The goal of rehabilitation interventions for clients with neurological impairments is to (re)learn motor skills during therapeutic practice and transfer those improvements to functional activities in daily life. Researchers and clinicians seek to understand the content and structure of practice that facilitates such learning and transfer for different tasks, environmental contexts and clinical populations . Although (re)learning activities of daily living is the focus of neurological rehabilitation, much of the evidence base for therapeutic interventions stems from basic or clinical research on simple experimentally-controlled tasks, such as reaching to a target in the horizontal plane or learning a finger tapping sequence. While these simplified tasks are very different from the tasks of daily life, they facilitate precise quantification of performance variables and stringent hypothesis testing, providing insights into basic principles of motor control and learning. However, their deliberately reduced testbeds lack a feature that is pervasive in real-world tasks: the affordance of multiple options to achieve a movement goal . Hence, principles of learning derived from these simple movement paradigms may not translate into useful transfer-oriented principles for rehabilitation .
With some exceptions, e.g., Constraint-Induced Movement Therapy , few rehabilitation interventions can consistently demonstrate evidence for transfer from practiced tasks to non-treatment contexts. This is also true for the rehabilitation-based use of virtual environments (VEs): computer hardware and software systems that generate simulations of real or imagined environments with which participants interact using their own movements . VEs differ according to viewing medium, level of immersion, and type of interaction . While practice in a variety of VEs offers promising evidence for skill acquisition as compared to conventional interventions in many rehabilitation populations, [e.g. 7, 8, 9, 10] the focus has been predominantly on training simplified movements. This may be one reason why successful transfer of skill learning to non-practiced tasks and real-life contexts often remains a challenge [11, 12, 13, 14, 15, 16]. As such, the design of both virtual and conventional interventions requires greater understanding of how humans acquire, retain and transfer real-world skills. We propose that VEs themselves can serve as useful experimental platforms to gain this knowledge as they allow the study of these complex skills with sufficient experimental control to draw scientifically tractable conclusions .
Complex real-world tasks have nested redundancy
In the motor learning literature, the adjective “complex” is often treated synonymously with “difficult” [17, 18]. For example, a task can be labelled as difficult or complex when reaction time or movement time are relatively long, when skill improvement requires long hours of practice, or when the task poses high demands on the learner’s attention and memory . To sharpen the discussion, we reserve the term ‘complex’ for tasks with nested redundancy. Redundancy is present when there is a greater number of execution variables than variables that define the result of the task. The well-known example for motor redundancy is pointing to a target with one’s fingertip, which can be achieved with many different joint configurations, because the arm (without the hand) has 7 degrees of freedom, while the target is defined in 3 degrees of freedom.