School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, China.
McGill Vision Research, Department of Ophthalmology and Visual Sciences, McGill University, Montreal, Canada.
Invest Ophthalmol Vis Sci. 2024 Jan 2;65(1):17. doi: 10.1167/iovs.65.1.17.
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/).
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.
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.
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 Also, the analysis and visualization routines have been archived in a computational package to boost the transparency and replicability of science.
先前的研究报告了色觉和无色觉的空间频率通道数量有所不同。为了解决这一分歧，我们对无色觉和色觉敏感性的人群数据进行了因子分析，这是一种多元建模技术。此外，我们还包括了重新采样和可视化方法来评估因子分析的模型。这些例程虽然复杂，但应用广泛。因此，我们通过构建 R 语言中的开源软件包 smCSF（https://smin95.github.io/dataviz/）来归档我们的分析例程。
对 103 名正常视力成年人的数据进行了分析。这些数据包括蓝-黄、红-绿和无色对比度敏感性。为了获得相关统计参数的置信区间，我们使用重采样方法对因子分析进行了分析。然后，我们开发了探索性模型。接下来，我们通过将它们拟合到经验数据并量化拟合的质量来进行模型选择。
在探索性因子分析中，不同的统计检验支持不同的因子模型。这可能部分解释了为什么会有相互矛盾的报告。然而，在确认性分析之后，我们发现一个包含两个空间通道的模型足以近似色觉敏感性数据，而一个包含三个通道的模型则足以近似无色觉敏感性数据。
我们的研究结果从人群数据中为色觉和无色觉对比敏感性的空间通道提供了新的见解。此外，分析和可视化例程已被归档在一个计算包中，以提高科学的透明度和可重复性。
