Inspired by recent advances in digital fabrication, artists and scientists have demonstrated that physical data encodings (i.e., data physicalizations) can increase engagement with data, foster collaboration, and in some cases, improve data legibility and analysis relative to digital alternatives. However, prior empirical studies have only investigated abstract data encoded in physical form (e.g., laser cut bar charts) and not continuously sampled spatial data fields relevant to climate and medical science (e.g., heights, temperatures, densities, and velocities sampled on a spatial grid). This paper presents the design and results of the first study to characterize human performance in 3D spatial data analysis tasks across analogous physical and digital visualizations. Participants analyzed continuous spatial elevation data with three visualization modalities: (1) 2D digital visualization; (2) perspective-tracked, stereoscopic "fishtank" virtual reality; and (3) 3D printed data physicalization. Their tasks included tracing paths downhill, looking up spatial locations and comparing their relative heights, and identifying and reporting the minimum and maximum heights within certain spatial regions. As hypothesized, in most cases, participants performed the tasks just as well or better in the physical modality (based on time and error metrics). Additional results include an analysis of open-ended feedback from participants and discussion of implications for further research on the value of data physicalization. All data and supplemental materials are available at https://osf.io/7xdq4/.

We present exploratory research of virtual reality techniques and mnemonic devices to assist in retrieving knowledge from scholarly articles. We used abstracts of scientific publications to represent knowledge in scholarly articles; participants were asked to read, remember, and retrieve knowledge from a set of abstracts. We conducted an experiment to compare participants' recall and recognition performance in three different conditions: a control condition without a pre-specified strategy to test baseline individual memory ability, a condition using an image-based variant of a mnemonic called a "memory palace," and a condition using a virtual reality-based variant of a memory palace. Our analyses show that using a virtual reality-based memory palace variant greatly increased the amount of knowledge retrieved and retained over the baseline, and it shows a moderate improvement over the other image-based memory palace variant. Anecdotal feedback from participants suggested that personalizing a memory palace variant would be appreciated. Our results support the value of virtual reality for some high-level cognitive tasks and help improve future applications of virtual reality and visualization.

Humans have the ability to identify objects under varying lighting conditions with extraordinary accuracy. We investigated the behavioral aspects of this ability and compared it to the performance of the illumination cones (IC) model of Belhumeur and Kriegman [1998]. In five experiments, observers learned 10 faces under a small subset of illumination directions. We then tested observers' recognition ability under different illuminations. Across all experiments, recognition performance was found to be dependent on the distance between the trained and tested illumination directions. This effect was modulated by the nature of the trained illumination directions. Generalizations from frontal illuminations were different than generalizations from extreme illuminations. Similarly, the IC model was also sensitive to whether the trained images were near-frontal or extreme. Thus, we find that the nature of the images in the training set affects the accuracy of an object's representation under variable lighting for both humans and the model. Beyond this general correspondence, the microstructure of the generalization patterns for both humans and the IC model were remarkably similar, suggesting that the two systems may employ related algorithms.

We describe an experiment in which art and illustration experts evaluated six 2D vector visualization methods. We found that these expert critiques mirrored previously recorded experimental results; these findings support that using artists, visual designers and illustrators to critique scientific visualizations can be faster and more productive than quantitative user studies. Our participants successfully evaluated how well the given methods would let users complete a given set of tasks. Our results show a statistically significant correlation with a previous objective study: designers' subjective predictions of user performance by these methods match users measured performance. The experts improved the evaluation by providing insights into the reasons for the effectiveness of each visualization method and suggesting specific improvements.

We present the results from a qualitative and quantitative user study comparing fishtank virtual-reality (VR) and CAVE displays. The results of the qualitative study show that users preferred the fishtank VR display to the CAVE system for our scientific visualization application because of perceived higher resolution, brightness and crispness of imagery, and comfort of use. The results of the quantitative study show that users performed an abstract visual search task significantly more quickly and more accurately on the fishtank VR display system than in the CAVE. The same study also showed that visual context had no significant effect on task performance for either of the platforms. We suggest that fishtank VR displays are more effective than CAVEs for applications in which the task occurs outside the user's reference frame, the user views and manipulates the virtual world from the outside in, and the size of the virtual object that the user interacts with is smaller than the user's body and fits into the fishtank VR display. The results of both studies support this proposition.

We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.

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No abstract available.