Hybrid Learning on the NRL Navigation Task; Fielding a New Hybrid Model of Human Learning

摘要

The central question addressed by the project is how humans learn complex visualmotor tasks. Can we construct a model of human learning of such tasks based purely on visualmotor performance data. We answer this question in the affirmative. From a large sequential corpus of visualmotor data gathered from human subjects during learning, We track the evolution of control policies as subjects make the transition from being novices to becoming task experts. The visualmotor data is non-stationary; it is characterized by periods of slow evolution punctuated by conceptual shifts in which policies, change radically. We have developed algorithms that build and track models of control policies across these conceptual shifts. These models are rich enough to capture individual differences in the task, and are simple enough to learn in real-time. That is, we have developed methods for learning objective models of cognitive activity (instead of relying on objective verbal reconstructions) by observing the time course of visualmotor performance. These models can be used to shape and speed up the training of human subjects on complex visualmotor tasks with significant strategic components.