Creating Personalized Digital Human Models of Perception for Visual Analytics

Abstract

Abstract. 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.

Robust models of mouse movement on dynamic web search results pages

“…Models include those based on the visual salience [27], and biology [4]. Our work is most closely related to machine learning models of visual attention [6,20].…”

Rendering algorithms for aberrated human vision simulation

“…Using this approach, experts can gain a better understanding of the impact of such conditions on the everyday lives of affected individuals, and thereby facilitate the development of proper tools and devices that better support the lives of these people. To this end, synthetic [61,[73][74][75][76][77], VR [78][79][80], and augmented reality (AR) [80][81][82] environments have already been successfully used.…”

Many people, many eyes

“…Factors will be joined by edges when two factors interact (e.g., age and eye disease both effect visual acuity [1]). This exercise will provide the opportunity to identify interactions between different factors that influence visual perception, helping build common knowledge among workshop participants.…”

“…Most prior work addressed one factor only (e.g., colorblindness [4], visual acuity [1], artifact size [7], memory [10], depth perception [5], visual search [11], saliency [15], perceptual grouping [13], gender [3], culture [12]). Some work resulted in perceptual models that can be used to adapt interfaces to a user's abilities and preferences [9][1].…”