Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants
Journal:
arXiv
Published Date:
Jan 31, 2025
Abstract
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.
