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Optimality: from neural networks to universal grammar

A Prince¹, P Smolensky

¹Department of Linguistics and Rutgers Center for Cognitive Science, Rutgers University, 18 Seminary Place, New Brunswick, NJ 08903, USA. prince@ruccs.rutgers.edu

Science (New York, N.Y.)

|March 14, 1997

Summary

Neural computation principles, specifically optimization, form the basis of optimality theory, a new grammatical framework. This theory suggests grammaticality is equivalent to optimality, explaining linguistic phenomena and language universals.

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Area of Science:

Cognitive Science
Linguistics
Computational Neuroscience

Background:

The theory of neural computation offers principles applicable to formalizing mental processes.
Optimization is a key principle in neural computation with potential implications for grammar.

Purpose of the Study:

To explore how concepts from neural computation, particularly optimization, can inform formal theories of the mind.
To introduce optimality theory as a new grammatical architecture based on optimization over symbolic linguistic structures.

Main Methods:

Applying the principle of optimization to symbolic linguistic structures.
Developing a new grammatical architecture, optimality theory, grounded in this optimization principle.

Main Results:

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The proposition that grammaticality is equivalent to optimality provides a novel perspective on linguistic theory.
This framework sheds light on diverse phenomena including child language production and comprehension, and language learnability.

Conclusions:

Optimality theory offers a formal framework for understanding grammatical structures based on computational principles.
This approach addresses fundamental questions regarding linguistic universals and language variation.