Traditionally, the power law has been used to describe the trajectory of skill acquisition. Recent research has challenged this ``law,'' suggesting other models may better capture individual-level data. Furthermore, the motor learning and recovery literature suggests dynamical models might better capture non-monotonic behavior or the effect of feedback. This study compares the fits of six models on data from two mirror tracing experiments with different levels of haptic feedback. This includes two power models, the exponential model, a hybrid power and exponential model, and two dynamical models. This research replicated previous findings that the exponential model is better than the power models for individual-level data. The APEX and two dynamical models showed some advantage, but a fit metric penalizing extra parameters (BIC) called the extent of these advantages into question. These results are important as the models with better fits may better represent the cognitive processes involved in skill acquisition.