Papers on human motor memory

[Reza Shadmehr]

Hello,

The following two papers will appear in the upcoming NIPS proceedings.
They deal with some of the properties of human motor memory, including
interference and forgetting.  I've included ftp instructions.

best wishes,

Reza Shadmehr
reza at bme.jhu.edu

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             Interference in learning internal models of 
                   inverse dynamics in humans

       Reza Shadmehr, Tom Brashers-Krug, Ferdinando Mussa-Ivaldi

Abstract: Experiments were performed to reveal some of the computational
properties of the human motor memory system.  We show that as humans
practice reaching movements while interacting with a novel mechanical
environment, they learn an internal model of the inverse
dynamics of that environment.  Subjects show recall of this model at
testing sessions 24 hours after the initial practice.  The
representation of the internal model in memory is such that there is
interference when there is an attempt to learn a new inverse dynamics
map immediately after an anticorrelated mapping was learned.  We
suggest that this interference is an indication that the same
computational elements used to encode the first inverse dynamics map
are being used to learn the second mapping.  We predict that this
leads to a forgetting of the initially learned skill.


anonymous ftp to:    ftp.ai.mit.edu
filename:            pub/users/reza/nips95a.ps.Z

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            Catastrophic interference in human motor memory

           Tom Brashers-Krug, Reza Shadmehr, Emanuel Todorov

Abstract: 
Biological sensorimotor systems are not static maps that transform
input (sensory information) into output (motor behavior). Evidence
from many lines of research suggests that their representations are
plastic, experience-dependent entities. While this plasticity is
essential for flexible behavior, it presents the nervous system with
difficult organizational challenges.  If the sensorimotor system
adapts itself to perform well under one set of circumstances, will it
then perform poorly when placed in an environment with different
demands (negative transfer)?  Will a later experience-dependent change
undo the benefits of previous learning (catastrophic interference)? We
explore the first question in a separate paper in this volume
(Shadmehr et al. 1995). Here we present psychophysical and
computational results that explore the question of catastrophic
interference in the context of a dynamic motor learning task.  Under
some conditions, subjects show evidence of catastrophic interference.
Under other conditions, however, subjects appear to be immune to its
effects. These results suggest that motor learning can undergo a
process of consolidation. Modular neural networks are well suited for
the demands of learning multiple input/output mappings. By incorporating 
the notion of fast- and slow-changing connections into a modular 
architecture, we were able to account for the psychophysical results.


anonymous ftp to:    ftp.ai.mit.edu
filename:            pub/users/reza/nips95b.ps.Z