优化字母符号设计，减少假体视觉阅读时的干扰。
SymbolSight: Minimizing Inter-Symbol Interference for Reading with Prosthetic Vision
- 用语言大词频统计优化符号与字母的映射关系。
- 模拟显示下误认率中位数降低22倍。
- 适合假体视觉系统设计者和神经工程研究者。
视网膜假体恢复有限视觉感知，但低空间分辨率和时间滞留导致阅读困难。在顺序呈现字母时，前一个符号的残像会干扰后续符号的识别，引发系统性错误。我们不依赖未来硬件改进，而是探索通过优化符号本身来缓解这种时间干扰。提出SymbolSight计算框架，基于语言特异的大词频统计，选择能最小化频繁相邻字母间混淆的符号-字母映射。利用模拟假体视觉（SPV）和神经代理观察者，估算符号混淆度并进行优化。在阿拉伯语、保加利亚语和英语的模拟中，所得非均匀符号集相比原字母表预测混淆度中位降低22倍。结果表明标准字体与序列式、低带宽假体视觉严重不匹配，且计算建模可缩小视觉编码设计空间，识别出高潜力候选方案用于未来心理物理学和临床评估，而非直接预测当前临床阅读表现。
原文摘要 · Abstract (English)
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.
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