The contrast sensitivity function (CSF) predicts functional vision better than acuity, but long testing times prevent its psychophysical assessment in clinical and practical applications. This study presents the quick CSF (qCSF) method, a Bayesian adaptive procedure that applies a strategy developed to estimate multiple parameters of the psychometric function (A. B. Cobo-Lewis, 1996; L. L. Kontsevich & C. W. Tyler, 1999). Before each trial, a one-step-ahead search finds the grating stimulus (defined by frequency and contrast) that maximizes the expected information gain (J. V. Kujala & T. J. Lukka, 2006; L. A. Lesmes et al., 2006), about four CSF parameters. By directly estimating CSF parameters, data collected at one spatial frequency improves sensitivity estimates across all frequencies. A psychophysical study validated that CSFs obtained with 100 qCSF trials (~10 min) exhibited good precision across spatial frequencies (SD < 2–3 dB) and excellent agreement with CSFs obtained independently (mean RMSE = 0.86 dB). To estimate the broad sensitivity metric provided by the area under the log CSF (AULCSF), only 25 trials were needed to achieve a coefficient of variation of 15–20%. The current study demonstrates the method’s value for basic and clinical investigations. Further studies, applying the qCSF to measure wider ranges of normal and abnormal vision, will determine how its efficiency translates to clinical assessment.

The qCSF method is sufficiently rapid, accurate, and precise in measuring CSFs in normal and amblyopic persons. It has great potential for clinical practice.

External noise paradigms, measuring contrast threshold as a function of external noise contrast (the "TvC" function), provide a valuable tool for studying perceptual mechanisms. However, measuring TvC functions at the multiple performance criteria needed to constrain observer models has previously involved demanding data collection (often>2000 trials). To ease this task, we developed a novel Bayesian adaptive procedure, the "quick TvC" or "qTvC" method, to rapidly estimate multiple TvC functions, by adapting a strategy originally developed to estimate psychometric threshold and slope [Cobo-Lewis, A. B. (1996). An adaptive method for estimating multiple parameters of a psychometric function. Journal of Mathematical Psychology, 40, 353-354; Kontsevich, L. L., and Tyler, C. W. (1999). Bayesian adaptive estimation of psychometric slope and threshold. Vision Research, 39(16), 2729-2737]. Exploiting the regularities observed in empirical TvC functions, the qTvC method estimates three parameters: the optimal threshold c(0), the critical noise level N(c), and the common slope, eta, of log-parallel psychometric functions across external noise conditions. Before each trial, the qTvC uses a one-step-ahead search to select the stimulus (jointly defined by signal and noise contrast) that minimizes the expected entropy of the three-dimensional posterior probability distribution, p(N(c),c(0),eta). The method's accuracy and precision, for estimating TvC functions at three performance criteria (65%, 79%, and 92% correct), were evaluated using Monte-Carlo simulations and a psychophysical task. Simulations showed that less than 300 trials were needed to estimate TvC functions at three widely separated criteria with good accuracy (bias<5%) and precision (mean root mean square error <1.5 dB). Using an orientation identification task, we found excellent agreement (weighted r(2)>.95) between TvC estimates obtained with the qTvC and the method of constant stimuli, although the qTvC used only 12% of the data collection (240 vs 1920 trials). The qTvC may hold considerable practical value for applying the external noise method to study mechanisms of observer state changes and special populations. We suggest that the same adaptive strategy can be applied to directly estimate other classical functions, such as the contrast sensitivity function, elliptical equi-discrimination contours, and sensory memory decay functions.

The contrast sensitivity function (CSF) provides a fundamental characterization of spatial vision, important for basic and clinical applications, but its long testing times have prevented easy, widespread assessment. The original quick CSF method was developed using a two-alternative forced choice (2AFC) grating orientation identification task (Lesmes, Lu, Baek, & Albright, 2010), and obtained precise CSF assessments while reducing the testing burden to only 50 trials. In this study, we attempt to further improve the efficiency of the quick CSF method by exploiting the properties of psychometric functions in multiple-alternative forced choice (m-AFC) tasks. A simulation study evaluated the effect of the number of alternatives m on the efficiency of the sensitivity measurement by the quick CSF method, and a psychophysical study validated the quick CS method in a 10AFC task. We found that increasing the number of alternatives of the forced-choice task greatly improved the efficiency of CSF assessment in both simulation and psychophysical studies. The quick CSF method based on a 10-letter identification task can assess the CSF with an averaged standard deviation of 0.10 decimal log unit in less than 2 minutes.

The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye disease or its treatment. However, detecting CSF changes with precision and efficiency at both the individual and group levels is very challenging. By exploiting the Bayesian foundation of the quick CSF method (Lesmes, Lu, Baek, & Albright, 2010), we developed and evaluated metrics for detecting CSF changes at both the individual and group levels. A 10-letter identification task was used to assess the systematic changes in the CSF measured in three luminance conditions in 112 naïve normal observers. The data from the large sample allowed us to estimate the test–retest reliability of the quick CSF procedure and evaluate its performance in detecting CSF changes at both the individual and group levels. The test–retest reliability reached 0.974 with 50 trials. In 50 trials, the quick CSF method can detect a medium 0.30 log unit area under log CSF change with 94.0% accuracy at the individual observer level. At the group level, a power analysis based on the empirical distribution of CSF changes from the large sample showed that a very small area under log CSF change (0.025 log unit) could be detected by the quick CSF method with 112 observers and 50 trials. These results make it plausible to apply the method to monitor the progression of visual diseases or treatment effects on individual patients and greatly reduce the time, sample size, and costs in clinical trials at the group level.