Abstract
Visual Perceptual Learning (VPL), improvements in visual tasks through experience, is an important tool for examining plasticity, and as treatment for diseases such as low vision. However, classic VPL presents some drawbacks, such as the high specificity (limited transfer of learning to beyond the training conditions) and the large number of training sessions needed to observe significant improvements. We suggest that key to overcoming these drawbacks is to implement training paradigms that are based on understanding of relevant models of neural processing and brain circuitry. A relevant case example where this approach can be informative and useful is Macular Degeneration (MD), where following central vision loss, patients must learn to use peripheral vision for everyday tasks that require fine-scale vision, such as reading, writing, and recognizing faces (Kwon, Nandy, Tjan, 2013). In MD, effective plasticity should entail not just optimization of low-level visual processes, as is the typical focus of VPL, but also higher-level vision and changes in top-down control of visual processing. A wide literature shows that top-down, attentional control in visual processing supports goal-directed behavior and involves interactions among fronto-parietal networks and early visual areas. Here, we focus on 2 well-studied networks. The "fronto-parietal" (FP) network, that includes the dorsolateral prefrontal cortex (dlPFC) and is involved in the moment-to-moment modulation of visual processing, and the 'cingulo-opercular' (CO) network that includes the dorsal anterior cingulate dACC and acts in part to maintain sustained attention. We present a novel coordinated attention training (CAT) designed to specifically target plasticity in how the FP and CO networks interact with visual cortex. We discuss how CAT training may produce more appropriate and generalizable learning than standard VPL frameworks that employ the same stimuli but with predictable central locations and timing and how this approach may be valid to other populations.
Meeting abstract presented at VSS 2017