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. (shrink)

We present a comprehension‐based computational model of UNIX user skill acquisition and performance in a training context (UNICOM). The work extends a comprehension‐based theory of planning to account for skill acquisition and learning. Individual models of 22 UNIX users were constructed and used to simulate user performance on successive command production problems in a training context. Comparisons of model and the human empirical data result in a high degree of agreement, validating the ability of UNICOM to predict user response to (...) training. (shrink)

People often remember what they attend to in the world. Such memory can be cast as a kind of mental catalog or index of attended objects. To investigate how such an index is acquired and used, protocol data were collected from a programmer who scrolled to off‐screen objects from time to time as she worked. These protocol data were modeled using Soar, which constrains how the index is constructed. In the model, an index entry is an episodic trace encoded during (...) attention. The trace associates the attention event with a time symbol denoting the event's occurrence. Later, the model can ask itself whether it saw that object by calling to mind an image of the attention event. If this image retrieves a time symbol, then the model infers that the object exists and can reason about bringing the object back into view. Episodic indexing is a theory of these encoding and retrieval processes. It posits that information about attention events is encoded automatically, but that retrieval requires effort and knowledge. Episodic indexing is congruent with a range of results on episodic and temporal codes and recognition and recall processes. It incorporates source monitoring (Johnson, Hashtroudi, & Lindsay, 1993) and is a simple and pervasive form of long‐term working memory (Ericsson & Kintsch, 1995). (shrink)