ArticleScientific reports2016
Perceptual learning in a non-human primate model of artificial vision.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Seeing anew: Mechanisms, outcomes, and challenges in adapting to restored sight via visual prostheses.Progress in retinal and eye research · 2026Review
- Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation.Bioengineering (Basel, Switzerland) · 2024Review
- More than expected: extracellular waveforms and functional responses in monkey LGN.bioRxiv : the preprint server for biology · 2024Article
- The Influence of Phosphene Synchrony in Driving Object Binding in a Simulation of Artificial Vision.Investigative ophthalmology & visual science · 2023Article
- Attitudes of potential recipients toward emerging visual prosthesis technologies.Scientific reports · 2023Article
- Chronic stability of a neuroprosthesis comprising multiple adjacent Utah arrays in monkeys.Journal of neural engineering · 2023Article
- Reading text works better than watching videos to improve acuity in a simulation of artificial vision.Scientific reports · 2022Article
- Neurophysiological considerations for visual implants.Brain structure & function · 2022Review
- Full gaze contingency provides better reading performance than head steering alone in a simulation of prosthetic vision.Scientific reports · 2021Article
- Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1.Scientific reports · 2021Article
- The impact of synchronous versus asynchronous electrical stimulation in artificial vision.Journal of neural engineering · 2021Review
- Toward Long-Term Communication With the Brain in the Blind by Intracortical Stimulation: Challenges and Future Prospects.Frontiers in neuroscience · 2020Article
- Improvement in reading performance through training with simulated thalamic visual prostheses.Scientific reports · 2018Article
- Development of visual Neuroprostheses: trends and challenges.Bioelectronic medicine · 2018Review
- Eye Movement Compensation and Spatial Updating in Visual Prosthetics: Mechanisms, Limitations and Future Directions.Frontiers in systems neuroscience · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Visual perceptual grouping, the process of forming global percepts from discrete elements, is experience-dependent. Here we show that the learning time course in an animal model of artificial vision is predicted primarily from the density of visual elements. Three naïve adult non-human primates were tasked with recognizing the letters of the Roman alphabet presented at variable size and visualized through patterns of discrete visual elements, specifically, simulated phosphenes mimicking a thalamic visual prosthesis. The animals viewed a spatially static letter using a gaze-contingent pattern and then chose, by gaze fixation, between a matching letter and a non-matching distractor. Months of learning were required for the animals to recognize letters using simulated phosphene vision. Learning rates increased in proportion to the mean density of the phosphenes in each pattern. Furthermore, skill acquisition transferred from trained to untrained patterns, not depending on the precise retinal layout of the simulated phosphenes. Taken together, the findings suggest that learning of perceptual grouping in a gaze-contingent visual prosthesis can be described simply by the density of visual activation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.