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Updated: May 21, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Formal language theory: refining the Chomsky hierarchy.

Gerhard Jäger¹, James Rogers

¹Department of Linguistics, University of Tuebingen, Wilhelmstrasse 19, Tuebingen 72074, Germany. gerhard.jaeger@uni-tuebingen

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|June 13, 2012

Summary

This summary is machine-generated.

The Chomsky hierarchy

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Last Updated: May 21, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

Computational linguistics
Cognitive science

Background:

The Chomsky hierarchy classifies formal languages based on grammatical complexity.
Regular and context-free grammars are insufficient for natural language syntax.

Purpose of the Study:

To review the limitations of traditional Chomsky hierarchy levels for natural language.
To introduce refined linguistic frameworks relevant to cognitive science.

Main Methods:

Overview of the Chomsky hierarchy, focusing on regular and context-free languages.
Discussion of arguments against their sufficiency for natural language syntax.
Review of mildly context-sensitive languages and the sub-regular hierarchy.

Main Results:

Regular and context-free grammars cannot fully account for natural language phenomena.
Mildly context-sensitive languages offer greater expressive power than context-free languages.
The sub-regular hierarchy provides finer distinctions within regular languages.

Conclusions:

Existing formal language theories require refinement to model natural language adequately.
Mildly context-sensitive and sub-regular frameworks offer more promising avenues for cognitive science research.
These refined hierarchies better capture the complexity of human language processing.