Modeling acuity for optotypes varying in complexity.

Watson and Ahumada (2008) described a template model of visual acuity based on an ideal-observer limited by optical filtering, neural filtering, and noise. They computed predictions for selected optotypes and optical aberrations. Here we compare this model's predictions to acuity data for six human observers, each viewing seven different optotype sets, consisting of one set of Sloan letters and six sets of Chinese characters, differing in complexity (Zhang, Zhang, Xue, Liu, & Yu, 2007). Since optical aberrations for the six observers were unknown, we constructed 200 model observers using aberrations collected from 200 normal human eyes (Thibos, Hong, Bradley, & Cheng, 2002). For each condition (observer, optotype set, model observer) we estimated the model noise required to match the data. Expressed as efficiency, performance for Chinese characters was 1.4 to 2.7 times lower than for Sloan letters. Efficiency was weakly and inversely related to perimetric complexity of optotype set. We also compared confusion matrices for human and model observers. Correlations for off-diagonal elements ranged from 0.5 to 0.8 for different sets, and the average correlation for the template model was superior to a geometrical moment model with a comparable number of parameters (Liu, Klein, Xue, Zhang, & Yu, 2009). The template model performed well overall. Estimated psychometric function slopes matched the data, and noise estimates agreed roughly with those obtained independently from contrast sensitivity to Gabor targets. For optotypes of low complexity, the model accurately predicted relative performance. This suggests the model may be used to compare acuities measured with different sets of simple optotypes.