Applying Resampling and Visualization Methods in Factor Analysis to Model Human Spatial Vision
Bibliographic record
Abstract
Purpose: Studies have reported different numbers of spatial frequency channels for chromatic and achromatic vision. To resolve the difference, we performed factor analysis, a multivariate modeling technique, on population data of achromatic and chromatic sensitivity. In addition, we included resampling and visualization methods to evaluate models from factor analysis. These routines are complex but widely useful. Therefore we have archived our analysis routines by building smCSF, an open-source software package in R (https://smin95.github.io/dataviz/). Methods: Data of 103 normally-sighted adults were analyzed. They included blue-yellow, red-green, and achromatic contrast sensitivity. To obtain the confidence interval of relevant statistical parameters, factor analysis was performed using a resampling method. Then exploratory models were developed. We then performed model selections by fitting them against the empirical data and quantifying the quality of the fits. Results: During the exploratory factor analysis, different statistical tests supported different factor models. These could partially be reasons for why there have been conflicting reports. However, after the confirmatory analysis, we found that a model that included two spatial channels was adequate to approximate the chromatic sensitivity data, whereas that with three channels was so for the achromatic sensitivity data. Conclusions: Our findings provide novel insights about the spatial channels for chromatic and achromatic contrast sensitivity from population data. Also, the analysis and visualization routines have been archived in a computational package to boost the transparency and replicability of science.
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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 | 0.036 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".