Abstract: A virtual reality neuropsychological assessment (VRNA) system uses a deep learning network and a VR headset to administer multi-domain assessments of human cognitive performance. The deep learning network is trained to identify features in sensor data indicative of neuropsychological performance and classify users based on the features identified in the sensor data. The VR headset provides a user with a virtual simulation of an activity involving decision-making scenarios. During the virtual simulation, sensor data via a plurality of sensors of the VR headset is captured. The sensor data is applied to the deep learning network to identify features of the user and classify the user based on the features into a neuropsychological domains, such as attention, memory, processing speed, and executive function. Sensor data includes eye-tracking, hand-eye motor coordination, reaction time, working memory, learning and delayed memory, and inhibitory control.