Abstract not found

Cell phone interfaces are now ubiquitous. In this paper, we describe concepts to support the analysis of cell phone menu hierarchies. We present an empirical study of user performance on five simple tasks of menu traversal on a cell phone. Two models we tested, based on GOMS and ACT-R, give very good predictions of behavior. We use the study results to motivate an effective evaluation process for menu hierarchies. Our work makes several contributions: a novel and timely study of a new, very common HCI task; new models for accurately predicting performance; novel development tools to support such modeling; and a search procedure to generate menu hierarchies that reduce traversal time, in simulation studies, by about a third.

We describe concepts to support the analysis of cell phone menu hierarchies, based on cognitive models of users and easy-to-use optimization techniques. We present an empirical study of user performance on five simple tasks of menu traversal on an example cell phone. Two of the models applied to these tasks, based on GOMS and ACT-R, give good predictions of behavior. We use the empirically supported models to create an effective evaluation and improvement process for menu hierarchies. Our work makes three main contributions: a novel and timely study of a new, very common HCI task; new versions of existing models for accurately predicting performance; and a search procedure to generate menu hierarchies that reduce traversal time, in simulation studies, by about a third.

This article is concerned with systems that accommodate and exploit the properties of the graphical user interface as an environment. Many of these systems are interface agents, in the sense defined by Maes (Shneiderman and Maes 1997, p. 53): "An [interface] agent basically interacts with the application just like you interact with the application. " Although nowadays the term "interface agent" encompasses all types of agents that interact with users, even those that depend not at all on the characteristics of the interface medium, we will focus on a more specialized interpretation of the term, to mean agents that interact with the objects in a direct manipulation graphical user interface, or, in other words, softbots whose sensors and effectors are the input and output capabilities of the interface (Lieberman 1998). Other efforts we describe, not necessarily related to agents, operate within the same constraints, treating the user interface as an environment rather than only a communication medium.

Human error in computer systems has been blamed for many military and civilian catastrophes resulting in mission failure and loss of money and lives. However, the root cause of such failures often lies in the system’s design. A central theme in designing for human-error tolerance is to build a multi-layered defense. Creating such a robust system requires that designers effectively manage several aspects of erroneous system usage: prevention, reduction, detection, identification, recovery, and mitigation. These also correspond to discrete stages before and after error occurrence where different defensive measures can be taken. Human error models can be used to better understand these stages, the underlying cognitive mechanisms responsible for errors, and ultimately how to design systems and training to reduce the effects of inherent human limitations. This paper presents a general framework for human error recovery based on five key stages of erroneous performance: the commission of an error, its detection, identification, and correction, and resumption of the original task. These stages constitute the main components of a state model that characterizes human performance, and allows designers and trainers comprehensively address the most important aspects of error-tolerant design. Furthermore, these performance stages can be modeled computationally, to varying degrees, using standard information processing architectures. This work also demonstrates the effectiveness of a technique using GOMS models to design systems to

This article describes a general-purpose programmable substrate designed to allow a cognitive modeling system to interact with off-the-shelf interactive applications. The substrate improves on conventional approaches, in which a model interacts with a hand-constructed abstraction, an artificial simulation, or an interface tailored specifically to the modeling system. The substrate constitutes a coarse computational model of vision, from low-level region segmentation to high-level object recognition. Our approach will allow researchers to extend their cognitive modeling work to a variety of user interface domains much more easily than is currently possible, and to test the ecological validity of their findings directly on real-world applications. 1 Introduction Close, beneficial ties have traditionally held between research in cognitive modeling and human-computer interaction (HCI). Cognitive modeling research drove some of the earliest rigorous examinations of user behavior, p...

We describe a system, G2A, that produces ACT-R models from GOMS models containing hierarchical methods, visual and memory stores, and control constructs. Because GOMS is a more abstract formalism than ACT-R, a single GOMS operator might be plausibly translated in different ways into ACT-R productions (e.g., a GOMS Look-for operator might be carried out by different visual search strategies in ACT-R). Given a GOMS model, G2A generates and evaluates alternative ACT-R models by systematically varying the mapping of GOMS operators to ACT-R productions. In experiments with a text editing task, G2A produces ACT-R models with predictions that are within 5 % of GOMS model predictions. In the same domain, G2A also generates ACT-R models that give good predictions of overall task duration for actual users (within 2 % error), though the models are much less accurate at a detailed level.

Cognitive modeling techniques provide a way of evaluating user interface designs, based on what is known about human cognitive strengths and limitations. Cognitive modelers face a tradeoff, however: more detailed models require disproportionately more time and effort to develop than coarser models. In this paper we describe a system, G2A, GOMS models into more detailed ACT-R models. G2A demonstrates how even simple AI techniques can facilitate the construction of cognitive models and suggests new directions for improving modeling tools.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 2006, ACM. Vol. 2, Issue 4, August 2007, pp. 190-210 This paper describes work on developing usable interfaces for creating and editing methods for highthroughput screening of chemical and biological compounds in the domain of life sciences automation. A modified approach to metaphor-based interface design was used as a framework for developing a screening method editor prototype analogous to the presentation of a recipe in a cookbook. The prototype was compared to an existing screening method editor application in terms of effectiveness, efficiency, and satisfaction of novice users and was found to be superior. Keywords Metaphor-based design, cognitive task analysis, usability evaluation, life sciences automation

The great variety of new (Post-WIMP) interaction styles make them difficult to evaluate and compare. We propose a new evaluation method for them, Knowledge-Based Usability Evaluation (KBUE), that is based on similar ideas to those that drive cognitive architectures, such as ACT-R and Soar. We present KBUE as a way to formally specify the knowledge in the environment and in the user’s head, and how this specification can be used to examine whether the aforementioned set of knowledge covers the required knowledge for the performance of a task in a user interface. We believe that by using this specification, it becomes easier to evaluate and compare Reality-Based interfaces. Author Keywords GOMS; RBI; reality-based interaction; interface evaluation;