Visuomotor learning by passive motor experience

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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

Humans can adapt to unfamiliar dynamic and/or kinematic transformations through the active motor experience. Recent studies of neurorehabilitation using robots or brain-computer interface (BCI) technology suggest that passive motor experience would play a measurable role in motor recovery, however our knowledge of passive motor learning is limited. To clarify the effects of passive motor experience on human motor learning, we performed arm reaching experiments guided by a robotic manipulandum. The results showed that the passive motor experience had an anterograde transfer effect on the subsequent motor execution, whereas no retrograde interference was confirmed in the ABA paradigm experiment. This suggests that the passive experience of the error between visual and proprioceptive sensations leads to the limited but actual compensation of behavior, although it is fragile and cannot be consolidated as a persistent motor memory.

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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