OPTIMAL STATIC AND DYNAMIC TRAINING SCHEDULES - STATE MODELS OF SKILLACQUISITION

Training takes place in complex environments. Typically, there are man y different tasks which need to be learned; each task can be performed at one of several different levels of proficiency and each level of p roficiency within a given task can be trained in one of various differ ent ways. Much is known about what tasks need to be trained in order t o achieve a particular objective, what methods are best for training a particular level of a particular task, and what measures should be us ed to evaluate training. Curiously, given that the tasks, methods, and measures have been selected, very little is known about how to determ ine which level of which task it is best to train in each session. In this article, a framework for pursuing the optimization of training sc hedules which are sensitive to (dynamic) and not sensitive to (static) the session by session (trial by trial) progress of the learner is de veloped. The framework takes as its starting point the early state mod els of learning first proposed within mathematical psychology in the 1 950s. We show that the stale models can be used to predict how perform ance will vary as a function of the scheduling of training trials. Pra ctically, it is important to consider the effect of changes in the sch eduling of training trials because such changes can substantially redu ce the time it takes any given individual to learn a composite skill. Theoretically, it is important to consider the effect of changes in th e scheduling of training trials because such changes can potentially p rovide the answers to a number of questions central to research in tra ining. We conclude that the state models of learning provide both of t he hoped for practical and theoretical benefits. (C) 1996 Academic Pre ss, Inc.