Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to improve both accuracy and efficiency simultaneously. Contrast Sensitivity Functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. They are impractically long to be used in many clinical workflows without compromises of some sort, however, such as sampling only a few spatial frequencies or making strong assumptions about the shape of the function. This paper describes the development of the Machine Learning Contrast Response Function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the MLCSF was evaluated in order to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was about an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no discernible advantage to the estimator as configured. The MLCSF exhibits performance characteristics on par with state-of-the-art CSF estimators and therefore should be explored further to uncover its full potential.

Precis Machine learning classifiers enable accurate and efficient contrast sensitivity function estimation with item-level prediction for individual eyes.

The authors have declared no competing interest.

Supported by NIH grants R21EY033553 and R01EY023582.

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Data for this study were simulated from measurements made in human participants.

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