Abstract
We present a developmental neural network model
of motor learning and control, called RL SURE REACH.
In a childhood phase, a motor controller for goal directed
reaching movements with a redundant arm develops unsupervisedly.
In subsequent task-specific learning phases, the
neural network acquires goal-modulation skills. These skills
enable RL SURE REACH to master a task that was used
in a psychological experiment by Trommersh�auser, Maloney,
and Landy (2003). This task required participants to select
aimpoints within targets that maximize the likelihood of hitting
a rewarded target and minimizes the likelihood of accidentally
hitting an adjacent penalty area. The neural network acquires
the necessary skills by means of a reinforcement learning based
modulation of the mapping from visual representations to the
target representation of the motor controller. This mechanism
enables the model to closely replicate the data from the original
experiment. In conclusion, the effectiveness of learned actions
can be significantly enhanced by fine-tuning action selection
based on the combination of information about the statistical
properties of the motor system with different environmental
payoff scenarios.
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