Sensory Perception & Interaction, Research Group

The design and evaluation of haptic icons -- brief, meaningful tactile stimuli -- has been studied extensively in the research community. Haptic icons are designed to support communication of information through the often-underutilized haptic modality. However, the learnability of haptic icons has not been evaluated in an ecologically plausible, longitudinal deployment scenario. This thesis endeavours to evaluate the learnability of haptic icons in a realistic context. We assign abstract meanings based on a realistic context to a large, previously developed set of rhythmic haptic stimuli. Then, during a period of 12 sessions over 4 weeks, we train users to recognize these icons and observe identification performance under workload using a Tetris game interruption task. Icons are presented to users in sets of 7. Upon the mastery of their current 7 icons, the user graduates to a new set, but must remember previously learned icons. We discover that perceptual discriminability dominates learnability -- the semantics of the icons have very little effect. We also find evidence that design based on multidimensional scaling (MDS) is adequate for developing haptic stimulus sets, but can be quite conservative in its identification performance predictions during deployment. Haptic icon learning is characterized by a peak in difficulty after learning progresses past a single group 7 icons, which may be explained by cognitive long-term encoding and an increase in perceptual sensitivity. In addition, we present a series of heuristics for designing rhythmic haptic icons, as well as guidelines for haptic icon training and advice for hardware designers. In an attempt to increase the expressiveness and learnability of rhythmic haptic icons, we explore the addition of melody. We iteratively develop a second set of 30 melodic haptic icons using an MDS methodology. We discover that rhythm dominates user categorization of melodies. This work also results in a set of heuristics for designing melodic icons. Finally, we evaluate the learnability of this new melodic set using our previous longitudinal methodology. Our results indicate that purely rhythmic haptic icons are easier to learn than melodic haptic icons that are grouped by rhythm and are thus more viable for deployment.