In virtual reality, users are typically represented by and interact through a virtual body. Research frequently manipulates different features of these bodies. We analyze 208 studies to identify what aspects of virtual bodies are manipulated, how these manipulations affect interaction and other outcomes, and why they are assumed to do so. Based on the analysis, we propose a design space comprising seven types of visual manipulations: appearance, size, morphology, viewpoint, transfer, remapping, and control. We also synthesize findings on their effects—ranging from task performance to physiological responses and social outcomes—and examine the theories used to explain them, such as embodiment, Proteus effect, and presence. The design space helps researchers identify key variables and their interconnections in design and empirical research of virtual bodies. The synthesis further reveals unexplored causal connections and highlights theories that may account for observed effects.

VR interaction techniques define mappings between physical movements and virtual outcomes. While some mappings are learned through the adaptation of existing movement strategies, others require acquiring entirely new control policies. Drawing on motor learning theory, we introduce a design space that organizes these mappings into three families and provides a basis for reasoning about how the mappings are learned. To examine learning within individual families and their compositions, we start with two simple hand-based mappings, mirror reversal and cross-hand control, and their combination, allowing us to probe the design space in a controlled experiment with 96 participants. The mappings differ in initial difficulty, but participants achieve comparable overall learning. In the combined condition, prior exposure produces mapping specific start-up advantages. We discuss how this design space can support the analysis and design of VR mapping techniques.

Tendon vibration can create movement illusions: vibrating the biceps tendon induces an illusion of extending the arm, while vibrating the triceps tendon induces an illusion of flexing the arm. However, it is unclear how to create and integrate such illusions shown in neuroscience to interaction techniques in virtual reality (VR). We first design a motor setup for tendon vibration. Study 1 validates that the setup induces movement illusions which on average create a 5.26 cm offset in active arm movements. Study 2 shows that tendon vibration improves the detection thresholds of visual motion gains often used in VR interaction techniques by 0.22. A model we developed in Study 2 predicts the effects of tendon vibration and is used in a biomechanical simulation to demonstrate the detection thresholds across typical reaching tasks in VR.

Virtual reality (VR) techniques can modify how physical body movements are mapped to the virtual body. However, it is unclear how users learn such mappings and, therefore, how the learning process may impede interaction. To understand and quantify the learning of the techniques, we design new metrics explicitly for VR interactions based on the motor learning literature. We evaluate the metrics in three object selection and manipulation tasks, employing linear-translational and nonlinear-rotational gains and finger-to-arm mapping. The study shows that the metrics demonstrate known characteristics of motor learning similar to task completion time, typically with faster initial learning followed by more gradual improvements over time. More importantly, the metrics capture learning behaviors that task completion time does not. We discuss how the metrics can provide new insights into how users adapt to movement mappings and how they can help analyze and improve such techniques.