http://journal.frontiersin.org/article/10.3389/fnhum.2015.00279/full?
Takashi Sakamoto† and Toshiyuki Kondo*†- Department of Computer and Information Sciences, Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
1. Introduction
Previous studies of human motor learning have shown that
we can adapt to unfamiliar environments with dynamic and/or kinematic
transformations through the active motor experience. Active motor
learning has been widely investigated based on various motor tasks, such
as mirror drawing (Adams, 1987; Basteris et al., 2012), shift prism (Luauté et al., 2009), visuomotor rotation (Krakauer et al., 1999; Imamizu et al., 2000; Caithness et al., 2004; Kondo and Kobayashi, 2007; Saijo and Gomi, 2010), and virtual force fields (Shadmehr and Brashers-Krug, 1997; Tong et al., 2002; Caithness et al., 2004; Bays et al., 2005; Ito et al., 2007).
These studies showed that the central nervous systems (CNS) generates
internal models; forward models predict future states according to the
current state and action, whereas inverse models calculate an
appropriate motor command based on a desired motor plan (Wolpert et al., 1995; Kawato, 1999), thereby facilitating fast and accurate movements via active interactions with the environment.
In these studies, transfer or interference of the
internal models were examined because the efficient acquisition of motor
skills is of general interest for human movement science research. In
particular, the consecutive learning of mutually conflicting motor tasks
(A and B) is known to be difficult because of retrograde interference,
i.e., the motor skill required for the first task (A) cannot be retained
as an internal model after 24-h rest period due to interference from a
secondary task (B) experienced immediately after the first motor
learning session (Brashers-Krug et al., 1996; Shadmehr and Brashers-Krug, 1997; Krakauer et al., 1999; Tong et al., 2002; Bays et al., 2005).
The methodology employed in these studies is referred to as the ABA
paradigm. Using the paradigm, we can investigate how a motor experience
is consolidated as an internal model in our brain.
These studies demonstrate that the adjustment of
feedforward motor commands is based mainly on the error between
re-afferent sensory feedback and the prediction of the forward model;
thus, active motor process is considered to be indispensable for motor
learning. However, recent studies on robot-assisted motor experience
suggest that robotic intervention facilitates the acquisition of novel
motor skills (Reinkensmeyer and Patton, 2009; Bara and Gentaz, 2011; Basteris et al., 2012; Beets et al., 2012) and might also improve the motor function of hemiparesis patients (Aisen et al., 1997; Krebs et al., 1998; Riener et al., 2005; Kahn et al., 2006; Vergaro et al., 2010).
In addition, brain-computer interface (BCI) based neurorehabilitation
research has hypothesized that passive motor experience via a robotic
exoskeleton or a functional electrical stimulation (FES) would play a
measurable role in motor recovery if it is coupled to a voluntary motor
intention (Takahashi et al., 2012).
These studies indicate that even a passive sensorimotor experience
might be effective in improving motor skills; however, our knowledge of
motor learning through the passive motor experience is still
insufficient compared with the active one.
To clarify the effect of passive motor experience on
human visuomotor learning, we performed two motor learning experiments
that comprised arm reaching tasks during visuomotor rotations guided by a
robotic manipulandum. The first experiment evaluated the anterograde
effect of passive motor experience on successive active motor learning.
The second experiment used an ABA paradigm to investigate both
anterograde and retrograde interference via passive motor experience.
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