There exists a fundamental gap in understanding of the complex interplay of the integrated processes that determine, induce and regulate plasticity in the perceptual systems. This gap represents an important problem because we can only have a fractured understanding of plasticity in the visual system without knowing how different factors contribute to and interact in learning. This limits our ability to effectively translate basic sciece findings of perceptual plasticity into therapies that could help the over 100 million people worldwide who suffer from low vision;deficits in vision due to disease, injury, stroke or aging that have significant negative impacts on all aspects of individuals'lives. The specific objective to fill the knowledge gap and develop a novel therapeutic approach that integrates, and benefits from, multiple perceptual-learning principles, is driven by the hypothesis that integrating perceptual-learning principles into a coordinated approach, will operate through multiple mechanisms to ameliorate a wider range of visual deficits. We formulated this hypothesis based on research showing that stimulus-reinforcement contingencies, multisensory stimulation, stimulation protocols derived from studies of synaptic plasticity, multi-stimulus training, and video games all enhance the magnitude and quality of learning. However, a key limitation of modern perceptual-learning research is the focus on singular mechanisms, which fail to illuminate the coordinated interactions of multiple learning factors in natural set- tings. Our preliminary data show that a video game that integrates multiple approaches dramatically improves basic visual abilities (e.g., acuity and contrast), and generalizes to untrained stimulus sets. The rationale of the proposed research is that integrated approaches will unravel natural learning mechanisms and improve out- comes for the patients afflicted with low vision.
In Aim 1, we will examine how perceptual learning approaches interact and determine the most efficacious integrated therapy, and in Aim 2, we will apply that therapy to low vision to understand mechanisms of action of the therapy to disease mechanisms affecting eye-dominance, spatial distortion to the retinal mosaic, and rapid change in aberrations in the eye. We are well prepared to undertake the proposed research because the investigative team combines veteran video game production executives and developers with an optometrist and a neuro-ophthalmologist who have experience treating low- vision populations. Aaron Seitz (PI), who developed many of the principles upon which this novel integrative approach is based, anchors the team. The proposed research is innovative because it represents a new and substantive departure from the status quo by integrating multiple principles of perceptual learning. This contribution is significant because the development of effective therapies to treat the brain-based aspect of low vision can lead to life-altering benefits for many millions of people worldwide. Our proposed studies should also catalyze a paradigm shift in the field of vision research, and pave the way for designing efficacious treatments of low vision conditions like amblyopia, age-related macular degeneration and cataracts, in the future.

Public Health Relevance

The proposed research is relevant to public health because a greater understanding of perceptual learning and the development of perceptual learning therapies for treating low vision has potential to benefit millions of individuals suffering from lw vision. The treatment of low vision is particularly relevant to mission of NEI to support research on visual disorders, mechanisms of visual function and preservation of sight.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Cognition and Perception Study Section (CP)
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Wiggs, Cheri
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University of California Riverside
Schools of Arts and Sciences
United States
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Wenliang, Li K; Seitz, Aaron R (2018) Deep Neural Networks for Modeling Visual Perceptual Learning. J Neurosci 38:6028-6044
Maniglia, Marcello; Thurman, Steven M; Seitz, Aaron R et al. (2018) Effect of Varying Levels of Glare on Contrast Sensitivity Measurements of Young Healthy Individuals Under Photopic and Mesopic Vision. Front Psychol 9:899
Thurman, Steven M; Maniglia, Marcello; Davey, Pinakin G et al. (2018) Multi-line Adaptive Perimetry (MAP): A New Procedure for Quantifying Visual Field Integrity for Rapid Assessment of Macular Diseases. Transl Vis Sci Technol 7:28
Sotiropoulos, Grigorios; Seitz, Aaron R; Seriès, Peggy (2018) Performance-monitoring integrated reweighting model of perceptual learning. Vision Res 152:17-39
Maniglia, Marcello; Seitz, Aaron R (2018) Towards a whole brain model of Perceptual Learning. Curr Opin Behav Sci 20:47-55
Butler, Pamela D; Thompson, Judy L; Seitz, Aaron R et al. (2017) Visual perceptual remediation for individuals with schizophrenia: Rationale, method, and three case studies. Psychiatr Rehabil J 40:43-52
Bays, Brett C; Turk-Browne, Nicholas B; Seitz, Aaron R (2016) Dissociable behavioural outcomes of visual statistical learning. Vis cogn 23:1072-1097
Zhou, Tianyou; Náñez Sr, Jose E; Zimmerman, Daniel et al. (2016) Two Visual Training Paradigms Associated with Enhanced Critical Flicker Fusion Threshold. Front Psychol 7:1597
Thurman, Steven M; Davey, Pinakin Gunvant; McCray, Kaydee Lynn et al. (2016) Predicting individual contrast sensitivity functions from acuity and letter contrast sensitivity measurements. J Vis 16:15
Gori, Simone; Seitz, Aaron R; Ronconi, Luca et al. (2016) Multiple Causal Links Between Magnocellular-Dorsal Pathway Deficit and Developmental Dyslexia. Cereb Cortex 26:4356-4369

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