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Virtual reality technologies, including head-mounted displays (HMD), can provide benefits to psychological research by combining high degrees of experimental control with improved ecological validity. This is due to the strong feeling of being in the displayed environment (presence) experienced by VR users. As of yet, it is not fully explored how using HMDs impacts basic perceptual tasks, such as object perception. In traditional display setups, the congruency between background environment and object category has been shown to impact response times in object perception tasks. In this study, we investigated whether this well-established effect is comparable when using desktop and HMD devices. In the study, 21 participants used both desktop and HMD setups to perform an object identification task and, subsequently, their subjective presence while experiencing two-distinct virtual environments (a beach and a home environment) was evaluated. Participants were quicker to identify objects in the HMD condition, independent of object-environment congruency, while congruency effects were not impacted. Furthermore, participants reported significantly higher presence in the HMD condition. [ABSTRACT FROM AUTHOR]

Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline. [ABSTRACT FROM AUTHOR]

Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well‐known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task. [ABSTRACT FROM AUTHOR]

Space-filling techniques have been used in the information visualization field as an alternative to the conventional node-link layouts for intuitively showing large hierarchies in less space. Different space filling layouts have been designed, developed and evaluated; however, much less effort have been made to look into how layout can impact user task performance on hierarchical data structures. In this paper, we focus on the impact of layout on user task performance by conducting evaluation studies for two common space-filling layout structures, the Sunburst (radial) layout and the Icicle (rectangular) layout. In our studies, users performed eight search-based tasks on files and directories in the resulting visualizations, first in a controlled environment and subsequently in an online environment. We focused on deriving user performance metrics with regard to effectiveness, efficiency, and user acceptance. Results demonstrate a mixed view of task performance and preference with both layouts, e.g., users performed better with the Icicle layout while they preferred the Sunburst layout for visual aesthetics. We further analyzed the impact of layout on the performance dynamics in terms of response times and accuracy using event history analysis (EHA) in the control study setting. The EHA results revealed clear differences in response tendencies even though no differences existed in mean response times for most of the tasks. It also clearly showed that participants performed more efficiently with the directory comparison tasks than the file comparison tasks. Overall, through these studies we were able to derive causal relationships between the layout and the user's task performance while interacting with hierarchical data structures. [ABSTRACT FROM AUTHOR]