Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision
Abstract To evaluate the quality of artificial visual percepts generated by emerging methodologies, researchers often rely on labor‐intensive and tedious human psychophysical experiments. These experiments necessitate repeated iterations upon any major/minor modifications in the hardware/software co...
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| Format: | Article |
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Wiley
2025-04-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202405789 |
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| author | Na Min An Hyeonhee Roh Sein Kim Jae Hun Kim Maesoon Im |
| author_facet | Na Min An Hyeonhee Roh Sein Kim Jae Hun Kim Maesoon Im |
| author_sort | Na Min An |
| collection | DOAJ |
| description | Abstract To evaluate the quality of artificial visual percepts generated by emerging methodologies, researchers often rely on labor‐intensive and tedious human psychophysical experiments. These experiments necessitate repeated iterations upon any major/minor modifications in the hardware/software configurations. Here, the capacity of standard machine learning (ML) models is investigated to accurately replicate quaternary match‐to‐sample tasks using low‐resolution facial images represented by arrays of phosphenes as input stimuli. Initially, the performance of the ML models trained to approximate innate human facial recognition abilities across a dataset comprising 3600 phosphene images of human faces is analyzed. Subsequently, due to the time constraints and the potential for subject fatigue, the psychophysical test is limited to presenting only 720 low‐resolution phosphene images to 36 human subjects. Notably, the superior model adeptly mirrors the behavioral trend of human subjects, offering precise predictions for 8 out of 9 phosphene quality levels on the overlapping test queries. Subsequently, human recognition performances for untested phosphene images are predicted, streamlining the process and minimizing the need for additional psychophysical tests. The findings underscore the transformative potential of ML in reshaping the research paradigm of visual prosthetics, facilitating the expedited advancement of prostheses. |
| format | Article |
| id | doaj-art-67f02e69d59642e39fec05a80bde3903 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-67f02e69d59642e39fec05a80bde39032025-08-20T02:26:45ZengWileyAdvanced Science2198-38442025-04-011215n/an/a10.1002/advs.202405789Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial VisionNa Min An0Hyeonhee Roh1Sein Kim2Jae Hun Kim3Maesoon Im4Brain Science Institute Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of KoreaBrain Science Institute Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of KoreaBrain Science Institute Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of KoreaBrain Science Institute Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of KoreaBrain Science Institute Korea Institute of Science and Technology (KIST) Seoul 02792 Republic of KoreaAbstract To evaluate the quality of artificial visual percepts generated by emerging methodologies, researchers often rely on labor‐intensive and tedious human psychophysical experiments. These experiments necessitate repeated iterations upon any major/minor modifications in the hardware/software configurations. Here, the capacity of standard machine learning (ML) models is investigated to accurately replicate quaternary match‐to‐sample tasks using low‐resolution facial images represented by arrays of phosphenes as input stimuli. Initially, the performance of the ML models trained to approximate innate human facial recognition abilities across a dataset comprising 3600 phosphene images of human faces is analyzed. Subsequently, due to the time constraints and the potential for subject fatigue, the psychophysical test is limited to presenting only 720 low‐resolution phosphene images to 36 human subjects. Notably, the superior model adeptly mirrors the behavioral trend of human subjects, offering precise predictions for 8 out of 9 phosphene quality levels on the overlapping test queries. Subsequently, human recognition performances for untested phosphene images are predicted, streamlining the process and minimizing the need for additional psychophysical tests. The findings underscore the transformative potential of ML in reshaping the research paradigm of visual prosthetics, facilitating the expedited advancement of prostheses.https://doi.org/10.1002/advs.202405789artificial visionhuman psychophysical testmachine learningprosthetic vision |
| spellingShingle | Na Min An Hyeonhee Roh Sein Kim Jae Hun Kim Maesoon Im Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision Advanced Science artificial vision human psychophysical test machine learning prosthetic vision |
| title | Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision |
| title_full | Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision |
| title_fullStr | Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision |
| title_full_unstemmed | Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision |
| title_short | Machine Learning Techniques for Simulating Human Psychophysical Testing of Low‐Resolution Phosphene Face Images in Artificial Vision |
| title_sort | machine learning techniques for simulating human psychophysical testing of low resolution phosphene face images in artificial vision |
| topic | artificial vision human psychophysical test machine learning prosthetic vision |
| url | https://doi.org/10.1002/advs.202405789 |
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