Learning where to look: location learning in graphical user interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SHARK2: a large vocabulary shorthand writing system for pen-based computers
Proceedings of the 17th annual ACM symposium on User interface software and technology
Modeling human performance of pen stroke gestures
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hard lessons: effort-inducing interfaces benefit spatial learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Deconstructing and reconstructing ACT-R: Exploring the architectural space
Cognitive Systems Research
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Stable structured layouts of buttons are a primary means of control for input in current graphical user interfaces. Such layouts are ubiquitous---from tiny iPhone screens to large kiosk screens in the malls---they are found everywhere. Yet, there has been relatively little theoretical account that compares the impact of cognitive effort on learning such stable layouts. In this paper, we demonstrate that prior empirical results on cognitive effort in learning stable layouts are theoretically predictable through the memory activation model of a cognitive architecture, ACT-R. We go beyond previous work by quantitatively comparing the level of cognitive effort in terms of a newly introduced parameter in the declarative memory model of ACT-R. We theoretically compare the cognitive effort of two different layouts of graphical buttons with respect to their label representativeness in the domains of traditional keyboard and ShapeWriter.