daily activities such as driving. The current study investigated the underlying causes of age-related motion contrast sensitivity losses by using an equivalent noise paradigm to decompose motion contrast sensitivity into calculation efficiency, the temporal modulation transfer function (i.e., temporal blur) and 3 sources of internal noise: stochastic absorption of photons by photoreceptors (i.e., photon noise), neural noise occurring at the retinal level (i.e., early noise) and at the cortical level (i.e., late noise). These sources of internal noise can be disentangled because there impacts on motion contrast sensitivity vary differently as a function of luminance intensity. The impact of healthy aging on these factors was evaluated by measuring motion contrast sensitivity of young and older healthy adults at different luminance intensities, temporal frequencies and with/without external noise. The older adults were found to have higher photon noise, which suggests a lower photon absorption rate of cones. When roughly equating the amount of photons being absorbed by the photoreceptors, older adults had lower calculation efficiencies, but no significant aging effect was found on temporal modulation transfer function, early noise and late noise.
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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 | 0.000 |
| 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".