Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods

Abstract

Abstract-We present an evaluation of a parameterized set of 2D icon-based visualization methods where we quantified how perceptual interactions among visual elements affect effective data exploration. During the experiment, subjects quantified three different design factors for each method: the spatial resolution it could represent, the number of data values it could display at each point, and the degree to which it is visually linear. The class of visualization methods includes Poisson-disk distributed icons where icon size, icon spacing, and icon brightness can be set to a constant or coupled to data values from a 2D scalar field. By only coupling one of those visual components to data, we measured filtering interference for all three design factors. Filtering interference characterizes how different levels of the constant visual elements affect the evaluation of the data-coupled element. Our novel experimental methodology allowed us to generalize this perceptual information, gathered using ad-hoc artificial datasets, onto quantitative rules for visualizing real scientific datasets. This work also provides a framework for evaluating visualizations of multi-valued data that incorporate additional visual cues, such as icon orientation or color.

Evaluating affective features of 3D motionscapes

“…Affect is important in an ambient context, the result of how an experience or environment feels. Attaining the right affective balance is considered core to user experience [Norman 2002;Peter and Beale 2008;Forlizzi and Battarbee 2004;McDonagh et al 2009] and is becoming an important aspect of information visualization [Acevedo and Laidlaw 2006;Lau and Vande Moere 2007;Kosara 2007]. Visual designers and artists carefully manipulate visual elements such as colour, form and motion to evoke affect [Collopy 2000], but empirical evidence on how such elements in motion textures should be manipulated to invoke affective impressions is still scarce and an emerging research area.…”

Bristle Maps: A Multivariate Abstraction Technique for Geovisualization

“…Research into the perceptual interactions among different visual variables was performed by Acevedo and Laidlaw [42]. They measured the perceptual interfernce of icon size, spacing and brightness, noting that brightness outperforms spacing and size while being subject to interferences from both spacing and size.…”

Measuring the Separability of Shape, Size, and Color in Scatterplots

“…Few studies have empirically explored interactions between separable channels in data visualizations. Acevedo & Laidlaw [1] found that people more precisely detect brightness differences than differences in other visual elements such as size or spacing in multivariate icon-based visualizations. Other studies have shown that quantity estimation is affected by both size and color [19,23] with size perceptions being biased by specific hues.…”