Emphasis effects – visual changes that make certain elements more
 prominent – are commonly used in information visualization to draw
 the user’s attention or to indicate importance. Although theoretical
 frameworks of emphasis exist (that link visually diverse emphasis
 effects through the idea of visual prominence compared to background
 elements), most metrics for predicting how emphasis effects
 will be perceived by users come from abstract models of human
 vision which may not apply to visualization design. In particular,
 it is difficult for designers to know, when designing a visualization,
 how different emphasis effects will compare and what level of one
 effect is equivalent to what level of another. To address this gap,
 we carried out two studies that provide empirical evidence about
 how users perceive different emphasis effects, using three visual
 variables (colour, size, and blur/focus) and eight strength levels.
 Results from gaze tracking, mouse clicks, and subjective responses
 show that there are significant differences between visual variables
 and between levels, and allow us to develop an initial understanding
 of perceptual equivalence. We developed a model from the data in
 our first study, and used it to predict the results in the second; the
 model was accurate, with high correlations between predictions and
 real values. Our studies and empirical models provide valuable new
 information for designers who want to understand and control how
 emphasis effects will be perceived by users. Emphasis effects are visual changes that make certain elements more prominent, and are commonly used in information visualization to draw the user's attention or to indicate importance. Emphasizing important data points is a common method used by designers to support the user when gradually exploring the data -or in narrative visualization [25] , when known aspects of the data are presented to the users. An effective emphasis effect will alter a data point's visual features [4, 22] , such that a viewer's attention will be guided to the region of interest [61] . The goal of emphasis is to alter important data points to appear more visually prominent and can be achieved through the use of a variety of visual effects [17, 22, 23, 59] . For example, a visualization can use colour changes to emphasize some data points, and differences in the visual prominence of the selected data points will be achieved from variations in color, a visual variable known to guide a user's attention [24] . Although theoretical frameworks of emphasis exist that link visually diverse emphasis effects through the idea of visual prominence compared to background elements [22] , we still know little about how emphasis effects will be perceived by users. In particular, we know little about what visual effects, and what magnitudes of those effects, will be most quickly recognized as emphasis by the viewer of a visualization; in addition, we know little about how different effects compare and what level of an effect is equivalent to what level of another. Many metrics for predicting how emphasis effects will be perceived by users come from abstract models of human vision which may not apply to visualization design. These abstract models from human vision are generally constructed using large and visually isolated stimuli under optimal conditions. Models of human visual attention are effective at predicting perceptibility in isolation but not within a field of distractors, and do not work well with even minor changes to the visual field [3, 58] . Visualizations, in contrast, often consist of large numbers of a variety of marks viewed using a wide range of devices and environments -and designers may use a variety of techniques to emphasize data points. Current guidelines do not address how different emphasis effects are perceived by viewers in visualizations, or provide an equivalence metric for perceived emphasis so designers can choose effects correctly. Without effective models of visual prominence in visualizations, designers lack information on know how different visual effects compare, and don't know what magnitude of effect to use to appropriately guide a viewer's attention to an area of interest. To address this gap, we carried out two studies that provide empirical evidence about how users perceive different emphasis effects, using three visual variables (colour, size, and blur/focus) and eight strength levels. It is important to note that the three emphasis effects are qualitatively different -for example, colour and size manipulate just the emphasized element, whereas blur/focus manipulates everything but the emphasized element -and so our goal is not simply to identify which effect is most perceivable, but rather to establish how the effects compare to one another at different magnitudes. To do this, our first study established a baseline of perceived visual prominence through eye-tracking data, interaction logs, and subjective ratings in simulated static scatterplot visualizations. We then built a model from the first study's data using logarithmic curves, that provides a prediction of equivalence between the three emphasis effects. Our second study then examined perceived emphasis in a more realistic context, by looking at visual prominence in complex visualizations that are taken from real-world applications (the MASSVIS dataset [7] ). We evaluated our model by using it to predict the results of the second study for three different measures; the model was accurate, with R 2 values as high as 0.96. Our two studies provide new findings about how people perceive three emphasis effects and their magnitudes in visualizations: • There were significant differences in both studies for emphasis effect: blur/focus was most prominent, and colour least prominent, with size in between depending on magnitude. • There were also significant differences between the magnitude levels for all effects, providing a graduated way to increase or decrease perceived prominence. • A predictive model based on logarithmic curves fit the Study 1 data well, and was accurate at predicting perceived emphasis in Study 2 (particularly in terms of subjective ratings). Our studies provide an initial empirical foundation for understanding how visual effects operate and are experienced by viewers when used for emphasis in visualizations -and although more work is needed to refine and broaden the models, our work provides useful new information for designers who want to control how emphasis effects will be perceived by users. Emphasis is essential to InfoVis and is used to highlight regions of interest in a visualization. While there is a large body of research in this domain, much of the work seeks to understand how the underlying perceptual system operates -limiting the possibility of extracting design lessons from low-level data and findings. We survey current empirical studies of perception from visualization and vision science to inform our work. Emphasis is an essential component of InfoVis, and is used by designers to draw a user's attention or to indicate importance. However, it is difficult for designers to know how different emphasis effects will compare and what level of one effect is equivalent to what level of another when designing visualizations. We carried out two user studies to evaluate the visual prominence of three emphasis effects (blur/focus, colour, and size) at various strength levels, and developed a predictive model that can indicate equivalence between effects. Results from our two studies provide the beginnings of an empirical foundation for understanding how visual effects operate and are experienced by viewers when used for emphasis in visualizations, and provide new information for designers who want to control how emphasis effects will be perceived by users.
Our studies and empirical models provide valuable new information for designers who want to understand and control how emphasis effects will be perceived by users This paper considers which visual highlighting is perceived faster in data visualization and how different highlighting methods compare to each other Two studies on the efficacy of emphasis effects, one assessing levels of useful differences, and one more applied using actual different visualizations for a more ecologically valid investigation.