Abstract
This paper addresses research questions that are central to the area of visualization interfaces for decision support: (RQ1) whether individual user differences in working memory should be considered when choosing how to present visualizations; (RQ2) how to present the visualization to support effective decision making and processing; and (RQ3) how to evaluate the effectiveness of presentational choices. These questions are addressed in the context of presenting plans, or sequences of actions, to users. The experiments are conducted in several domains, and the findings are relevant to applications such as semi-autonomous systems in logistics. That is, scenarios that require the attention of humans who are likely to be interrupted, and require good performance but are not time critical. Following a literature review of different types of individual differences in users that have been found to affect the effectiveness of presentational choices, we consider specifically the influence of individuals' working memory (RQ1). The review also considers metrics used to evaluate presentational choices, and types of presentational choices considered. As for presentational choices (RQ2), we consider a number of variants including interactivity, aggregation, layout, and emphasis. Finally, to evaluate the effectiveness of plan presentational choices (RQ3) we adopt a layered-evaluation approach and measure performance in a dual task paradigm, involving both task interleaving and evaluation of situational awareness. This novel methodology for evaluating visualizations is employed in a series of experiments investigating presentational choices for a plan. A key finding is that emphasizing steps (by highlighting borders) can improve effectiveness on a primary task, but only when controlling for individual variation in working memory.
Highlights
An autonomous system consists of physical or virtual systems that can perform tasks without continuous human guidance
The aggregation allowed investigating the effects of interactivity in plan presentations. These two presentational choices are two alternatives that we investigated as part of RQ2
The statistical analyses reported below were carried out in the mixed effects regression framework using the R package lme4 (Bates et al, 2013). This method is well suited for studying repeated measures; it allows us to model individual variations between subjects as might be expected by variation in visual working memory (Conati and Merten, 2007)
Summary
An autonomous system consists of physical or virtual systems that can perform tasks without continuous human guidance. Autonomous systems are becoming increasingly ubiquitous, ranging from unmanned vehicles, to virtual agents which process information on the internet Such systems can potentially replace humans in a variety of tasks which can be dangerous (such as refuelling a nuclear reactor), mundane (such as crop picking), or require superhuman precision (as in robotic surgery). Most of these systems are still semi-autonomous in the sense that they need human approval for execution, or can be interrupted by human operators (e.g., current commercial self-driving cars require a person to keep their hands on the wheel at all times) For these semi-autonomous systems to operate optimally, it is vital that humans understand the actions the system is planning. This paper focuses on visual representations of plans
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
