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Cognitive Science,
23, 157-205.
Toward a connectionist model of recursion in human
linguistic performance
Morten H. Christiansen
Nick Chater
Abstract
Naturally occurring speech contains only a limited amount of complex
recursive structure, and this is reflected in the empirically
documented difficulties that people experience when processing such
structures. We present a connectionist model of human performance in
processing recursive language structures. The model is trained on
simple artificial languages. We find that the qualitative performance
profile of the model matches human behavior, both on the relative
difficulty of center-embedded and cross-dependency, and between the
processing of these complex recursive structures and right-branching
recursive constructions. We analyze how these differences in
performance are reflected in the internal representations of the model
by performing discriminant analyses on these representation both
before and after training. Furthermore, we show how a network trained
to process recursive structures can also generate such structures in a
probabilistic fashion. This work suggests a novel explanation of
people's limited recursive performance, without assuming the existence
of a mentally represented competence grammar allowing unbounded
recursion.
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