Coordinated Attentional Training promotes generalization of learning in healthy and MD subjects

Perceptual Learning (PL), experience-induced improvement in perceptual tasks, has shown potential to enhance perceptual abilities in healthy individuals and may provide foundation for rehabilitative protocols in clinical populations. However, PL is often specific to trained features (i.e., type of stimulus, retinal position, eye used during the training etc.). Such specificity is an obstacle to translating PL into effective rehabilitative tools. Here we examine PL in the context of Macular Degeneration (MD), a pathology affecting the retina, which is the leading cause of vision loss in western countries. MD patients must rely on peripheral vision, and do so with mixed success. In the best cases, patients learn to use their PRLs for reading and recognizing faces, however many others fail to develop effective PRLs. The factors driving these differences are not well understood. Current rehabilitation techniques, focusing on low-level improvements or oculomotor trainings are often exploratory and there is no standard practice, creating a critical need for evidence-based rehabilitation strategies. Here we present data comparing two training paradigms: a standard perceptual learning paradigm (SPL) and a Coordinated Attentional Training (CAT) that explicitly targets both low-level, perceptual components and cognitive control networks by combining a visual search task, shifts in attention, and multisensory stimulation with standard PL. We hypothesized that CAT would lead to greater learning and generalization in both healthy participants and patients with macular degeneration than standard methods. Initial results show that CAT induced a significant improvement in VA in both healthy and MD participants that trended to be larger than the non-significant change in VA after by SPL. This work suggests that incorporating attention manipulations to standard perceptual learning approaches may improve outcomes. Meeting abstract presented at VSS 2018