Being able to predict the performance of interface designs using models of human cognition and performance is a long-standing goal of HCI research. This paper presents recent advances in cognitive modeling which permit increasingly realistic and accurate predictions for visual human-computer interaction tasks such as icon search by incorporating an "active vision" approach which emphasizes eye movements to visual features based on the availability of features in relationship to the point of gaze. A high fidelity model of a classic visual search task demonstrates the value of incorporating visual acuity functions into models of visual performance. The features captured by the high-fidelity model are then used to formulate a model simple enough for practical use, which is then implemented in an easy-to-use GLEAN modeling tool. Easy-to-use predictive models for complex visual search are thus feasible and should be further developed.

We present the case study of a complex, mixed-initiative scheduling system to illustrate Work-Centered Design (WCD), a new approach for the design of information systems. WCD is based on theory of distributed cognition and extends established user-centered methods with abstract task modeling, using innovative techniques for work ontology and top-level algorithms to capture the logic of a human-computer interaction paradigm. WCD addresses a long-standing need for more effective methods of function allocation. The illustrating case study succeeded on a large, difficult problem for aircraft scheduling where prior expensive attempts failed. The new system, called Solver, reduces scheduling labor from 9 person-days a week to about 1 person-hour. These results were obtained from the first user test, demonstrating notable effectiveness of WCD. Further, the value of Solver's higher quality schedules is far-reaching. WCD extends HCI methods to fill an important need for technical problem-solving systems.

A formal interaction modeling technique known as Goals, Operators, Methods, and Selection rules (GOMS) is well-established in human-computer interaction as a cost-effective way of evaluating designs without the participation of end users. This paper explores the use of GOMS for evaluating human-robot interaction. We provide a case study in the urban search-and-rescue domain and raise issues for developing GOMS models that have not been previously addressed. Further, we provide rationale for selecting different types of GOMS modeling techniques to help the analyst model human-robot interfaces.

This paper presents the lessons learned when a computational GOMS modeling tool was used to evaluate user interface concepts and team structure designs for a new class of military shipboard workstations. The lessons are both encouraging and cautionary: For example, computational GOMS models scaled well to a large and complex task involving teams of users. Interruptability and working memory constructs had to be added to conventional GOMS model concepts. However, two surprises emerged: First, the non-psychological aspects of the model construction were the practical bottleneck. Second, user testing data in this domain were difficult to collect and lacked definition, meaning that the model provided a better characterization of the design details than the user testing data. Included in these lessons are recommendations for future model applications and modeling methodology development.

Direct manipulation involves a large number of interacting psychological mechanisms that make the performance of a given interface hard to predict on intuitive or informal grounds. This paper applies cognitive modeling to explain the subtle effects produced by using a keypad versus a touchscreen in a performance-critical laboratory task.

This research presents cognitive models of a person selecting an item from a familiar, ordered, pull-down menu. Two different models provide a good fit with human data and thus two different possible explanations for the lowlevel cognitive processes involved in the task. Both models assert that people make an initial eye and hand movement to an anticipated target location without waiting for the menu to appear. The first model asserts that a person knows the exact location of the target item before the menu appears, but the model uses nonstandard Fitts' law coefficients to predict mouse pointing time. The second model asserts that a person would only know the approximate location of the target item, and the model uses Fitts' law coefficients better supported by the literature. This research demonstrates that people can develop considerable knowledge of locations in a visual task environment, and that more work regarding Fitts' law is needed. KEYWORDS Cognitive models, Fitts' law, men...

Article Free Access Share on Cognitive modeling reveals menu search in both random and systematic Authors: Anthony J. Hornof Artificial Intelligence Laboratory, Electrical Engineering & Computer Science Department, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI Artificial Intelligence Laboratory, Electrical Engineering & Computer Science Department, University of Michigan, 1101 Beal Avenue, Ann Arbor, MIView Profile , David E. Kieras Artificial Intelligence Laboratory, Electrical Engineering & Computer Science Department, University of Michigan, 1101 Beal Avenue, Ann Arbor, MI Artificial Intelligence Laboratory, Electrical Engineering & Computer Science Department, University of Michigan, 1101 Beal Avenue, Ann Arbor, MIView Profile Authors Info & Claims CHI '97: Proceedings of the ACM SIGCHI Conference on Human factors in computing systemsMarch 1997 Pages 107–114https://doi.org/10.1145/258549.258621Published:27 March 1997Publication History 50citation946DownloadsMetricsTotal Citations50Total Downloads946Last 12 Months35Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

EPIC (Executive Process-Interactive Control) is a cognitive architecture especially suited for modeling human multimodal and multiple-task performance. The EPIC architecture includes peripheral sensory-motor processors surrounding a production-rule cognitive processor and is being used to construct precise computational models for a variety of human-computer interaction situations. We briefly describe some of these models to demonstrate how EPIC clarifies basic properties of human performance and provides usefully precise accounts of performance speed.

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.

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.

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.

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.

Article A validation of the GOMS model methodology in the development of a specialized, commercial software application Share on Authors: Richard Gong Center for Ergonomics, The University of Michigan, 1205 Beal Ave., Ann Arbor, MI Center for Ergonomics, The University of Michigan, 1205 Beal Ave., Ann Arbor, MIView Profile , David Kieras Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MIView Profile Authors Info & Claims CHI '94: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsApril 1994 Pages 351–357https://doi.org/10.1145/191666.191782Online:24 April 1994Publication History 18citation828DownloadsMetricsTotal Citations18Total Downloads828Last 12 Months18Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Kieras and Polson (1985) proposed an approach for making quantitative predictions on ease of learning and ease of use of a system, based on a production system version of the goals, operators, methods, and selection rules (GOMS) model of Card, Moran, and Newel1 (1983). This article describes the principles for constructing such models and obtaining predictions of learning and execution time. A production rule model for a simulated text editor is described in detail and is compared to experimental data on learning and performance. The model accounted well for both learning and execution time and for the details of the increase in speed with practice. The relationship between the performance model and the Keystroke-Level Model of Card et al. (1983) is discussed. The results provide strong support for the original proposal that production rule models can make quantitative predictions for both ease of learning and ease of use.

This paper investigates whether changes in the user/computer dialogue structure will affect the performance of users who are familiar with an earlier version of the product. Quantitative predictions using the Kieras and Polson (1985) production system model were derived to test whether changing the lexical attributes and structure of a popular menu-driven word-processor would permit transfer of existing knowledge of the word-processor to a new version. The results show that changes to the dialogue structure of the menu-system are not detrimental, while changes to the lexical attributes of the menus will hinder user performance.

This paper describes a successful test of a quantitative model that accounts for large positive transfer effects between similar screen editors, between different line editors and from line editors to a screen editor, and between text and graphic editors. The model is tested in an experiment using two very similar full-screen text-editors differing only in the structure of their editing commands, verb-noun vs noun-verb. Quantitative predictions for training time were derived from a production system model based on the Polson and Kieras (1985) model of text editing.

A model of manuscript editing, implemented as a simulation program, is described in this paper. The model provides an excellent, quantitative description of learning, transfer, and performance data from two experiments on text editing methods. Implications of the underlying theory for the design process are briefly discussed.

A general method for describing the behavior of an interactive system is presented which is based on transition networks generalized enough to describe even very complex systems easily, as shown by an example description of a word processor. The key feature is the ability to easily describe hierarchies of modes or states of the system. The representation system is especially valuable as a design tool when used in a simulation of a proposed user interface.