Lu Zhong-Lin, Zhao Yukai, Lesmes Luis Andres, Dorr Michael
Division of Arts and Sciences, NYU Shanghai, Shanghai, China; Center for Neural Science and Department of Psychology, New York University, New York, USA; NYU-ECNU Institute of Brain and Cognitive Neuroscience, Shanghai, China.
Center for Neural Science, New York University, New York, USA.
Res Sq. 2023 Jun 9:rs.3.rs-3031340. doi: 10.21203/rs.3.rs-3031340/v1.
We introduce expected information gain to quantify measurements and apply it to compare visual acuity (VA) and contrast sensitivity (CS) tests. We simulated observers with parameters covered by the visual acuity and contrast sensitivity tests and observers based on distributions of normal observers tested in three luminance and four Bangerter foil conditions. We first generated the probability distributions of test scores for each individual in each population in the Snellen, ETDRS and qVA visual acuity tests and the Pelli-Robson, CSV-1000 and qCSF contrast sensitivity tests and constructed the probability distributions of all possible test scores of the entire population. We then computed expected information gain by subtracting expected residual entropy from the total entropy of the population. For acuity tests, ETDRS generated more expected information gain than Snellen; scored with VA threshold only or with both VA threshold and VA range, qVA with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS. For contrast sensitivity tests, CSV-1000 generated more expected information gain than Pelli-Robson; scored with AULCSF or with CS at six spatial frequencies, qCSF with 25 trials generated more expected information gain than CSV-1000. The active learning based qVA and qCSF tests can generate more expected information than the traditional paper chart tests. Although we only applied it to compare visual acuity and contrast sensitivity tests, information gain is a general concept that can be used to compare measurements and data analytics in any domain.
我们引入期望信息增益来量化测量，并将其应用于比较视力（VA）和对比敏感度（CS）测试。我们模拟了具有视力和对比敏感度测试所涵盖参数的观察者，以及基于在三种亮度和四种Bangerter滤光片条件下测试的正常观察者分布的观察者。我们首先在Snellen、ETDRS和qVA视力测试以及Pelli-Robson、CSV-1000和qCSF对比敏感度测试中，为每个群体中的每个个体生成测试分数的概率分布，并构建整个群体所有可能测试分数的概率分布。然后，我们通过从群体的总熵中减去期望剩余熵来计算期望信息增益。对于视力测试，ETDRS产生的期望信息增益比Snellen更多；仅用视力阈值或同时用视力阈值和视力范围进行评分时，具有15行（或45个视标）的qVA产生的期望信息增益比ETDRS更多。对于对比敏感度测试，CSV-1000产生的期望信息增益比Pelli-Robson更多；用AULCSF或在六个空间频率下的对比敏感度进行评分时，进行25次试验的qCSF产生的期望信息增益比CSV-1000更多。基于主动学习的qVA和qCSF测试比传统的纸质图表测试能产生更多的期望信息。尽管我们仅将其应用于比较视力和对比敏感度测试，但信息增益是一个通用概念，可用于比较任何领域的测量和数据分析。
