Learning Rapid and Precise Skills

A theory is presented about how instruction and experience combine to produce human fluency in a complex skill. The theory depends critically on 4 aspects of the ACT-R architecture. The first is the timing of various modules, particularly motor timing, which results in behavior that closely matches human behavior. The second is the ability to interpret declarative representations of instruction so that they lead to action. The third aspect concerns how practice converts this declarative knowledge into a procedural form so that appropriate actions can be quickly executed. The fourth component, newly added to the architecture, is a Controller module that learns the setting of control variables for actions. The overall theory is implemented in a computational model that is capable of simulating human learning. Its predictions are confirmed in a first experiment involving 2 games derived from the experimental video game Space Fortress. The second experiment tests predictions from the Controller module about lack of transfer between video games. Across the 2 experiments a single model, with the same parameter settings, is shown to simulate human learning of 3 video games.