Visual Function Measurements in Eyes With Diabetic Retinopathy: An Expert Opinion on Available Measures.

Clinical Relevance: Visual function impairment from diabetic retinopathy can have a considerable impact on patient's quality of life. Best-corrected visual acuity (BCVA) is most commonly used to assess visual function and guide clinical trials. However, BCVA is affected late in the disease process, is not affected in early disease, and does not capture some of the visual disturbances described by patients with diabetes. The goal of this report is to evaluate the relationship between diabetic retinal disease (DRD) and visual function parameters to determine which if any of them may be used in a future DRD staging system. Methods: The visual functions working group was 1 of 6 areas of DRD studied as part of the DRD staging system update, a project of the Mary Tyler Moore Vision Initiative. The working group identified 12 variables of possible interest, 7 of which were judged to have sufficient preliminary data to suggest an association with DR to warrant further review: microperimetry, static automated perimetry, electroretinogram (ERG) oscillatory potentials, flicker ERG, low luminance visual acuity (LLVA), contrast sensitivity (CS), and BCVA. The objective field analyzer (OFA) was added after subsequent in-person workshops. Results: Currently, the only visual function test available for immediate use is BCVA; the remaining tests are either promising (within 5 years) or have potential (>5 years) use. Besides BCVA, most visual function tests had a limited role in current clinical care; however, LLVA, CS, flicker ERG, and OFA demonstrated potential for screening and research purposes. Conclusions: Although current visual function tests are promising, future prospective studies involving patients with early and more advanced retinopathy are necessary to determine if these tests can be used clinically or as endpoints for clinical studies. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Quantification of expected information gain in visual acuity and contrast sensitivity tests.

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.

Collective endpoint of visual acuity and contrast sensitivity function from hierarchical Bayesian joint modeling.

Clinical trials typically analyze multiple endpoints for signals of efficacy. To improve signal detection for treatment effects using the high-dimensional data collected in trials, we developed a hierarchical Bayesian joint model (HBJM) to compute a five-dimensional collective endpoint (CE5D) of contrast sensitivity function (CSF) and visual acuity (VA). The HBJM analyzes row-by-row CSF and VA data across multiple conditions, and describes visual functions across a hierarchy of population, individuals, and tests. It generates joint posterior distributions of CE5D that combines CSF (peak gain, peak frequency, and bandwidth) and VA (threshold and range) parameters. The HBJM was applied to an existing dataset of 14 eyes, each tested with the quantitative VA and quantitative CSF procedures in four Bangerter foil conditions. The HBJM recovered strong correlations among CE5D components at all levels. With 15 qVA and 25 qCSF rows, it reduced the variance of the estimated components by 72% on average. Combining signals from VA and CSF and reducing noises, CE5D exhibited significantly higher sensitivity and accuracy in discriminating performance differences between foil conditions at both the group and test levels than the original tests. The HBJM extracts valuable information about covariance of CSF and VA parameters, improves precision of the estimated parameters, and increases the statistical power in detecting vision changes. By combining signals and reducing noise from multiple tests for detecting vision changes, the HBJM framework exhibits potential to increase statistical power for combining multi-modality data in ophthalmic trials.

Quantification of Expected Information Gain in Visual Acuity and Contrast Sensitivity Tests.

We introduce expected information gain to quantify measurements and apply it to compare visual acuity (VA) and contrast sensitivity (CS) tests. We simulated observers with parameters covered by the visual acuity and contrast sensitivity tests and observers based on distributions of normal observers tested in three luminance and four Bangerter foil conditions. We first generated the probability distributions of test scores for each individual in each population in the Snellen, ETDRS and qVA visual acuity tests and the Pelli-Robson, CSV-1000 and qCSF contrast sensitivity tests and constructed the probability distributions of all possible test scores of the entire population. We then computed expected information gain by subtracting expected residual entropy from the total entropy of the population. For acuity tests, ETDRS generated more expected information gain than Snellen; scored with VA threshold only or with both VA threshold and VA range, qVA with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS. For contrast sensitivity tests, CSV-1000 generated more expected information gain than Pelli-Robson; scored with AULCSF or with CS at six spatial frequencies, qCSF with 25 trials generated more expected information gain than CSV-1000. The active learning based qVA and qCSF tests can generate more expected information than the traditional paper chart tests. Although we only applied it to compare visual acuity and contrast sensitivity tests, information gain is a general concept that can be used to compare measurements and data analytics in any domain.

Contrast Sensitivity Is Associated With Chorioretinal Thickness and Vascular Density of Eyes in Simple Early-Stage High Myopia.

Objective: To evaluate the contrast sensitivity function (CSF), chorioretinal thickness and vascular density as well as their relationships in subjects with simple early-stage high myopia. Methods: Eighty-one young subjects were enrolled in this study. They were categorized into the simple high myopia group (sHM, n = 51) and the low-moderate myopia group (control group, n = 30). Monocular CSF under best correction was measured with the qCSF method. Retinal superficial and deep vascular density, inner and outer retinal thickness and choroidal thickness were measured using optical coherence tomography angiography. Results: The area under log CSF (AULCSF) and cutoff spatial frequency (Cutoff SF) of the sHM group were significantly reduced compared to those of the control group (P = 0.003 and P < 0.001, respectively). The parafoveal and perifoveal retinal thickness, deep vascular density and choroidal thickness were also significantly reduced in the sHM group (all P < 0.05). Multiple regression analysis revealed that AULCSF was significantly correlated with retinal deep vascular density, outer retinal thickness in the parafoveal and perifoveal areas (all P < 0.05). Conclusion: Compared to low to moderate myopic eyes, patients with simple high myopia have thinner retinal and choroidal thickness, lower retinal vascular density, and reduced contrast sensitivity. Moreover, the CSF was correlated with the measures of chorioretinal structure and vasculature. The results suggest that the CSF is a sensitive functional endpoint in simple early-stage high myopia.

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design.

Recent development of the quick contrast sensitivity function (qCSF) method has made it possible to obtain accurate, precise, and efficient contrast sensitivity function (CSF) assessment. To improve statistical inference on CSF changes in a within-subject design, we developed a hierarchical Bayesian model (HBM) to compute the joint distribution of CSF parameters and hyperparameters at test, subject, and population levels, utilizing information within- and between-subjects and experimental conditions. We evaluated the performance of the HBM relative to a non-hierarchical Bayesian inference procedure (BIP) on an existing CSF dataset of 112 subjects obtained with the qCSF method in three luminance conditions (Hou, Lesmes, Kim, Gu, Pitt, Myung, & Lu, 2016). We found that the average d's of the area under log CSF (AULCSF) and CSF parameters between pairs of luminance conditions at the test-level from the HBM were 33.5% and 103.3% greater than those from the BIP analysis of AULCSF. The increased d' resulted in greater statistical differences between experimental conditions across subjects. In addition, simulations showed that the HBM generated accurate and precise CSF parameter estimates. These results have strong implications for the application of HBM in clinical trials and patient care.

Quantifying Uncertainty of the Estimated Visual Acuity Behavioral Function With Hierarchical Bayesian Modeling.

Purpose: The goal of this study is to develop a hierarchical Bayesian model (HBM) to better quantify uncertainty in visual acuity (VA) tests by incorporating the relationship between VA threshold and range across multiple individuals and tests. Methods: The three-level HBM consisted of multiple two-dimensional Gaussian distributions of hyperparameters and parameters of the VA behavioral function (VABF) at the population, individual, and test levels. The model was applied to a dataset of quantitative VA (qVA) assessments of 14 eyes in 4 Bangerter foil conditions. We quantified uncertainties of the estimated VABF parameters (VA threshold and range) from the HBM and compared them with those from the qVA. Results: The HBM recovered covariances between VABF parameters and provided better fits to the data than the qVA. It reduced the uncertainty of their estimates by 4.2% to 45.8%. The reduction of uncertainty, on average, resulted in 3 fewer rows needed to reach a 95% accuracy in detecting a 0.15 logMAR change of VA threshold or both parameters than the qVA. Conclusions: The HBM utilized knowledge across individuals and tests in a single model and provided better quantification of the uncertainty of the estimated VABF, especially when the number of tested rows was relatively small. Translational Relevance: The HBM can increase the accuracy in detecting VA changes. Further research is necessary to evaluate its potential in clinical populations.

Evaluating the Performance of qVFM in Mapping the Visual Field of Simulated Observers With Eye Diseases.

PURPOSE: Recently, we developed a novel active learning framework, qVFM, to map visual functions in the visual field. The method has been implemented and validated in measuring light sensitivity and contrast sensitivity visual field maps (VFMs) of normal observers. In this study, we evaluated the performance of the qVFM method in mapping the light sensitivity VFM of simulated patients with peripheral scotoma, glaucoma, age-related macular degeneration (AMD), and cataract. METHODS: For each simulated patient, we sampled 100 locations (60 × 60 degrees) of the visual field and compared the performance of the qVFM method with a procedure that tests each location independently (the qYN method) in a cued Yes/No task. Two different switch modules, the distribution sampling method (DSM) and parameter delivering method (PDM), were implemented in the qVFM method. Simulated runs of 1,200 trials were used to compare the accuracy and precision of the qVFM-DSM, qVFM-PDM and qYN methods. RESULTS: The qVFM method with both switch modules can provide accurate, precise, and efficient assessments of the light sensitivity VFM for the simulated patients, with the qVFM-PDM method better at detecting VFM deficits in the simulated glaucoma. CONCLUSIONS: The qVFM method can be used to characterize residual vision of simulated ophthalmic patients. The study sets the stage for further investigation with real patients and potential translation of the method into clinical practice.

Normal- and Low-Luminance Automated Quantitative Contrast Sensitivity Assessment in Eyes With Age-Related Macular Degeneration.

PURPOSE: To assess the effectiveness of an active learning approach to measuring the contrast sensitivity function (CSF) in patients with various degrees of dry age-related macular degeneration (AMD) under multiple luminance conditions. DESIGN: Cross-sectional study. METHODS: Patients with AMD (26 intermediate AMD, 19 AMD with subretinal drusenoid deposits [SDD], 20 geographic atrophy [GA]) and 23 age-matched controls were tested with the Manifold Contrast Vision Meter (Adaptive Sensory Technology) and the qCSF algorithm, which applies active learning to estimate a model of the CSF's global shape. Testing was performed under conditions of standard and low luminance. For each AMD severity, the area under log CSF (AULCSF) and contrast sensitivities at individual spatial frequencies were calculated for analysis. Low-luminance deficits (LLDs) for visual acuity (VA) and AULCSF were calculated as the difference between standard and low luminance values. RESULTS: Progressive decreases in AULCSF were observed as disease severity increased. For standard luminance, pairwise comparisons revealed significant differences between control/intermediate AMD (P < .0005), control/SDD (P < .0005), control/GA (P < .0005), and intermediate AMD/GA (P < .005). Similarly, for low luminance, pairwise comparisons revealed significant differences between the controls and each disease group (all P < .0005), in addition to significant differences between intermediate AMD/SDD (P < .005), and intermediate AMD/GA (P < .005). No correlations were found between LLD VA and LLD AULCSF in any AMD groups. CONCLUSIONS: Contrast sensitivity measured via qCSF under both standard- and low-luminance conditions correlates with advancing stages of dry AMD. The interaction between luminance and contrast sensitivity appears to reflect a different aspect of visual function than the interaction between luminance and VA.

Psychophysical Validation of a Novel Active Learning Approach for Measuring the Visual Acuity Behavioral Function.

Purpose: To evaluate the performance of the quantitative visual acuity (qVA) method in measuring the visual acuity (VA) behavioral function. Methods: We evaluated qVA performance in terms of the accuracy, precision, and efficiency of the estimated VA threshold and range in Monte Carlo simulations and a psychophysical experiment. We also compared the estimated VA threshold from the qVA method with that from the Electronic Early Treatment Diabetic Retinopathy Study (E-ETDRS) and Freiburg Visual Acuity Text (FrACT) methods. Four repeated measures with all three methods were conducted in four Bangerter foil conditions in 14 eyes. Results: In both simulations and psychophysical experiment, the qVA method quantified the full acuity behavioral function with two psychometric parameters (VA threshold and VA range) with virtually no bias and with high precision and efficiency. There was a significant correlation between qVA estimates of VA threshold and range in the psychophysical experiment. In addition, qVA threshold estimates were highly correlated with those from the E-ETDRS and FrACT methods. Conclusions: The qVA method can provide an accurate, precise, and efficient assessment of the full acuity behavioral function with both VA threshold and range. Translational Relevance: The qVA method can accurately, precisely, and efficiently assess the full VA behavioral function. Further research will evaluate the potential value of these rich measures for both clinical research and patient care.

Mapping the Contrast Sensitivity of the Visual Field With Bayesian Adaptive qVFM.

Current clinical evaluation, which focuses on central vision, could be improved through characterization of residual vision with peripheral testing of visual acuity, contrast sensitivity, color vision, crowding, and reading speed. Assessing visual functions in addition to light sensitivity, a comprehensive visual field map (VFM) would be valuable for detecting and managing eye diseases. In a previous study, we developed a Bayesian adaptive qVFM method that combines a global module for preliminary assessment of the VFM's shape and a local module for assessment at individual retinal locations. The method was validated in measuring the light sensitivity VFM. In this study, we extended the qVFM method to measure contrast sensitivity across the visual field. In both simulations and psychophysics, we sampled 64 visual field locations (48 x 48 deg) and compared the qVFM method with a procedure that tested each retinal location independently (qFC; Lesmes et al., 2015). In each trial, subjects were required to identify a single optotype (size: 2.5 x 2.5 deg), one of 10 filtered Sloan letters. To compare the accuracy and precision of the two methods, three simulated eyes were tested in 1,280 trials with each method. In addition, data were collected from 10 eyes (5 OS, 5 OD) of five normal observers. For simulations, the average RMSE of the estimated contrast sensitivity with the qVFM and qFC methods were 0.057 and 0.100 after 320 trials, and 0.037 and 0.041 after 1,280 trials [all in log10 units, represent as log(sensitivity)], respectively. The average SD of the qVFM and qFC estimates were 0.054 and 0.096 after 320 trials, and 0.032 and 0.041 after 1,280 trials, respectively. The within-run variability (68.2% HWCIs) were comparable to the cross-run variability (SD). In the psychophysics experiment, the average HWCI of the estimated contrast sensitivity from the qVFM and qFC methods across the visual field decreased from 0.33 on the first trial to 0.072 and 0.16 after 160, and to 0.060 and 0.10 after 320 trials. The RMSE between the qVFM and qFC estimates started at 0.26, decreased to 0.12 after 160 and to 0.11 after 320 qVFM trials. The qVFM provides an accurate, precise, and efficient mapping of contrast sensitivity across the entire visual field. The method might find potential clinical applications in monitoring vision loss, evaluating therapeutic interventions, and developing effective rehabilitation for visual diseases.

Measuring Contrast Sensitivity Function With Active Learning in Retinal Vein Occlusion: A New Endpoint of Visual Function.

BACKGROUND AND OBJECTIVE: To characterize contrast sensitivity function (CSF) in patients with retinal vein occlusion (RVO) compared to age-matched controls using novel computerized contrast sensitivity (CS) testing with active learning algorithms. PATIENTS AND METHODS: CSF was prospectively measured in RVO patients with visual acuity (VA) greater than 20/200 and age-matched controls using the novel Manifold Contrast Vision Meter implementing quantitative CSF testing. Outcomes included area under the Log CSF (AULCSF), contrast acuity (CA), and CS thresholds at 1, 1.5, 3, 12, and 18 cycles per degree (cpd). A sub-analysis was performed on RVO eyes with good acuity (VA ≥ 20/30). RESULTS: Twenty-two eyes with RVO and 63 control eyes were included. Mean AULCSF (± standard deviation) in RVO eyes was 0.817 (0.28) compared to 1.217 (0.28) in controls (P < .0001). Mean contrast acuity in the RVO group was 1.054 (0.19) versus 1.286 ± 0.16 in controls (P < .0001). For individual spatial frequencies, CS loss at 6.0 cpd was most prominent in the RVO group. In 10 RVO eyes with VA of 20/30 or greater, mean AULCSF was 0.978 versus 1.217 in control eyes. (P = .008). CONCLUSIONS: CSF in eyes with RVO was found to be significantly reduced compared to age-matched controls. CSF seems to be a promising visual function endpoint with potential applications in clinical practice and future clinical trials. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:392-400.].

New Technologies for Outcome Measures in Glaucoma: Review by the European Vision Institute Special Interest Focus Group.

Glaucoma is the leading cause of irreversible blindness worldwide, with an increasing prevalence. The complexity of the disease has been a major challenge in moving the field forward with regard to both pathophysiological insight and treatment. In this context, discussing possible outcome measures in glaucoma trials is of utmost importance and clinical relevance. A recent meeting of the European Vision Institute (EVI) special interest focus group was held on "New Technologies for Outcome Measures in Retina and Glaucoma," addressing both functional and structural outcomes, as well as translational hot topics in glaucoma and retina research. In conjunction with the published literature, this review summarizes the meeting focusing on glaucoma.

A novel Bayesian adaptive method for mapping the visual field.

Measuring visual functions such as light and contrast sensitivity, visual acuity, reading speed, and crowding across retinal locations provides visual-field maps (VFMs) that are extremely valuable for detecting and managing eye diseases. Although mapping light sensitivity is a standard glaucoma test, the measurement is often noisy (Keltner et al., 2000). Mapping other visual functions is even more challenging. To improve the precision of light-sensitivity mapping and enable other VFM assessments, we developed a novel hybrid Bayesian adaptive testing framework, the qVFM method. The method combines a global module for preliminary assessment of the VFM's shape and a local module for assessing individual visual-field locations. This study validates the qVFM method in measuring light sensitivity across the visual field. In both simulation and psychophysics studies, we sampled 100 visual-field locations (60° × 60°) and compared the performance of qVFM with the qYN procedure (Lesmes et al., 2015) that measured light sensitivity at each location independently. In the simulations, a simulated observer was tested monocularly for 1,000 runs with 1,200 trials/run, to compare the accuracy and precision of the two methods. In the experiments, data were collected from 12 eyes (six left, six right) of six human subjects. Subjects were cued to report the presence or absence of a target stimulus, with the luminance and location of the target adaptively selected in each trial. Both simulations and a psychological experiment showed that the qVFM method can provide accurate, precise, and efficient mapping of light sensitivity. This method can be extended to map other visual functions, with potential clinical signals for monitoring vision loss, evaluating therapeutic interventions, and developing effective rehabilitation for low vision.

Comparing Spatial Contrast Sensitivity Functions Measured With Digit and Grating Stimuli.

PURPOSE: The contrast sensitivity function (CSF) is measured traditionally with grating stimuli. Recently, we introduced a new set of digit stimuli to improve the efficiency of CSF tests for people unfamiliar with the Latin alphabet. Given the significant differences between grating and digit stimuli, we conducted this study to evaluate whether the estimated CSFs from the digit test are equivalent to those from the grating test. METHODS: The CSFs of five young (with Psi) and five older (with quick CSF [qCSF]) participants were measured with a two-alternative forced choice (2AFC) grating orientation identification task and a 10-digit identification task. The CSFs obtained from the two tasks were compared. RESULTS: The estimated CSFs from the two tasks matched well after controlling for stimulus types and performance levels. The root mean square error (RMSE) between the CSFs from the two tasks was 0.093 ± 0.029 (300 trials) and 0.131 ± 0.016 (100 trials) log10 units for young and older observers, respectively. To reach the same standard deviation (0.1 log10 units), the digit CSF test required fewer trials/less time than the classic grating CSF for young (60 vs. 90 trials) and older (15 vs. 21 trials) observers. The complicated behavioral responses of the observer in the 10-AFC digit identification task can be accounted by a model that consists of digit similarity and one single parameter of sensory noise (χ2[99] = 3.42, P = 0.999). CONCLUSIONS: The estimated CSFs from the digit test highly matched those obtained from the grating test; however, the digit test is much more efficient. TRANSLATIONAL RELEVANCE: The digit CSF test provides a compatible assessment of the CSF as the traditional grating CSF test with more efficiency.

Binocular Summation and Suppression of Contrast Sensitivity in Strabismus, Fusion and Amblyopia.

Purpose: Amblyopia and strabismus affect 2%-5% of the population and cause a broad range of visual deficits. The response to treatment is generally assessed using visual acuity, which is an insensitive measure of visual function and may, therefore, underestimate binocular vision gains in these patients. On the other hand, the contrast sensitivity function (CSF) generally takes longer to assess than visual acuity, but it is better correlated with improvement in a range of visual tasks and, notably, with improvements in binocular vision. The present study aims to assess monocular and binocular CSFs in amblyopia and strabismus patients. Methods: Both monocular CSFs and the binocular CSF were assessed for subjects with amblyopia (n = 11), strabismus without amblyopia (n = 20), and normally sighted controls (n = 24) using a tablet-based implementation of the quick CSF, which can assess a full CSF in <3 min. Binocular summation was evaluated against a baseline model of simple probability summation. Results: The CSF of amblyopic eyes was impaired at mid-to-high spatial frequencies compared to fellow eyes, strabismic eyes without amblyopia, and control eyes. Binocular contrast summation exceeded probability summation in controls, but not in subjects with amblyopia (with or without strabismus) or strabismus without amblyopia who were able to fuse at the test distance. Binocular summation was less than probability summation in strabismic subjects who were unable to fuse. Conclusions: We conclude that monocular and binocular contrast sensitivity deficits define important characteristics of amblyopia and strabismus that are not captured by visual acuity alone and can be measured efficiently using the quick CSF.

Assessing reading performance in the periphery with a Bayesian adaptive approach: The qReading method.

Reading is a crucial visual activity and a fundamental skill in daily life. Rapid Serial Visual Presentation (RSVP) is a text-presentation paradigm that has been extensively used in the laboratory to study basic characteristics of reading performance. However, measuring reading function (reading speed vs. print size) is time-consuming for RSVP reading using conventional testing procedures. In this study, we develop a novel method, qReading, utilizing the Bayesian adaptive testing framework to measure reading function in the periphery. We perform both a psychophysical experiment and computer simulations to validate the qReading method. In the experiment, words are presented using an RSVP paradigm at 10° in the lower visual field. The reading function obtained from the qReading method with 50 trials exhibits good agreement (i.e., high accuracy) with the reading function obtained from a conventional method (method of constant stimuli [MCS]) with 186 trials (mean root mean square error: 0.12 log10 units). Simulations further confirm that the qReading method provides an unbiased measure. The qReading procedure also demonstrates excellent precision (half width of 68.2% credible interval: 0.02 log10 units with 50 trials) compared to the MCS method (0.03 log10 units with 186 trials). This investigation establishes that the qReading method can adequately measure the reading function in the normal periphery with high accuracy, precision, and efficiency, and is a potentially valuable tool for both research and clinical assessments.

Efficient assessment of the time course of perceptual sensitivity change.

Perceptual sensitivity is usually estimated over trials and time intervals, which results in imprecise and biased estimates when it changes rapidly over time. We develop a novel procedure, the quick Change-Detection (qCD) method, to accurately, precisely, and efficiently assess the trial-by-trial time course of perceptual sensitivity change. Based on Bayesian adaptive testing, qCD selects the optimal stimulus, and updates, trial by trial, a joint probability distribution of the parameters that quantify perceptual sensitivity change over time. We demonstrate the utility of the method in measuring the time course of dark adaptation. Simulations showed that the accuracy and precision of the estimated dark adaptation curve after one qCD run (root mean squared error (RMSE): 0.002; the half width of the 68.2% credible interval (HWCI): 0.016; standard deviation (SD): 0.020; all in log10 units) was higher than those obtained by ten runs of the quick Forced-Choice (qFC) procedure (RMSE: 0.020; HWCI: 0.032; SD: 0.031) and ten runs of a weighted up-down staircase procedure (RMSE: 0.026; SD: 0.031). Further, the dark adaptation curve obtained from one qCD run in a psychophysics experiment was highly consistent with the average of four qFC runs (RMSE = 0.076 log10 units). Overall, qCD provides a procedure to characterize the detailed time course of perceptual sensitivity change in both basic research and clinical applications.

Measuring the Contrast Sensitivity Function Using the qCSF Method With 10 Digits.

PURPOSE: The Bayesian adaptive quick contrast sensitivity function (qCSF) method with a 10-letter identification task provides an efficient CSF assessment. However, large populations are unfamiliar with letters and cannot benefit from this test. To overcome the barrier, we conducted this study. METHOD: A new font for digits (0∼9) was created. The digits were then filtered with a raised cosine filter, rescaled to different sizes to cover spatial frequencies from 0.5 to 16 cycles per degree (cpd), and used as stimuli in a 10-alternative forced choice (10AFC) digit identification task. With the 10AFC digit identification task, the CSFs of five young and five old observers were measured using the qCSF and Psi methods. The estimates from the latter served as reference. RESULTS: The new digit font showed significantly improved similarity structure, Levene's test, F(1, 88) = 6.36, P = 0.014. With the 10-digit identification task, the CSFs obtained with the qCSF method matched well with those obtained with the Psi method (root mean square error [RMSE] = 0.053 log10 units). With approximately 30 trials, the precision of the qCSF method reached 0.1 log10 units. With approximately 75 trials, the precision of the CSFs obtained with the qCSF was comparable to that of the CSFs measured by the Psi method in 150 trials. CONCLUSIONS: The qCSF with the 10 digit identification task is validated for both young and old observers. TRANSLATIONAL RELEVANCE: The qCSF method with the 10-digit identification task provides an efficient and precise CSF test especially for people who are unfamiliar with letters.

Bayesian adaptive assessment of the reading function for vision: The qReading method.

Reading is a fundamental skill that can be significantly affected by visual disabilities. Reading performance, which typically is measured as reading speed with a reading chart, is a key endpoint for quantifying normal or abnormal vision. Despite its importance for clinical vision, existing reading tests for vision are time consuming and difficult to administer. Here, we propose a Bayesian adaptive method, the qReading method, for automated assessment of the reading speed versus print size function. We implemented the qReading method with a word/nonword lexical decision task and validated the method with computer simulations and a psychophysical experiment. Computer simulations showed that both the interrun standard deviation and intrarun half width of the 68.2% credible interval of the estimated reading speeds from the qReading method were less than 0.1 log10 units after 150 trials, with a bias of 0.05 log10 units. In the psychophysical experiment, reading functions measured by the qReading and Psi methods (Kontsevich & Tyler, 1999) in a word/nonword lexical decision task were compared. The estimated reading functions obtained with the qReading and Psi methods were highly correlated (r = 0.966 ± 0.004, p < 0.01). The precision of the qReading method with 225 trials was comparable to that of the Psi method with 450 trials. We conclude that the qReading method can precisely and accurately assess the reading function in much reduced time, with great promise in both basic research and clinical applications.