Pointing and visual feedback for spatial interaction in large-screen display environments

Abstract. The two visual systems hypothesis in neuroscience suggests that pointing without visual feedback may be less affected by spatial visual illusions than cognitive interactions such as judged target location. Our study examined predictions of this theory for target localization on a large-screen display. We contrasted pointing interactions under varying levels of visual feedback with location judgments of targets that were surrounded by an offset frame. As predicted by the theory, the frame led to systematic errors in verbal report of target location but not in pointing without visual feedback for some participants. We also found that pointing with visual feedback produced a similar level of error as location judgments, while temporally lagged visual feedback appeared to reduce these errors somewhat. This suggests that pointing without visual feedback may be a useful interaction technique in situations described by the two visual systems literature, especially with large-screen displays and immersive environments.

The personal equation of interaction for interface learning: Predicting the performance of visual analysis through the assessment of individual differences

The Personal Equation of Interaction (PEI) for Interface Learning is a short self-report psychometric measure which predicts reasoning outcomes of interface learning such as accurate target identification and insights garnered through and inferred from learning interaction. By predicting outcomes, we consider why some interfaces are more appropriate than others, provide a tool for intuitive interface design, and advance the pursuit and design of interface individuation. Through study designs which use comparative interfaces and simple but imperative tasks to any interface learning, such as target identification and inferential learning, we evaluate the accuracy of analysts and how it is impacted by graphical representation. By using psychometric items culled from normed trait assessment, we have created a measure which predicts accuracy and learning, called the Personal Equation of Interaction. This prediction tool can be used in a variety of ways, including as a function or equation that puts a number on the association between analyst and interface. We also use the PEI to build profiles of analyst expert cohorts and discuss how its use might impact Visual Analytics