Systematic errors In performance are an important aspect of human behavior that have not received adequate explanation. One such systematic error is termed postcompletion error; a typical example is leaving one's card In the automatic teller after withdrawing cash. This type of error seems to occur when people have an extra step to perform in a procedure after the main goal has been satisfied. The fact that people frequently make this type of error, but do not make this error every time, may best be explained by considering the working memory load at the time the step is to be performed: The error is made when the load on working memory is high, but will not be made when the load is low. A model of performance In the task was constructed using Just and Carpenter's (1992) CAPS that predicted that high working memory load should be associated with postcompletion errors. Two experiments confirmed that such errors can be produced in a laboratory as well as a naturalistic setting, and that the conditions under which the CAPS model makes the error are consistent with the conditions under which the errors occur in the laboratory.

This report presents three studies concerned with learning how to operate a simple control panel device, and how this learning is affected by understanding a device model that describes the internal mechanism of the device. The first experiment compared two groups, one of which learned a set of operating procedures for the device by rote, and the other learned the device model before receiving the identical procedure training. The model group learned the procedures faster, retained them more accurately, executed them faster, and simplified inefficient procedures far more often, than the rote group. The second study demonstrated that the model group is able to infer the procedures much more easily than the rote group, which would lead to more rapid learning and better recall performance. The third study showed that the important content of the device model was the specific configuration of components and controls, and not the motivational aspects, component descriptions, or general principles. This specific information is what is logically required to infer the procedures. Thus, the benefits of having a device model depend on whether it supports direct and simple inference of the exact steps required to operate the device.

No abstract

No abstract

No abstract