Active Sampling for Efficient Subjective Evaluation of Tactons at Scale


Traditional tacton evaluation studies often rely on pre-defined haptic effects that are specifically tailored to explore a handful of design parameters. To prevent combinatorial explosion, researchers are forced to constrain their exploration to very limited subsets of the parameter space. In this work, we propose a hands-off active sampling strategy grounded in probability and information theory that automatically generates tactons to maximize the perceptual information gain at each stimulus presentation. As a proof of concept of the proposed technique, we present the results from a crowdsourced study investigating the perceived similarity between tactons with over 200 participants. Without researcher intervention in the tacton selection process, our method allowed a set of the most salient features for perception of tacton similarity to emerge naturally from the data. This approach is highly scalable and allows for a more efficient exploration of a larger haptic space than typical laboratory study designs aimed at evaluating perceptual attributes of tactons.

2021 IEEE World Haptics Conference (WHC)