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The learning curve is a fundamental empirical measure of perceptual learning. Typically constructed from contrast or difference thresholds estimated from blocks or sessions of hundreds of trials, it may gloss over short-term performance changes such as initial rapid learning or overnight performance improvements. Here, we developed a two-layer hierarchical Bayesian model (HBM) to compute the joint posterior distribution of the learning curves at the population and individual subject levels and a Bayesian inference procedure (BIP) to estimate the posterior distributions of the learning curve for each subject independently. In the HBM, hyperparameters of the contrast thresholds at the population level defined the means of the distributions of the contrast thresholds at the subject level. We applied both procedures to data from two studies that investigated the interaction of feedback and training accuracy in Gabor …