Moving a slider to set the music volume or control the air conditioning is a familiar task that requires little attention. However, adjusting a virtual slider on a featureless touchscreen is much more demanding and can be dangerous in situations such as driving. Here, we study how a gradual tactile feedback, provided by a haptic touchscreen, can replace visual cues. As users adjust a setting with their finger, they feel a continuously changing texture, which spatial frequency correlates to the value of the setting. We demonstrate that, after training with visual and auditory feedback, users are able to adjust a setting on a haptic touchscreen without looking at the screen, thereby reducing visual distraction. Every learning strategy yielded similar performance, suggesting an amodal integration. This study shows that surface haptics can provide intuitive and precise tuning possibilities for tangible interfaces on touchscreens.

Virtual reality (driving simulators) tends to generalize for the study of human behavior in mobility. It is thus crucial to ensure that perception of space and motion is little or not affected by the virtual environment (VE). The aim of this study was to determine a metrics of distance perception in VEs and whether this metrics depends on interactive factors: stereoscopy and motion parallax. After a training session, participants were asked, while driving, to estimate the relative location (5 to 80 m) of a car on the same road. The overall results suggest that distance perception in this range does not depend on interactive factors. In average, as generally reported, subjects underestimated the distances whatever the vision conditions. However, the study revealed a large interpersonal variability: two profiles of participants were defined, those who quite accurately perceived distances in VR and those who underestimated distances as usually reported. Overall, this classification was correlated to the level of performance of participants during the training phase. Furthermore, learning performance is predictive of the behavior of participants.