A Mirror Neuron Inspired Model for Goal Inference
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2008-12-22
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en
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Abstract
The human mirror neuron system (MNS) refers to brain regions becoming
active both when an action is performed, and when a similar action is being observed.
Several functions have been attributed to the MNS. It is argued that the
MNS is responsible for goal inference, and could be involved in learning by imitation.
Here we investigated an existing neural network model which exhibits
mirror properties, the RecurrentNeuralNetworkwith Parametric Bias (RNNPB).
However, this model fails to account for goal inference, when the body of the
actor and observer are dissimilar. The RNNPB model is extended such that it is
able to explain this general goal inference. This is achieved by ignoring bodily
parameters as a direct input to the model, and instead use the perceived state
of the world as a body-invariant input for the model.
First, the RNNPB model is tested for robustness against noise and parametric
noise in the original signals. It is demonstrated that the model is able to generalize,
which means that the extended RNNPB model does not demand strict
requirements on the features used for representing the state of the world. Furthermore,
we demonstrated the present model in a toy world example, in which
it is shown that observation of multiple goal-directed actions and generation of
a goal directed action achieving the same goal both leads to equal activation of
an additional input layer of the network. This layer contains the so called parametric
bias nodes, which is an essential part of the RNNPB architecture. This
exemplifies the goal related mirror properties of the model.
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Faculteit der Sociale Wetenschappen