Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience

TL;DR

This paper argues that Vector Symbolic Architectures can address Jackendoff's challenges regarding neural instantiation of linguistic combinatoriality, providing a promising connectionist approach to modeling language processing in the brain.

Contribution

It demonstrates that Vector Symbolic Architectures can meet key linguistic and cognitive challenges that traditional connectionist models fail to address.

Findings

Vector Symbolic Architectures can represent complex compositional structures.

These models can perform rapid construction and transformation of symbolic representations.

They offer a viable neural implementation for linguistic combinatoriality.

Abstract

Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language present to theories of brain function. The essence of these problems is the question of how to neurally instantiate the rapid construction and transformation of the compositional structures that are typically taken to be the domain of symbolic processing. He contended that typical connectionist approaches fail to meet these challenges and that the dialogue between linguistic theory and cognitive neuroscience will be relatively unproductive until the importance of these problems is widely recognised and the challenges answered by some technical innovation in connectionist modelling. This paper claims that a little-known family of connectionist models (Vector Symbolic Architectures) are able to meet Jackendoff's challenges.