UC Riverside

There is considerable variability in the observed effects of perceptual learning, and consequently considerable debate about the underlying mechanisms of learning. Resolving these irregularities and debates requires careful experimental control and precise application of research methods. At times, improving consistency between labs and methods is the best solution, but sometimes the best choice is to develop a new approach. Here we describe first a traditional perceptual learning experiment aimed at understanding individual differences. Then, when it was discovered that important data- analytic assumptions were violated by that experiment, we describe the development of novel data analytic techniques to help understand the results. Finally, we discuss the development of an entirely new paradigm to help understand aspects of perceptual learning that are at present difficult to measure reliably.