A Two Visual Systems Approach to Understanding Voice and Gestural Interaction

It is important to consider the physiological and behavioral mechanisms that allow users to physically interact with virtual environments. Inspired by a neuroanatomical model of perception and action known as the two visual systems hypothesis, we conducted a study with two controlled experiments to compare four different kinds of spatial interaction: (1) voice-based input, (2) pointing with a visual cursor, (3) pointing without a visual cursor, and (4) pointing with a time-lagged visual cursor. Consistent with the two visual systems hypothesis, we found that voice-based input and pointing with a cursor were less robust to a display illusion known as the induced Roelofs Effect than pointing without a cursor or even pointing with a lagged cursor. The implications of these findings are discussed, with an emphasis on how the two visual systems model can be used to understand the basis for voice and gestural interactions that support spatial target selection in large-screen and immersive environments. Keywords: two visual systems, pointing, cursors, visual feedback, voice input, visual illusion

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