用真人测试视觉假体优化方法，发现人机协同能显著提升效果。
Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants
- 让志愿者在模拟视野中对比不同刺激方案，逐步优化深度编码器。
- 所有条件下人机协同生成的刺激都优于初始编码器和单独模型。
- 提醒需用人验证算法，适合假体个性化开发团队参考。
人机协同优化（HILO）是一种通过用户反馈迭代调整刺激参数以个性化视觉假体的有前景方法。以往研究在仿真中验证了其有效性，但尚未在真人参与者中测试。本文利用视障者模拟视觉假体的视觉体验，评估了三类条件下的HILO表现：标准优化、阈值误设以及分布外参数采样。参与者在多个对比任务中持续选择由竞争编码器生成的光幻视，以迭代优化深度刺激编码器（DSE）。结果显示，在所有条件下，人类偏好由HILO生成的刺激，对数优势比均支持该方法。此外，观察到人类与仿真决策间的关键差异，凸显了在真实人参与下验证优化策略的重要性。这些结果支持将HILO作为未来视觉假体个体化适配的有效路径。临床意义：通过视障者模拟视觉假体体验来验证HILO，是实现未来假体个性化校准的重要一步。
原文摘要 · Abstract (English)
Human-in-the-loop optimization (HILO) is a promising approach for personalizing visual prostheses by iteratively refining stimulus parameters based on user feedback. Previous work demonstrated HILO's efficacy in simulation, but its performance with human participants remains untested. Here we evaluate HILO using sighted participants viewing simulated prosthetic vision to assess its ability to optimize stimulation strategies under realistic conditions. Participants selected between phosphenes generated by competing encoders to iteratively refine a deep stimulus encoder (DSE). We tested HILO in three conditions: standard optimization, threshold misspecifications, and out-of-distribution parameter sampling. Participants consistently preferred HILO-generated stimuli over both a naive encoder and the DSE alone, with log odds favoring HILO across all conditions. We also observed key differences between human and simulated decision-making, highlighting the importance of validating optimization strategies with human participants. These findings support HILO as a viable approach for adapting visual prostheses to individuals. Clinical relevance: Validating HILO with sighted participants viewing simulated prosthetic vision is an important step toward personalized calibration of future visual prostheses.
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