ACT-R/PM and menu selection: applying a cognitive architecture to HCI

Abstract

Understanding the interaction of a user with a designed device such as a GUI requires clear understanding of three components: the cognitive, perceptual and motor capabilities of the user, the task to be accomplished and the artefact used to accomplish the task. Computational modeling systems which enable serious consideration of all these constraints have only recently begun to emerge. One such system is ACT-R/PM, which is described in detail. ACT-R/PM is a production system architecture that has been augmented with a set of perceptual-motor modules designed to enable the detailed modeling of interactive tasks. Nilsen's (1991) random menu selection task serves two goals: to illustrate the promise of this system and to help further our understanding of the processes underlying menu selection and visual search. Nilsen's original study, two earlier models of the task, and recent eye-tracking data are all considered. Drawing from the best properties of the previous models considered and guided by information from the eye-tracking experiment, a series of new models of random menu selection were constructed using ACT-R/PM. The "nal model provides a zero-parameter "t to the data that does an excellent, though not perfect, job of capturing the data.2001 Academic Press ¹he Psychology of Human}Computer Interaction (Card, Moran & Newell, 1983) is often credited with the creation of the "eld of human}computer interaction and is, at the very least, one of its most central early in#uences. This book introduced the Model Human Processor (MHP) as an engineering model of human performance and goals, operators, methods and selection rules (GOMS) as a method of task analysis. The conceptual basis for GOMS and, at heart, the underlying belief about the best way to #esh out the MHP, is production rule systems. Since that time, the dominant production rule systems have been the ACT family of systems (Anderson, 1983(Anderson, , 1993) and the Soar architecture (Newell, 1990). EPIC (Kieras & Meyer, 1997) is a more recent, but promising and in#uential, entry into this arena. The applicability and success of GOMS and its MHP-inspired extension, CPM-GOMS (see John & Kieras, 1996, for a review), has clearly indicated that this is a fruitful approach for desktop-style user interfaces. However, the future of the human}computer interface is not on the desktop. Increasingly, computers with user interfaces are appearing in tasks where they previously have not been present, such as automobile navigation systems. These new applications provide signi"cant challenges, both practical and theoretical, to traditional analyses. These interfaces are increasingly

Models of interactive systems: a case study on programmable user modelling

“…As shown, two of the analyses produce finding that are consistent with the empirical findings, while the third (the efficiency analysis) does not. Such simplified models are inadequate for the detailed predictions and cognitive explanations that are exemplified by the work of Gray (2000), Byrne and Bovair (1997), Byrne (2001) or Kieras, Wood and Meyer (1997); however, they have a role to play in supporting an analyst exploring the behavioural and design consequences of different user knowledge and user strategies, in the style discussed by Fields (2001).…”

When Eye-Tracking Meets Cognitive Modeling: Applications to Cyber Security Systems

“…Differently from GOMS models, low-level cognitive architectures and models such as ACT-R [1,4] and Soar [21,29] can be used to model broader human cognitive processes, e.g., modeling users' performance on multi-modal UIs such as car navigation systems, which represents a challenge for traditional GOMS analysis [6,26]. ACT-R specifies the time parameters of processes such as the shifting of a user's visual attention, so it can be used to model visual search tasks.…”

Prediction of Human Performance Time to Find Objects on Multi-display Monitors using ACT-R Cognitive Architecture

“…이러한 Model-based Evaluation은 ACT-R (Byrne & Anderson., 1997;Byrne, 2001;Anderson et al, 2004), SOAR (Laird et al, 1983), EPIC (Kieras & Meyer, 1997) 과 같은 인지아키텍처(Cognitive Architecture)를 이용하 여 사람의 실제 인지 과정을 묘사해 주는 인지 모델을 작성 하거나 GOMS (Card et al, 1983;John & Kieras, 1996a, 1996b)와 같은 모델을 이용하여 사람의 행동을 정량적으로 예측할 수 있게 해주는 방법이다. 특히, ACT-R은 사람의 행동을 학습 (Anderson & Matessa, 1998), 운전 (Salvucci, 2006) 등과 같은 높은 수준의 인지 과정에서부터 메뉴 선 택 (Min et al, 2010;Lim et al, 2012;St.…”