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Marcello Maniglia, Kristina M Visscher, Aaron R Seitz; Oculomotor strategy classification in simulated central vision loss. Journal of Vision 2019;19(10):145c. doi: https://doi.org/10.1167/19.10.145c.
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Age-related visual diseases, such as Macular degeneration (MD), are a central public health concern, further aggravated by the increasingly aging population. MD patients must overcome loss of central vision by adopting compensatory oculomotor strategies, with a large majority developing an eccentric fixation spot (preferred retinal location, or PRL). Understanding the process of development of the PRL would be extremely useful in understanding individual differences in compensatory strategies that can in turn help develop appropriate interventions. However, numerous factors seem to play a role, making it difficult to investigate this phenomenon over a largely inhomogeneous sample. This is further complicated by the challenges that clinical research has to face, from recruitment and compliance to the diverse etiologies and extent of the visual loss. Several labs have therefore begun to simulate visual diseases in a healthy population, which opens up the possibility of testing the development of compensatory strategies and possible intervention approaches. However, this field is still in its initial stage and few elements are taken into account when looking at eye movement patterns in the context of simulated vision loss. Here we propose a systematic approach to classify eye movements after central vision loss that distinguishes different oculomotor components, namely Saccadic Re-referencing, Saccadic Precision, Fixation Stability, and Latency of Target Acquisition. We tested this model in a group of healthy individuals in which peripheral looking strategies were induced through the use of a gaze contingent display obstructing the central 10 degrees of visual field. Results show that it is possible to observe and characterize different oculomotor behaviors on the basis of the proposed components. This more complete characterization of eye movements will help the field understand individual differences in eye movement strategies and how these interact with gaze contingent training and explain differences in task performance.
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