Vibes on Demand: Adding Vibrotactile Encoding to Line Charts Shows Experiential Benefits Without Performance Costs
Authors
Anchit Mishra (University of Waterloo), Oliver Schneider (University of Waterloo), Matthew Brehmer (University of Waterloo)
Presentation
- Session
- This feels amazingly real!
- Time
- Friday, Nov 13, 08:12 – 08:24 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall America north
Keywords
Haptics, multi-sensory interfaces, details-on-demand, temporal data, perception & cognition, human-subjects studies.
Abstract
Details on demand is a common design pattern in visualization design, especially useful when interacting with visually-saturated or small displays. Beyond visualization, another common approach for saturated displays is to incorporate other modalities, such as haptic feedback. While haptic rendering in visualization has primarily targeted accessibility needs, with haptics as a substitute for visual feedback, studies using haptics outside of a visualization context have shown value in experiential factors, such as increased confidence in ambiguous contexts and higher engagement. We explore vibrotactile feedback as a reinforcing information channel for communicating trends in details-on-demand tooltips on touchscreens. We identify preferred parameter configurations for our haptic encoding, informed by a study where participants identified parameter configurations that they perceived to most accurately reflect the dynamics of line charts appearing in tooltips. In a second study, we evaluated participant performance in a pairwise comparison task, finding that incorporating vibrotactile encoding improves involvement without affecting accuracy. We discuss the implications of these findings for future visualization design, and propose directions for applications and future studies.
For Practitioners
We believe this paper provides useful insights for practitioners who study visual perception and contribute to theoretical aspects pertaining to visualization design such as the different types of visual variables (e.g., shape, colour, etc.) and their interactions with each other (e.g., whether they are integral or separable). We also believe that our findings may be of interest to practitioners who work with affective visualization and slow analytics.