An Internal Model for Acquisition and Retention of Motor Learning During Arm Reaching
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Lonini-2009-An Internal Model for Acquisition and Retention of Motor Learning During Arm Reaching.pdf
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Author(s)
Lonini, Luca
Dipietro, Laura
Zollo, Loredana
Guglielmelli, Eugenio
Krebs, Hermano Igo
Date Issued
June 2009
Journal
Neural Computation
Publisher
MIT Press
Citation
Lonini, Luca et al. “An Internal Model for Acquisition and Retention of Motor Learning During Arm Reaching.” Neural Computation 21.7 (2009): 2009-2027. ©2009 Massachusetts Institute of Technology.
Version
Final published version
Abstract
Humans have the ability to learn novel motor tasks while manipulating
the environment. Several models of motor learning have been proposed
in the literature, but few of them address the problem of retention and interference
ofmotor memory. The modular selection and identification for
control (MOSAIC) model, originally proposed by Wolpert and Kawato,
is one of the most relevant contributions; it suggests a possible strategy
on how the human motor control system learns and adapts to novel environments.
MOSAIC employs the concept of forward and inverse models.
The same group later proposed the hidden Markov model (HMM)
MOSAIC, which affords learning multiple tasks. The significant drawback
of this second approach is that the HMM must be trained with a
complete data set that includes all contexts. Since the number of contexts or modules is fixed from the onset, this approach does not afford incremental
learning of new tasks. In this letter, we present an alternative
architecture to overcome this problem, based on a nonparametric regression
algorithm, named locally weighted projection regression (LWPR).
This network structure develops according to the contexts allowing incremental
training. Of notice, interaction force is used to disambiguate
among different contexts. We demonstrate the capability of this alternative
architecture with a simulated 2 degree-of-freedom representation of
the human arm that learns to interact with three distinct objects, reproducing
the same test paradigm of the HMM MOSAIC. After learning
the dynamics of the three objects, the LWPR network successfully learns
to compensate for a novel velocity-dependent force field. Equally important,
it retains previously acquired knowledge on the interactionwith the
three objects. Thus, this architecture allows both incremental learning of
new tasks and retention of previously acquired knowledge, a feature of
human motor learning and memory.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Newman Laboratory for Biomechanics and Human Rehabilitation
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DOI of Published Version
https://doi.org/10.1162/neco.2009.03-08-721
