Feature congestion: a measure of display clutter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Modeling emotion and its expression in virtual humans
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Recommending food: reasoning on recipes and ingredients
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Many people, many eyes: aggregating influences of visual perception on user interface design
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Robust models of mouse movement on dynamic web search results pages
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Our bodies shape our experience of the world, and our bodies influence what we design. How important are the physical differences between people? Can we model the physiological differences and use the models to adapt and personalize designs, user interfaces and artifacts? Within many disciplines Digital Human Models and Standard Observer Models are widely used and have proven to be very useful for modeling users and simulating humans. In this paper, we create personalized digital human models of perception (Individual Observer Models), particularly focused on how humans see. Individual Observer Models capture how our bodies shape our perceptions. Individual Observer Models are useful for adapting and personalizing user interfaces and artifacts to suit individual users' bodies and perceptions. We introduce and demonstrate an Individual Observer Model of human eyesight, which we use to simulate 3600 biologically valid human eyes. An evaluation of the simulated eyes finds that they see eye charts the same as humans. Also demonstrated is the Individual Observer Model successfully making predictions about how easy or hard it is to see visual information and visual designs. The ability to predict and adapt visual information to maximize how effective it is is an important problem in visual design and analytics.