論文アブストラクト： Interactive diagrams are expensive to build, requiring significant programming experience. The cost of building such diagrams often prevents novice programmers or non-programmers from doing so. In this paper, we present user-guided techniques that transform a static diagram into an interactive one without requiring the user to write code. We also present a tool called EDDIE that prototypes these techniques. We evaluate EDDIE through: (1) a case study in which we use EDDIE to implement existing real-world diagrams from the literature and (2) a usability session with target users in which subjects build several diagrams in EDDIE and provide feedback on EDDIE's user experience. Our experiments demonstrate that EDDIE is usable and expressive, and that EDDIE enables real-world diagrams to be implemented without requiring programming expertise.
論文アブストラクト： By exploiting visual popout effects, interface designers can rapidly draw a user's attention to salient information objects in a display. A variety of different visual stimuli can be used to achieve popout effects, including color, shape, size, motion, luminance, and flashing. However, there is a lack of understanding about how accurately different intensities of these effects support popout, particularly as targets move further from the center of the visual field. We therefore conducted a study to examine the accuracy of popout target identification using different visual variables, each at five different levels of intensity, and at a wide range of angles from the display center. Results show that motion is a strong popout stimulus, even at low intensities and wide angles. Identification accuracy decreases rapidly across visual angle with other popout stimuli, particularly with shape and color. The findings have relevance to a wide variety of applications, particularly as multi-display desktop environments increase in size and visual extent.
論文アブストラクト： This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.