ABSTRACT

The training principles presented in preceding chapters (especially chapters 2 and 3) come primarily from controlled laboratory experiments. An important question, then, is how well these principles generalize to real-world job training. One such training principle with clear relevance to the real world is the Procedural Reinstatement Principle (Healy, Wohldmann, & Bourne, 2005). Specifically, in simple laboratory experiments, people perform best when the testing conditions reinstate those encountered during training. In real-world terms, this principle implies that training conditions should reinstate the real world—the actual conditions under which people will perform these trained tasks. This principle has been the primary driver behind large investments in high-fidelity simulators used for training in the military, aviation, space, medicine, and nuclear power generation industries. Given the cost of training, and more importantly, the potential cost of errors in such high-risk operations, it is clear that training should be as effective and as efficient as possible. However, before training that reinstates the “real world” can be designed, the real world must be understood.