Table 1 Attributes of virtual environments that facilitate the study of complex skill learning and transfer

Detailed measurements of execution (or process) beyond course-grained descriptive outcome measures of motor performance.

Precise tracking of human kinematics and interaction with virtual objects. Ability to combine measurement of task execution variables and result variables.

Ability to mathematically model motor tasks and vary relevant task parameters

Mathematical modeling of task physics makes explicit the variables that define task execution and result. Parameters that can be manipulated include those that reduce or augment task error. Such task constraints can be systematically varied to identify their effect on performance.

Precise simulation of the physics of virtual objects limits uncontrolled aspects that may confound results.

Modeling in a VE confines the task to the measured variables, excluding, for example, environmental noise such as drag or lateral forces influencing the trajectory of a thrown ball.

Ability to examine a range of perceptual conditions with robust experimental control.

VEs enable precise manipulation of experimental parameters, including amount of haptic, haptic, visual or auditory feedback and task difficulty (e.g. changing the size or position of a target), to test hypotheses about performance strategies.