Project Summary/Abstract: Research in perceptual learning has demonstrated a remarkable ability of training or practice to enhance perception in the adult human. The last thirty years have yielded many important findings about how people learn, what limits transfer, how generalization can be improved, how to model learning, and the nature of visual plasticity. At the same time, learning and transfer have been measured at a relatively coarse scale that leads to relatively inaccurate measures of learning in individuals, which could be very important to choosing adapted training options. Related issues of estimation have also limited the types of training protocols that have been studied. The objective of this research is to use innovative new adaptive performance assessment (based on Bayesian principles) to provide unbiased and high precision estimates of learning in individuals. We also use computational neural network models to generate predictions about more complicated training regimens that are then tested experimentally. We develop a framework for searching among these predictions computationally to identify better (optimized) training methods. The long-term goal is to develop efficient new assessments of learning and transfer and the modeling techniques that may then be applied to improve clinical applications, rehabilitation, and perceptual expertise identified as key aspects of the NEI mission.