Engineering models of human performance permit some aspects of usability of interface designs to be predicted from an analysis of the task, and thus they can replace to some extent expensive user-testing data. We successfully predicted human performance in telephone operator tasks with engineering models constructed in the EPIC ( E xecutive P rocess- I nteractive C ontrol) architecture for human information processing, which is especially suited for modeling multimodal, complex tasks, and has demonstrated success in other task domains. Several models were constructed on an a priori basis to represent different hypotheses about how operators coordinate their activities to produce rapid task performance. The models predicted the total time with useful accuracy and clarified some important properties of the task. The best model was based directly on the GOMS analysis of the task and made simple assumptions about the operator's task strategy, suggesting that EPIC models are a feasible approach to predicting performance in multimodal high-performance tasks.

Engineering models of human performance permit some aspects of usability of interface designs to be predicted from an analysis of the task, and thus can replace to some extent expensive user testing data. 5/22/13 4:56 PM Predictive Engineering Models Using the EPIC Architecture for a High-Performance Task Page 2 of 10 http://www.sigchi.org/chi95/proceedings/papers/dek_bdy.htm Human performance in telephone operator tasks was successfully predicted using engineering models constructed in the EPIC (Executive Process-Interactive Control) architecture for human informationprocessing, which is especially suited for modeling multimodal, complex tasks. Several models were constructed on an a priori basis to represent different hypotheses about how users coordinate their activities to produce rapid task performance. All of the models predicted the total task time with useful accuracy, and clarified some important properties of the task.

Engineering models of human performance permit some aspects of usability of interface designs to be predicted from an analysis of the task, and thus can replace to some extent expensive user testing data.The best developed such tools are GOMS models, which have been shown to be accurate and effective in predicting usability of the procedural aspects of interface designs.This paper describes a computer-based tool, GLEAN, that generates quantitative predictions from a supplied GOMS model and a set of benchmark tasks.GLEAN is demonstrated to reproduce the results of a case study of GOMS model application with considerable time savings over both manual modeling as well as empirical testing.

One method for user interface analysis that has proven successful is formal interface analysis, such as GOMSbased analysis. Such methods are often criticized for being difficult to learn, or at the very least an additional burden for the system designer. However, if the process of constructing and using formal models could be automated as part of the interface design environment, such models could be of even greater value. This paper describes an early version of such a system, called USAGE (the UIDE System for semi-Automated GOMS Evaluation). Given the application model necessary to drive the UIDE system, USAGE generates an NGOMSL model of the interface which can be “run ” on a typical set of user tasks and provide execution and learning time estimates.