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Zhong-Lin Lu¹, Yukai Zhao², Luis Andres Lesmes³

¹Division 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.

Research square

|June 19, 2023

概括

预期的信息获取量化了测量结果,显示了像qVA和qCSF这样的先进测试比传统的视觉敏度和对比敏感度图表提供了更好的洞察力.

科学领域:

眼科和视觉科学眼科和视觉科学
信息理论信息理论
统计分析统计分析

背景情况:

精确测量视敏度 (VA) 和对比度 (CS) 敏感度对于诊断和管理视力障碍至关重要.
传统的测试可能无法最佳地量化从患者反应中获得的信息.
一种新的指标,即预期的信息获取,可用于评估和比较不同测量工具的效率.

研究的目的:

引入和应用预期信息获取作为量化和比较视觉功能测试性能的方法.
将已建立的VA和CS测试的信息获取与较新的自适应方法进行比较.

主要方法:

在各种亮度和班格特薄膜条件下模拟观察者.
对Snellen,ETDRS,qVA,Pelli-Robson,CSV-1000和qCSF测试中的个人和人群的测试分数的生成概率分布.
通过从总人口中减去预期的剩余来计算预期的信息获取.

主要成果:

对于VA测试,qVA显示了比ETDRS更高的预期信息获取,而ETDRS反过来表现优于Snellen.
对于CS测试,qCSF显示了比CSV-1000更大的预期信息获取,比Pelli-Robson更好.
基于主动学习的qVA和qCSF测试比传统的纸张图表测试提供了更多信息.

结论:

预期的信息获取为评估视觉科学中的测量效率提供了一个强大的框架.
现代适应性测试 (qVA,qCSF) 比传统的VA和CS测试更具信息性.
获取信息的概念广泛适用于比较任何测量或数据分析方法.

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