Several simple mathematical learning models have been proposed that combine aspects of the simple basic all-or-none model and linear operator learning models for two response alternatives. Three of these hybrid models are the insight model, the two-phase model, and the random-trial incremental model. Each of these models formalizes different assumptions about the learning process, but all three nest the two basic models. All of these models have been designed to predict error-success sequences terminating in a criterion run of successes in a simple learning task with feedback. Most statistical inference for these models has been based on the assumption that all error-success sequences are probabilistically determined by the same set of parameters. Although this assumption excludes individual differences in learning ability, great individual differences in performance are predicted. In the presence of individual differences in learning ability, e.g., in a mixture model where each mixture component consists of one of the models with its own set of parameter values, the flexibility of the models is enhanced even further. In this study, the basic models, the hybrid models and the individual difference models were compared using both analytical and simulation methodologies. In the cross-fitting simulation study, trade-off between parameter values, model parameter recovery, and the performance of several indices for model selection were examined under different sample size and parameter value conditions. |