Artificial grammar learning meets formal language theory: an overview

Area of Science:

• Cognitive Science
• Computational Linguistics
• Neuroscience

Background:

• Formal language theory (FLT), a branch of the mathematical theory of computation, provides systematic tools for describing rule-based generative systems.
• FLT's applications extend beyond human language to computer science, music, visual patterns, animal communication, RNA structure, and dance.
• The artificial grammar-learning paradigm has increasingly utilized FLT for hypothesis framing and experimental design in cognitive research.

Purpose of the Study:

• To provide an accessible introduction to formal language theory (FLT).
• To analyze empirical research employing FLT, identifying challenges and potential advancements.
• To clarify distinct concepts within FLT, such as hierarchy, dependency, complexity, and recursion, and their experimental validation.

Main Methods:

• Review of formal language theory principles and their application in cognitive science.
• Analysis of empirical studies, particularly those using the artificial grammar-learning paradigm.
• Examination of neuroimaging literature that incorporates formal language frameworks.

Main Results:

• Progress in FLT-based research may be hindered by conflation of key concepts like hierarchy and recursion.
• Specific hypotheses and experimental designs are proposed for rigorous testing of FLT-related cognitive phenomena.
• A review of brain imaging studies reveals areas of consensus and ongoing debate regarding FLT's role in cognition.

Conclusions:

• Formal language theory offers significant potential for rigorous empirical investigations into human cognition.
• FLT provides a valuable perspective for neuroscientific and comparative studies of cognitive processes.
• Further clarification of FLT concepts and refined experimental designs are crucial for advancing the field.