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Abstract
We make use of expected information gain to quantify the amount of knowledge obtained from measurements in a population. In the first application, we compared the expected information gain in the Snellen, ETDRS, and qVA visual acuity (VA) tests, as well as in the Pelli–Robson, CSV-1000, and qCSF contrast sensitivity (CS) tests. For the VA tests, ETDRS generated more expected information gain than Snellen. Additionally, the qVA test with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS, whether scored with VA threshold alone or with both VA threshold and VA range. Regarding the CS tests, CSV-1000 generated more expected information gain than Pelli–Robson, and the qCSF test with 25 trials generated more expected information gain than CSV-1000, whether scored with AULCSF or with CSF at six spatial frequencies. The active learning-based qVA and qCSF tests have the potential to generate more expected information gain than traditional paper chart tests. Although we have specifically applied it to compare VA and CS tests, expected information gain is a general concept that can be used to compare measurements in any domain.
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Details
1 NYU Shanghai, Division of Arts and Sciences, Shanghai, China (GRID:grid.449457.f) (ISNI:0000 0004 5376 0118); New York University, Center for Neural Science and Department of Psychology, New York, USA (GRID:grid.137628.9) (ISNI:0000 0004 1936 8753); NYU-ECNU Institute of Brain and Cognitive Neuroscience at NYU Shanghai, Shanghai, China (GRID:grid.449457.f) (ISNI:0000 0004 5376 0118)
2 New York University, Center for Neural Science, New York, USA (GRID:grid.137628.9) (ISNI:0000 0004 1936 8753)
3 Adaptive Sensory Technology Inc., San Diego, USA (GRID:grid.137628.9)