An, Na Min Roh, Hyeonhee Kim, Sein Kim, Jae Hun Im, Maesoon 2024-01-12T02:46:08Z 2024-01-12T02:46:08Z 2023-10-05 2023-06 2161-4393 https://pubs.kist.re.kr/handle/201004/76435 The quality of the artificial vision produced by visual prostheses has traditionally been evaluated by human psychophysical tests using images expressed with an array of phosphenes. However, such experiments involving human subjects are considerably time-consuming and labor-intensive. One potential solution may be implementing an efficient approach to assist or replace psychophysical experiments showing a short-term learning effect in human subjects. The present work developed a reinforcement learning (RL)-based feedback framework which built artificial agents to emulate the behavioral changes in the learning process of human subjects over the experimental trials. In our framework, we first trained an agent which can gradually learn to identify 720 faces presented in low-resolution phosphene images with feedback rewards received from the agreement in the perception of nine training human subjects. Then, in the automating stage of the framework, we created nine RL agents. By testing those agents, we found the RL agents mimicked the learning effects of nine test human subjects better than nine instances of a supervised learning (SL) model. Given the similar outcomes with human tests and the time efficiency of RL, our framework may expedite the development of visual prosthetic systems by at least partially replacing laborious human psychophysical experiments. English IEEE Reinforcement Learning Framework to Simulate Short-Term Learning Effects of Human Psychophysical Experiments Assessing the Quality of Artificial Vision Conference 10.1109/IJCNN54540.2023.10191870 1 International Joint Conference on Neural Networks (IJCNN) International Joint Conference on Neural Networks (IJCNN) US Broadbeach, AUSTRALIA 2023-06-18 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN 001046198706062 2-s2.0-85169617898