Frank E. Ritter

Model-based evaluation of expert cell phone menu interaction

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.

Supporting cognitive models as users

Cognitive models are computer programs that simulate human performance of cognitive skills. They have been useful to HCI by predicting task times, by assisting users, and by acting as surrogate users. If cognitive models could interact with the same interfaces that users do, the models would be easier to develop and would be easier to apply as interface testers. This approach can be encapsulated as a cognitive model interface management system (CMIMS), which is analogous to and based on a user interface management system (UIMS). We present five case studies using three different UIMSes. These show how models can interact with interfaces using an interaction mechanism that is designed to apply to all interfaces generated within a UIMS. These interaction mechanisms start to support and constrain performance in the same ways that human performance is supported and constrained by interaction. Most existing UIMSes can and should be extended to create CMIMSes, and models can and should use CMIMSes to look at larger and more complex tasks. CMIMSes will help to further exploit the synergy between the disciplines of cognitive modeling and HCI by supporting cognitive models as users.

Model-based evaluation of cell phone menu interaction

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.

Using a Cognitive Architecture to Examine what Develops

Different theories of development propose alternative mechanisms by which development occurs. Cognitive architectures can be used to examine the influence of each proposed mechanism of development while keeping all other mechanisms constant. An ACT-R computational model that matched adult behavior in solving a 21-block pyramid puzzle was created. The model was modified in three ways that corresponded to mechanisms of development proposed by developmental theories. The results showed that all the modifications (two of capacity and one of strategy choice) could approximate the behavior of 7-year-old children on the task. The strategy-choice modification provided the closest match on the two central measures of task behavior (time taken per layer, r = .99, and construction attempts per layer, r = .73). Modifying cognitive architectures is a fruitful way to compare and test potential developmental mechanisms, and can therefore help in specifying “what develops.”

RUI: recording user input from interfaces under Windows and Mac OS X.

Event and timing logs are useful in studying human—computer interaction, evaluating applications, and comparing input devices. Recording User Input (RUI) is a tool that records user—computer interface behavior. It is created in the .Net framework with C# for Windows and in the Carbon framework for Mac OS X. RUI runs in the background and works with software that runs under Windows or Mac OS X (10.3 Panther and later versions). We illustrate its use with a human—robot interaction interface and present two simple tests that RUI passes and that other timing software should pass: avoiding 0-msec timings and time distributions that follow a gamma (or gamma-like) distribution.

An integrated theory for improved skill acquisition and retention in the three stages of learning

We introduce an integrated theory of learning and forgetting that has implications for training theory and practice. We begin with a review of skill acquisition research that argues that individuals employ different cognitive mechanisms when learning, which can be reliably associated with three stages of learning. This review leads to our proposed skill retention theory, which recommends a method to increase skill retention when designing systems, covering a range of system design issues, from interface design to training. We conclude with a discussion of how we might optimise skill retention based upon this approach. Specifically, we discuss how we might improve training by better spacing the iterations between training sessions to support proceduralisaion to improve skill retention.

Caffeine and stress alter salivary α‐amylase activity in young men

We examined the effects of caffeine and a psychological stressor on salivary α‐amylase (sAA) in healthy young males (age 18–30 years) who consumed caffeine on a daily basis.

Foundations for Designing User-Centered Systems

If designers and developers want to design better technologies that are intended for human use they need to have a good understanding of the people who are or who will be using their systems. Understanding people, their characteristics, capabilities, commonalities, and differences allows designers to create more effective, safer, efficient, and enjoyable systems. This book provides readers with resources for thinking about people—commonly called ‘‘users’’—their tasks and the context in which they perform those tasks. Our intention is to enable you to make more informed decisions when designing complex interactive systems. This chapter thus introduces this argument through example design problems. We then present the benefits and costs associated with understanding the user. Two approaches for understanding users are introduced. The first is a framework called the ABCS for understanding, in broad strokes, different aspects of users. The second is user knowledge and action simulation for developing and testing how users approach tasks in more detail. After reading this chapter you should be able to appreciate why it is important to understand users, and the associated benefits and costs of doing so.

Targeting audiences on the internet

94 April 2001/Vol. 44, No. 4 COMMUNICATIONS OF THE ACM M uch useful research can be conducted with the help of an Internet audience, ranging from psychological experiments to market research surveys [7, 9]. Internet surveys are cheaper and faster to carry out than postal surveys, and their electronic format facilitates analysis. The Internet can also provide anonymity not available elsewhere, which helps reduce or eliminate the social desirability effect, whereby respondents give answers they think the surveyor expects [5]. Computer presentation yields more frank and complete answers to sensitive questions, according to the Computer-Assisted Personal Interviewing literature [12]. Research results via the Internet can be collected around the clock from individuals who may be difficult to contact or unwilling to participate using traditional research methods. The novelty of computer-aided research results in higher respondent interest and higher response rates. We explored how survey respondents may be targeted on the Internet using a survey on mobile phone designs. To attract respondents, we advertised the survey in newsgroups and placed banner ads on several Web pages. The survey was also included in the databases of three Internet search engines. Email may also be used to target respondents. Broad-based, unsolicited spamming is illegal in some U.S. states, but using email to contact a highly selected, interested audience, to survey existing users of a software package for example, has received less criticism. Because we lacked a mailing list of individuals interested in mobile phones, we could not use this approach. Our survey was directed toward three consumer groups: women, UK residents, and existing mobile phone users. Women were chosen as a target since they have traditionally been underrepresented online. UK-based Internet users are another relatively small but important group for our work. Mobile phone users were the specific user group. At the time this survey took place, Internet penetration levels in the UK may have been as low as 2% of households and 5% of businesses [1]. We used 10 versions of the survey, each with a different URL. Six versions were used for the newsgroups, three for the banner ads, and one for the search engines. Individuals were unlikely to see the survey in multiple places; no one responded to multiple surveys. We targeted respondents on several Web sites using a banner-advertised incentive: “WIN a Mobile Phone: Complete a short survey and WIN a highquality digital mobile phone.” Women were targeted on a romance novel Web site called “Your Weekly Kiss.” The ad was on the site’s home page for 14 days, receiving an estimated 800 viewings. Mobile phone users were targeted on a mobile phone Web site called GSMag International. The banner was placed on the site’s home page until it received 5,000

Providing user models direct access to interfaces: an exploratory study of a simple interface with implications for HRI and HCI

Models of users are a way to understand and improve the usability of computer interfaces. We present here a model in ACT-R cognitive-modeling language that interacts with a publicly available driving simulation as a simple analog for robot interfaces. The model interacts with the unmodified Java interface by incorporating a novel use of bitmap parsing. The models structure starts to describe the knowledge a human operator of a robot must have. The model also indicates some of the aspects of the task will be difficult for the operator. For example, the models performance makes quantitative predictions about how robot speed will influence navigation quality, correlating well to human performance. While the model does not cover all aspects of human-robot interaction, it illustrates how providing user models access to an interface through its bitmap can lead to more accurate and more widely applicable model users