Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915261115793408 |
|---|---|
| author | Schoinas, Eirini Rastogi, Adyah Carter, Anissa Granley, Jacob Beyeler, Michael |
| author_facet | Schoinas, Eirini Rastogi, Adyah Carter, Anissa Granley, Jacob Beyeler, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants Schoinas, Eirini Rastogi, Adyah Carter, Anissa Granley, Jacob Beyeler, Michael Machine Learning Computer Vision and Pattern Recognition Human-Computer Interaction I.2.10 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. |
| title | Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants |
| topic | Machine Learning Computer Vision and Pattern Recognition Human-Computer Interaction I.2.10 |
| url | https://arxiv.org/abs/2502.00177 |