LEARNING CONSISTENT, INTERACTIVE, AND MEANINGFUL TASK-ACTION MAPPINGS- A COMPUTATIONAL MODEL

Within the field of human-computer interaction, the study of the inter action between people and computers has revealed many phenomena. For e xample, highly interactive devices, such as the Apple Macintosh, are o ften easier to learn and use than keyboard-based devices such as Unix. Similarly, consistent interfaces are easier to learn and use than inc onsistent ones. This article describes an integrated cognitive model d esigned to exhibit a range of these phenomena while learning task-acti on mappings: action sequences for achieving simple goals, such as open ing a file in a word processor. The model, called TAL, is of a user wh o is familiar with the basic operations of a keyboard and mouse, but u nfamiliar with the particular menu structures, words, and actions requ ired to use the device. The model is constructed in Soar and employs a single set of architectural mechanisms. It exhibits behavior that cap tures human preference for consistent, interactive, and meaningful tas k-action mappings.