TL;DR: The GOMS Model of Manuscript Editing as mentioned in this paper has been used in many applications, e.g., for text selection and text editing in computer science, and for circuit design.
Abstract: Contents: Preface. An Applied Information-Processing Psychology. Part I: Science Base. The Human Information-Processor. Part II: Text-Editing. System and User Variability. An Exercise in Task Analysis. The GOMS Model of Manuscript Editing. Extensions of the GOMS Analysis. Models of Devices for Text Selection. Part III: Engineering Models. The Keystroke-Level Model. The Unit-Task Level of Analysis. Part IV: Extensions and Generalizations. An Exploration into Circuit Design. Cognitive Skill. Applying Psychology to Design Reprise.
TL;DR: This article compares and contrasts four popular variantsof the GOMS family (the Keystroke-Level Model, the original GomS formulation, NGOMSL, and CPM-GOMS) by applying them to a single task example.
Abstract: Sine the publication of The Psychology of Human-Computer Interaction, the GOMS model has been one of the most widely known theoretical concepts in HCI. This concept has produced severval GOMS analysis techniques that differ in appearance and form, underlying architectural assumptions, and predictive power. This article compares and contrasts four popular variantsof the GOMS family (the Keystroke-Level Model, the original GOMS formulation, NGOMSL, and CPM-GOMS) by applying them to a single task example.
TL;DR: The GOMS model has been one of the few widely known theoretical concepts in human-computer interaction as discussed by the authors, and has been used in real-world design and evaluation situations.
Abstract: Since the seminal book, The Psychology of Human-Computer Interaction, the GOMS model has been one of the few widely known theoretical concepts in human-computer interaction. This concept has spawned much research to verify and extend the original work and has been used in real-world design and evaluation situations. This article synthesizes the previous work on GOMS to provide an integrated view of GOMS models and how they can be used in design. We briefly describe the major variants of GOMS that have matured sufficiently to be used in actual design. We then provide guidance to practitioners about which GOMS variant to use for different design situations. Finally, we present examples of the application of GOMS to practical design problems and then summarize the lessons learned.
TL;DR: The process and results of model building as well as the design and outcome of the field trial are discussed and the accuracy of GOMS predictions are assessed and the mechanisms of the models are used to explain the empirical results.
Abstract: Project Ernestine served a pragmatic as well as a scientific goal: to compare the worktimes of telephone company toll and assistance operators on two different workstations and to validate a GOMS analysis for predicting and explaining real-world performance. Contrary to expectations, GOMS predicted and the data confirmed that performance with the proposed workstation was slower than with the current one. Pragmatically, this increase in performance time translates into a cost of almost $2 million a year to NYNEX. Scientifically, the GOMS models predicted performance with exceptional accuracy.
The empirical data provided us with three interesting results: proof that the new workstation was slower than the old one, evidence that this difference was not constant but varied with call category, and (in a trial that spanned 4 months and collected data on 72,450 phone calls) proof that performance on the new workstation stabilized after the first month. The GOMS models predicted the first two results and explained all three.
In this article, we discuss the process and results of model building as well as the design and outcome of the field trial. We assess the accuracy of GOMS predictions and use the mechanisms of the models to explain the empirical results. Last, we demonstrate how the GOMS models can be used to guide the design of a new workstation and evaluate design decisions before they are implemented.
TL;DR: A GOMS analysis was used to predict the behavior of an expert in a graphic, machine-paced, highly interactive task and concludes that GOMs is capable of predicting expert behavior in a broader range of tasks than previously demonstrated.
Abstract: A GOMS analysis was used to predict the behavior of an expert in a graphic, machine-paced, highly interactive task. The analysis was implemented in a computational model using the Soar cognitive architecture. Using only the information available in an instruction booklet and some simple heuristics for selecting between operators, the functional-level behavior of the expert proved to be virtually dictated by the objects visible on the display. At the keystroke-level, the analysis predicted about 60% of the behavior, in keeping with similar results in previous GOMS research. We conclude that GOMS is capable of predicting expert behavior in a broader range of tasks than previously demonstrated.