Carkeet Andrew, Bailey Ian L
School of Optometry and Vision Science and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
School of Optometry, University of California, Berkeley, USA.
Ophthalmic Physiol Opt. 2017 Mar;37(2):118-127. doi: 10.1111/opo.12357.
To assess whether the slopes of psychometric functions for measuring low contrast letter acuity were different from those for measuring high contrast letter acuity.
Ten participants, wearing their best spectacle correction, were assessed monocularly. Stimuli were logarithmic progression charts, generated on a computer monitor, with nine rows of five randomised Sloan letters generated in either high contrast format (Weber contrast 99.2%) or low contrast format (Weber contrast 18.7%). For each participant, psychometric functions were generated by probit analysis of the data on each of 16 attempts at a low contrast chart and 16 attempts at a high contrast chart. Each of these probit fits yielded an estimate of Probit Size which provided information about how steep or flat the psychometric function was, along with an estimate of Probit Acuity Threshold.
Probit Size was significantly larger (p < 0.001) for low contrast charts than for high contrast charts, indicating that psychometric functions were flatter for low contrast charts. Mean Probit Sizes in logMAR were 0.099 (SEM 0.022) for low contrast charts and 0.071 (SEM 0.009) for high contrast charts if a guess rate of 1/26 was assumed, or were 0.086 (SEM 0.019) for low contrast charts and 0.064 for high contrast charts if a guess rate of 1/10 was assumed. Monte Carlo analysis showed that these means were likely to be biased estimates, with true Probit Size probably being larger (i.e. slightly flatter fits) by 0.016-0.019 logMAR. As expected, Probit Acuity Thresholds were poorer for low contrast charts than for high contrast charts (p < 0.001).
Our Monte Carlo modelling showed that such differences in acuity psychometric functions would be expected to give greater intra-subject variability in low contrast letter-by-letter acuity thresholds than for high contrast letter-by-letter acuity thresholds, and that this difference would depend on the termination rule used when measuring acuity. Likewise the variation in letter-by- letter acuity thresholds with termination rule will be different for high and low contrast charts. For low contrast and high contrast Sloan letter charts in a standard logarithmic format, a termination rule of four mistakes on a row, will give close to optimum sensitivity-to-change.
评估测量低对比度字母视力的心理测量函数斜率是否与测量高对比度字母视力的心理测量函数斜率不同。
对10名佩戴最佳眼镜矫正的参与者进行单眼评估。刺激物是在计算机显示器上生成的对数递增图表,有九行,每行包含五个随机排列的斯隆字母,以高对比度格式(韦伯对比度99.2%)或低对比度格式(韦伯对比度18.7%)呈现。对于每位参与者,通过对低对比度图表的16次尝试和高对比度图表的16次尝试的数据进行概率分析来生成心理测量函数。这些概率拟合中的每一个都产生了一个概率大小估计值,该估计值提供了有关心理测量函数有多陡峭或平坦的信息,以及一个概率视力阈值估计值。
低对比度图表的概率大小显著大于高对比度图表(p < 0.001),表明低对比度图表的心理测量函数更平坦。如果假设猜测率为1/26,低对比度图表以对数最小分辨角(logMAR)表示的平均概率大小为0.099(标准误0.022),高对比度图表为0.071(标准误0.009);如果假设猜测率为1/10,低对比度图表为0.086(标准误0.019),高对比度图表为0.064。蒙特卡罗分析表明,这些平均值可能是有偏差的估计值,真实的概率大小可能大0.016 - 0.019 logMAR(即拟合稍平坦)。正如预期的那样,低对比度图表的概率视力阈值比高对比度图表差(p < 0.001)。
我们的蒙特卡罗模型表明,与高对比度逐字母视力阈值相比,视力心理测量函数的这种差异预计会使低对比度逐字母视力阈值的受试者内变异性更大,并且这种差异将取决于测量视力时使用的终止规则。同样,高对比度和低对比度图表中逐字母视力阈值随终止规则的变化也会不同。对于标准对数格式的低对比度和高对比度斯隆字母图表,连续四个错误的终止规则将产生接近最佳的变化敏感性。