Mixing Board Versus Mouse Interaction In Value Adjustment Tasks

We present a controlled, quantitative study with 12 participants comparing interaction with a haptically enhanced mixing board against interaction with a mouse in an abstract task that is motivated by several practical parameter space exploration settings. The study participants received 24 sets of one to eight integer values between 0 and 127, which they had to match by making adjustments with physical or graphical sliders, starting from a default position of 64. Based on recorded slider motion path data, we developed an analysis algorithm that identifies and measures different types of activity intervals, including error time moving irrelevant sliders and end time in breaks after completing each trial item. This decomposition facilitates data cleaning and more selective outlier removal, which is adequate for the small sample size. Our results showed a significant increase in speed of the mixing board interaction accompanied by reduced perceived cognitive load when compared with the traditional mouse-based GUI interaction. We noticed that the gains in speed are largely due to the improved times required for the hand to reach for the first slider (acquisition time) and also when moving between different ones, while the actual time spent manipulating task-relevant sliders is very similar for either input device. These results agree strongly with qualitative predictions from Fitts’ Law that the larger targets afforded by the mixer handles contributed to its faster performance. Our study confirmed that the advantage of the mixing board acquisition times and between times increases, as more sliders need to be manipulated in parallel. To investigate this further, we computed a measure of motion simultaneity based on velocity correlation. This enabled us to identify types of items for which increased simultaneous adjustments occur. In the context of continuous parameter space exploration our findings suggest that mixing boards are a considerable option that provides a detailed multi-value control. The strengths of this input method should particularly show in settings where screen space is precious and undisrupted visual focus is crucial.