Adaptive personalization, where the system adapts the interface to a user’s needs, has the potential for significant performance benefits on small screen devices. However, research on adaptive interfaces has almost exclusively focused on desktop displays. To explore how well previous findings generalize to small screen devices, we conducted a study with 36 subjects to compare adaptive interfaces for small and desktop-sized screens. Results show that high accuracy adaptive menus have an even larger positive impact on performance and satisfaction when screen real estate is constrained. The drawback of the high accuracy menus, however, is that they reduce the user’s awareness of the full set of items in the interface, potentially making it more difficult for users to learn about new features. Author Keywords Adaptive interfaces, personalization, small screen devices, menu design, user study, interaction techniques. ACM Classification Keywords H.5.2 [User Interfaces]: Evaluation/methodology, interaction styles.

As driver distraction from in-vehicle devices becomes an increasingly critical issue, researchers have aimed to establish better scientific understanding of distraction along with better engineering tools to build less distracting devices. This paper presents a new system, Distract-R, that allows designers to rapidly prototype and evaluate new in-vehicle interfaces. The core engine of the system relies on a rigorous cognitive model of driver behavior which, when integrated with models of task behavior on the prototyped interfaces, generate predictions of driver performance and distraction. Distract-R allows a designer to prototype basic interfaces, demonstrate possible tasks on these interfaces, specify relevant driver characteristics and driving scenarios, and finally simulate, visualize, and analyze the resulting behavior as generated by the cognitive model. The paper includes three modeling studies that demonstrate the system’s ability to account for various aspects of driver performance for several types of in-vehicle interfaces. More generally, Distract-R illustrates how cognitive models can be used as internal simulation engines for design tools intended for non-modelers, with the ultimate goal of helping to understand and predict user behavior in multitasking environments. Categories and Subject Descriptors: H.1.2 [Models and Principles] User/Machine Systems – Human factors;

In this paper, we describe a high-level behavior representation language (Herbal) and report new work regarding Herbal’s ACT-R compiler. This work suggests that Herbal reduces model development time by a factor of 10 when compared to working directly in Soar, ACT-R, or Jess. We then introduce a large ACT-R model (541 rules) that we generated in approximately 8 hours. We fit the model to learning data. The comparison indicates that humans performing spreadsheet tasks appeared to start with some expertise. The comparison also suggests that ACT-R, when processing tasks consisting of hundreds of unique memory elements over times spans of twenty to forty minutes, may have problems accurately representing the learning rates of humans. In addition, our study indicates that the spacing between learning sessions has significant effects that may impact the modeling of memory decay in ACT-R.

Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces, and also simulating the interaction with these interfaces. The action of predicting is based on a task analysis which analyses what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the functionalities of the system to be modeled. Cognitive models are part of the analytical approaches that do not make necessarily appeal to the user during the interface development process. This paper presents a study about the evaluation of a human machine interaction (HMI) with an interface of a contextual assistant, using ACT-R and GOMS cognitive models. It shows how these techniques may be applied in HMI evaluation, design and research, emphasizing on the task analysis in one side, and on the time execution of tasks in the other side. In order to validate and support our results, an experimental study of user performance, during the interaction with the contextual assistant interface is conducted at the DOMUS laboratory. The results of our models show that both models GOMS and ACT-R give good to very good predictions of user performance at the task level as well as the object level, our results are very close to those obtained in the experimental study. Keywords—HMI, interface evaluation, cognitive modeling, user modeling, user performance. I.

No work on mobile text messaging so far has taken into account the effect of learning on the change in visual exploration behavior as users progress from non-expert to expert level. We discuss within the domain of multi-tap texting on mobile phone and address the process of searching versus selecting a letter on the keypad interface. We develop a simulation model that forecasts the probability of letter location recall by non-expert users and thereby models learning, as the user acquires expertise in recalling, with practice, session after session. We then plugin this probability within a model of visual strategy that combines the effect of different ways visual exploration: non-expert users search for a letter while expert users select a letter. The observed non-expert non-motor time preceding a key press (for a letter) correlates extremely well with the simulation results.

Personalized graphical user interfaces have the potential to reduce visual complexity and improve efficiency by modifying the interface to better suit an individual user’s needs. Working in a personalized interface can make users faster, more accurate and more satisfied; in practice, however, personalization also comes with costs, such as a reliance on user effort to control the personalization, or the introduction of spatial instability when interface items are reorganized automatically. We conducted a series of studies to examine both the costs and benefits of personalization, and to identify techniques and contexts that would be the most likely to provide an overall benefit. We first interviewed long-term users of a software application that provides adaptable (usercontrolled) personalization. A design trade-off that emerged is that while personalization can increase the accessibility of features useful to a user’s current task, it may in turn negatively impact the user’s awareness of the full set of available features. To assess this potential trade-off, we introduced awareness as an evaluation metric to be used alongside more standard performance measures and we ran a series of three studies to understand how awareness relates to core task performance. These studies used two different measures to assess awareness, showing that personalization can impact both the recognition rate of unused features in the interface and user performance on new

We reviewed the service structure, needs analysis, user interface model, and interaction analysis for television (TV) and mobile phones. Due to the increasing use of services such as electronic program guide (EPG), digital video recorder (DVR), and pay-per-view (PPV), we concluded that text input for the TV interface will be inevitable. Furthermore, jumping interaction will remain as the main interaction for TV. Based on the successes and failures of various interaction technologies for mobile phones, we present a prediction of new input paradigms for the TV interface. Finally, cooperative design by TV manufacturers and service operators will be significant for the success of advanced interactive TV services.

Abstract—Cognitive models allow predicting some aspects of utility and usability of human machine interfaces, and also simulating the interaction with these interfaces. The action of predicting is based on a task analysis which analyses what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the functionalities of the system to be modeled. Cognitive models are part of the analytical approaches that do not make necessarily appeal to the user during the interface development process. This paper presents a study about the evaluation of a human machine interaction (HMI) with an interface of a contextual assistant, using ACT-R and GOMS cognitive models. It shows how these techniques may be applied in HMI evaluation, design and research, emphasizing on the task analysis in one side, and on the time execution of tasks in the other side. In order to validate and support our results, an experimental study of user performance, during the interaction with the contextual assistant interface is conducted at the DOMUS laboratory. The results of our models show that both models GOMS and ACT-R give good to very good predictions of user performance at the task level as well as the object level, our results are very close to those obtained in the experimental study. Keywords—HMI, interface evaluation, cognitive modeling, user modeling, user performance. I.