Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
Mental models in human-computer interaction: research issues about what the user of software knows
Mental models in human-computer interaction: research issues about what the user of software knows
Split menus: effectively using selection frequency to organize menus
ACM Transactions on Computer-Human Interaction (TOCHI)
Cognitive modeling reveals menu search in both random and systematic
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
SUPPLE: automatically generating user interfaces
Proceedings of the 9th international conference on Intelligent user interfaces
Who's asking for help?: a Bayesian approach to intelligent assistance
Proceedings of the 11th international conference on Intelligent user interfaces
A predictive model of menu performance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The need for an interaction cost model in adaptive interfaces
AVI '08 Proceedings of the working conference on Advanced visual interfaces
A decision-theoretic approach to task assistance for persons with dementia
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Fast and robust interface generation for ubiquitous applications
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Benefits of subliminal feedback loops in human-computer interaction
Advances in Human-Computer Interaction - Special issue on subliminal communication in human-computer interaction
AppMap: exploring user interface visualizations
Proceedings of Graphics Interface 2011
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Adaptive software systems are intended to modify their appearance, performance or functionality to the needs and preferences of different users. A key bottleneck in building effective adaptive systems is accounting for the cost of disruption to a user's mental model of the application caused by the system's adaptive behaviour. In this work, we propose a probabilistic approach to modeling the cost of disruption. This allows an adaptive system to tradeoff disruption cost with expected savings (or other benefits) induced by a potential adaptation in a principled, decision-theoretic fashion. We conducted two experiments with 48 participants to learn model parameters in an adaptive menu selection environment. We demonstrate the utility of our approach in simulation and usability studies. Usability results with 8 participants suggest that our approach is competitive with other adaptive menus w.r.t. task performance, while providing the ability to reduce disruption and adapt to user preferences.