This proposal addresses fundamental issue of specificity and generality of training in the context of Perceptual Learning (PL). PL broadly encompasses the set of mechanisms through which experience with the environment gives rise to changes in perceptual processing. The potential broader impacts of PL are immense. Careful research in this domain can greatly enhance our basic understanding of the perceptual systems and the plasticity of these systems. Furthermore, translational approaches underpinned by the basic science of PL are becoming increasingly prominent. This includes a host of emerging translational approaches for the rehabilitation of both perceptual deficits and for cognitive training, which are believed to share cortical plasticity mechanisms. However, while existing research provides evidence that PL approaches can improve perceptual skills, our ability to develop effective interventions is limited by a lack of understanding of the behavioral outcomes associated with different PL approaches. Here we suggest that to understand and maximally exploit PL, it is necessary to know how training with different tasks and in different individuals gives rise to different outcomes. One major obstacle to successful translation of PL is that the field to-date has been strongly driven by ?novel? and ?provocative? findings demonstrated via small N studies with very few projects digging deep to achieve robust and reliable results. In turn, not surprisingly, the field of PL, like many others in psychology, has suffered from numerous replication challenges. Furthermore, perhaps because following in direct footsteps runs counter to the tendencies noted above, it is surprisingly rare for different research groups to use identical training tasks or outcome tests. This is problematic given research showing that small changes in task- procedures can give rise to large differences in learning outcome. Here we overcome these limitations by comparing a large number of different training tasks using common outcome measures and in a large subject population. Each of these tasks involves a different ?critical feature? for learning proposed by a given research group. However, these tasks have never been directly compared or contrasted. The outcome of the proposed research will be of tremendous value to both basic understanding of PL as well as how to translate PL to help those with visual needs. We will achieve robust and reliable results by training a large sample of participants on PL tasks and assess the outcomes via a common set of measures. We will also collect a broad assessment of individual differences, which will provide a unique dataset that can resolve controversies in the literature and lead to new understandings. Our proposed analytical approach tests central key hypothesis in the field, explores the extent to which different training approaches leads to systematically different profiles of learning, and examines how these can differ based upon the individuals being trained. Further by releasing our training and testing tools as well as the data collected, we will enable other groups to model results, replicate our studies, and make well specified modifications of training tasks with known outcomes to guide future research.
The proposed research is relevant to public health, and in particular, the mission of the NEI to support research on visual disorders, mechanisms of visual function and preservation of sight, because a greater understanding of mechanisms related to perceptual learning can inform new therapies for treating low vision, which in turn has the potential to benefit millions of individuals suffering from low vision. Declines in vision are particularly common in older adults and thus increasing our understanding of how to create effective means of improving vision is also highly relevant to the mission of the NIA to support research on aging and the health and well-being of older people. In addition to strongly aligning with the missions of the NEI and NIA, the proposed re- search cuts across the bounds of numerous other NIH agencies, including the NCI, NIMH, NIAAA, NINCD, NIDA, NINDS, in that all of these agencies work with populations who could gain direct benefits from successful approaches to utilizing behavioral methods to enhance human well-being and performance.
