Evaluating Perceptual Predictions based on Movement Primitive Models in VR- and Online-Experiments

Abstract

We investigate the role of prediction in biological movement perception by comparing different representations of human movement in a virtual reality (VR) and online experiment. Predicting movement enables quick and appropriate action by both humans and artificial agents in many situations, e.g. when the interception of objects is important. We use different predictive movement primitive (MP) models to probe the visual system for the employed prediction mechanism. We hypothesize that MP-models, originally devised to address the degrees-of-freedom (DOF) problem in motor production, might be used for perception as well. In our study we consider object passing movements. Our paradigm is a predictive task, where participants need to discriminate movement continuations generated by MP models from the ground truth of the natural continuation. This experiment was conducted first in VR, and later on continued as online experiment. We found that results transfer from the controlled and immersive VR setting with movements rendered as realistic avatars to a simple and COVID-19 safe online setting with movements rendered as stick figures. In the online setting we further investigate the effect of different occlusion timings. We found that contact events during the movement might provide segmentation points that render the lead-in movement independent of the continuation and thereby make perceptual predictions much harder for subjects. We compare different MP-models by their capability to produce perceptually believable movement continuations and their usefulness to predict this perceptual naturalness. Our research might provide useful insight for application in computer animation, by showing how movements can be continued without violating the expectation of the user. Our results also contribute towards an efficient method of animating avatars by combining simple movements into complex movement sequences.