Tools in Haptic Exploration with Early Loss of Vision: Using Deep Learning to Investigate Complex Novel Neurocognitive Processes and Underlying Plasticity

During development, specialized regions in the brain are primed to take on specific tasks such as language, motor control, and object recognition. In cases where the ability to perform these tasks is disrupted, compensatory plasticity describes the remapping of these regions to support other cognitive functions. In our study, we explore potential cognitive remapping in Congenitally Totally Blind (CTB) and Sight but Visually Impaired (SVI) children performing a haptic identification task with embossed images. We employ deep learning to quantify sensory attention and stochastic exploration by tracking finger movements in recorded videos. This sensory and kinematic data reveals movement patterns that may offer insight into different tools used between and within groups which may relate to potential compensatory plasticity associated with a performance differences that was found between CTB and SVI children.