DeepAI
Log In Sign Up

Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience

12/13/2004
by   Ross W. Gayler, et al.
0

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.

READ FULL TEXT
01/29/2021

Does injecting linguistic structure into language models lead to better alignment with brain recordings?

Neuroscientists evaluate deep neural networks for natural language proce...
11/14/2019

Radically Compositional Cognitive Concepts

Despite ample evidence that our concepts, our cognitive architecture, an...
01/02/2017

Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation

During the last decades, many cognitive architectures (CAs) have been re...
01/24/2018

Multi-optional Many-sorted Past Present Future structures and its description

The cognitive theory of true conditions (CTTC) is a proposal to describe...
12/08/2022

A paradigm shift in neuroscience driven by big data: State of art, challenges, and proof of concept

A recent editorial in Nature noted that cognitive neuroscience is at a c...
12/31/2021

Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences

Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architec...
11/22/2020

Modelling Compositionality and Structure Dependence in Natural Language

Human beings possess the most sophisticated computational machinery in t...