Motion and Meaning: Sample-Level Nonlinear Analyses of Virtual Reality Tracking Data

Abstract

Behavioral data is the “gold standard” for experiments in psychology. The tracking component of virtual reality systems captures data on nonverbal behavior both covertly and continuously at high spatial and temporal fidelity, enabling what is called behavioral tracing. With previous research analyzing this type of data, however, inference has primarily been limited to linear relationships of subject-level aggregates. In this work, we suggest these rough aggregations are often neither the best according to theory nor do they make use of the rich data available from behaviorally traced experiments. We also explore the relationships between motion and subjective experiences with a previously published dataset of 360-degree video and emotion, and we find evidence for nonlinear sample-level relationships. In particular, reported valence relates with head pitch and pitch velocity, among others, and reported arousal relates with head rotation speed and yaw velocity, among others. The role of these sample-level nonlinear relationships for future work are discussed.