The Neural Representation of Sequences: From Transition Probabilities to Algebraic Patterns and Linguistic Trees

Area of Science:

• Cognitive Neuroscience
• Neuroscience
• Computational Neuroscience

Background:

• Sequences are fundamental to cognition, involving processing of items and their temporal order.
• Existing models capture various aspects of sequence coding, but a unified framework is lacking.

Purpose of the Study:

• To propose a comprehensive taxonomy of five distinct cerebral mechanisms for sequence coding.
• To review experimental paradigms and identify behavioral and neural signatures for each mechanism.

Main Methods:

• Literature review of experimental paradigms for sequence coding.
• Analysis of behavioral and neural data associated with sequence processing.
• Theoretical proposal of a five-tiered taxonomy of sequence coding mechanisms.

Main Results:

• Identified five distinct cerebral mechanisms: transitions and timing, chunking, ordinal knowledge, algebraic patterns, and nested tree structures.
• Detailed the experimental evidence and neural correlates for each mechanism.
• Highlighted the unique requirements of nested tree structures, including a recursive neural code.

Conclusions:

• The proposed taxonomy offers a framework for understanding diverse sequence coding strategies in the brain.
• Nested tree structures, potentially unique to humans, may rely on an unidentified recursive neural code.
• This recursive code could be key to understanding the singularity of human language and cognition.