Abstract: Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.

Abstract: A perceptual-learning system and method is used to improve age-related declines in contrast sensitivity. The system and method comprises a processor and monitor, and a first set of instructions executable on the processor configured to familiarize subjects with the system. A second set of instructions are executable on the processor configured for training the subjects.

Abstract: A perceptual-learning system and method is used to improve age-related declines in contrast sensitivity. The system and method comprises a processor and monitor, and a first set of instructions executable on the processor configured to familiarize subjects with the system. A second set of instructions are executable on the processor configured for training the subjects.