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      A novel Bayesian adaptive method for mapping the visual field

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          Abstract

          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.

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          Most cited references 149

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          QUEST: a Bayesian adaptive psychometric method.

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            Adaptive procedures in psychophysical research.

             R Leek (2001)
            As research on sensation and perception has grown more sophisticated during the last century, new adaptive methodologies have been developed to increase efficiency and reliability of measurement. An experimental procedure is said to be adaptive if the physical characteristics of the stimuli on each trial are determined by the stimuli and responses that occurred in the previous trial or sequence of trials. In this paper, the general development of adaptive procedures is described, and three commonly used methods are reviewed. Typically, a threshold value is measured using these methods, and, in some cases, other characteristics of the psychometric function underlying perceptual performance, such as slope, may be developed. Results of simulations and experiments with human subjects are reviewed to evaluate the utility of these adaptive procedures and the special circumstances under which one might be superior to another.
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              Adaptive psychophysical procedures.

              Improvements in measuring thresholds, or points on a psychometric function, have advanced the field of psychophysics in the last 30 years. The arrival of laboratory computers allowed the introduction of adaptive procedures, where the presentation of the next stimulus depends on previous responses of the subject. Unfortunately, these procedures present themselves in a bewildering variety, though some of them differ only slightly. Even someone familiar with several methods cannot easily name the differences, or decide which method would be best suited for a particular application. This review tries to illuminate the historical background of adaptive procedures, explain their differences and similarities, and provide criteria for choosing among the various techniques.
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                Author and article information

                Contributors
                Journal
                J Vis
                J Vis
                jovi
                J Vis
                JOVI
                Journal of Vision
                The Association for Research in Vision and Ophthalmology
                1534-7362
                2019
                17 December 2019
                : 19
                : 14
                Affiliations
                xu.2844@ 123456osu.edu
                luis.lesmes@ 123456adaptivesensorytech.com
                yu.858@ 123456osu.edu
                zhonglin@ 123456nyu.edu
                College of Optometry, The Ohio State University, Columbus, OH, USA
                Adaptive Sensory Technology, Inc., San Diego, CA, USA
                College of Optometry, The Ohio State University, Columbus, OH, USA
                Division of Arts and Sciences, NYU Shanghai, Shanghai, China
                Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
                NYU-ECNU Institute of Cognitive Neuroscience at NYU Shanghai, Shanghai, China
                Article
                jovi-19-13-09 JOV-06857-2019
                10.1167/19.14.16
                6917184
                31845976
                Copyright 2019 The Authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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