Optimality: From Neural Networks to Universal Grammar
Alan Prince
*
and
Paul Smolensky
*
Can concepts from the theory of neural computation contribute to
formal theories of the mind? Recent research has explored the
implications of one principle of neural computation, optimization, for
the theory of grammar. Optimization over symbolic linguistic structures
provides the core of a new grammatical architecture, optimality theory.
The proposition that grammaticality equals optimality sheds light on a
wide range of phenomena, from the gulf between production and
comprehension in child language, to language learnability, to the
fundamental questions of linguistic theory: What is it that the
grammars of all languages share, and how may they differ?
A. Prince is in the Department of Linguistics and Rutgers Center
for Cognitive Science, Rutgers University, 18 Seminary Place, New
Brunswick, NJ 08903, USA. E-mail:
prince{at}ruccs.rutgers.edu P. Smolensky is in the
Department of Cognitive Science, Johns Hopkins University, 3400 North
Charles Street, Baltimore, MD 21218-2685, USA. E-mail:
smolensky{at}cogsci.jhu.edu
*
Both authors contributed equally to this work.
