A Mirror Neuron System for Syntax Acquisition

Steve Womble and Stefan Wermter
University of Sunderland, Centre of Informatics,
St. Peter's Way, Sunderland, SR6 0DD. United Kingdom
www.his.sunderland.ac.uk

Abstract. We investigate the use of a connectionist model of a mirror
neuron cortical network for a context free syntax acquisition task. A finite
state representation of the context free grammar is learned by an implicit
knowledge system (IKS) modelled by a connectionist network. A mirror
neuron system (MNS) whose evolutionary pedigree suggests adaptation
for goal-directed sequential processing is used to track embedded recursions 
in a learned finite state model of the grammar. The mirror system
modifies the output of the IKS depending on the depth of embedding.
Reciprocally the IKS updates the MNS as natural `goals' occur within
a sequence during sentence production. This solves the computationally
hard problem of inferring contexts from sequential input.

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