SymbolSight: Minimizing Inter-Symbol Interference for Reading with Prosthetic Vision

TL;DR

SymbolSight is a computational framework that optimizes symbol-to-letter mappings to reduce confusion and improve reading accuracy in prosthetic vision, especially under low-resolution and sequential presentation constraints.

Contribution

It introduces a novel method to select symbol mappings that minimize letter confusion, enhancing reading performance in prosthetic vision without hardware changes.

Findings

Heterogeneous symbol sets reduced confusion by median factor of 22

Simulations in multiple languages show broad applicability

Highlights mismatch between standard typography and prosthetic vision constraints

Abstract

Retinal prostheses restore limited visual perception, but low spatial resolution and temporal persistence make reading difficult. In sequential letter presentation, the afterimage of one symbol can interfere with perception of the next, leading to systematic recognition errors. Rather than relying on future hardware improvements, we investigate whether optimizing the visual symbols themselves can mitigate this temporal interference. We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using language-specific bigram statistics. Across simulations in Arabic, Bulgarian, and English, the resulting heterogeneous symbol sets reduced predicted confusion by a median factor of 22 relative to native alphabets. These results suggest that standard typography is poorly matched to serial, low-bandwidth prosthetic vision and demonstrate how computational modeling can narrow the design space of visual encodings, identifying high-potential candidates for future psychophysical and clinical evaluation rather than predicting present-day clinical reading performance directly.