Visual Acuity of Simulated Thalamic Visual Prostheses in Normally Sighted Humans

Abstract

Simulation in normally sighted individuals is a crucial tool to evaluate the performance of potential visual prosthesis designs prior to human implantation of a device. Here, we investigated the effects of electrode count on visual acuity, learning rate and response time in 16 normally sighted subjects using a simulated thalamic visual prosthesis, providing the first performance reports for thalamic designs. A new letter recognition paradigm using a multiple-optotype two-alternative forced choice task was adapted from the Snellen eye chart, and specifically devised to be readily communicated to both human and non-human primate subjects. Validation of the method against a standard Snellen acuity test in 21 human subjects showed no significant differences between the two tests. The novel task was then used to address three questions about simulations of the center-weighted phosphene patterns typical of thalamic designs: What are the expected Snellen acuities for devices with varying numbers of contacts, do subjects display rapid adaptation to the new visual modality, and can response time in the task provide clues to the mechanisms of perception in low-resolution artificial vision? Population performance (hit rate) was significantly above chance when viewing Snellen 20/200 optotypes (Log MAR 1.0) with 370 phosphenes in the central 10 degrees of vision, ranging to Snellen 20/800 (Log MAR 1.6) with 25 central phosphenes. Furthermore, subjects demonstrated learning within the 1–2 hours of task experience indicating the potential for an effective rehabilitation and possibly better visual performance after a longer period of training. Response time differences suggest that direct letter perception occurred when hit rate was above 75%, whereas a slower strategy like feature-based pattern matching was used in conditions of lower relative resolution. As pattern matching can substantially boost effective acuity, these results suggest post-implant therapy should specifically address feature detection skills.