Cogprints

Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience

Gayler, Dr Ross W. (2003) Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience. [Conference Paper] (Unpublished)

Full text available as:

[img]
Preview
PDF
58Kb

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.

Item Type:Conference Paper
Additional Information:This is a slightly updated version of the refereed paper presented at the conference.
Keywords:connectionist compositionality binding
Subjects:Computer Science > Language
Neuroscience > Computational Neuroscience
Computer Science > Artificial Intelligence
ID Code:3983
Deposited By:Gayler, Dr Ross
Deposited On:11 Dec 2004
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Anderson, J. A., Silverstein, J. W., Ritz, S. A., & Jones, R. S. (1977). Distinctive features, categorical perception, and probability learning: Some applications of a neural model. Psychological Review, 84, 413-451.

Arathorn, D. W. (2002). Map-seeking circuits in visual cognition: A computational mechanism for biological and machine vision. Stanford, CA, USA: Stanford University Press.

Bod, R. & Scha, R. (1997). Data-oriented language processing: An overview. In S. Young & G. Bloothooft (Eds.) Corpus-based methods in language and speech processing. Boston, MA, USA: Kluwer Academic Publishers. (http://turing.wins.uva.nl/~rens/overview.ps)

Copestake, A. (2002). Implementing typed feature structure grammars. Stanford, CA, USA: CSLI Publications.

Feldman, J. (2002, August). Neural binding. Connectionists Mailing List [On-line discussion group], 5th August, 2002. (http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/connect/connect-archives/arch.2002-08.gz see 0005.txt and also 8, 9, 18, & 21)

Gayler, R. W. (1998). Multiplicative binding, representation operators, and analogy [Abstract of poster]. In K. Holyoak, D. Gentner & B. Kokinov (Eds.), Advances in analogy research: Integration of theory and data from the cognitive, computational, and neural sciences. Sofia, Bulgaria: New Bulgarian University. (http://cogprints.ecs.soton.ac.uk/archive/00000502/ for full poster)

Gayler, R. W., & Wales, R. (1998). Connections, binding, unification, and analogical promiscuity. In K. Holyoak, D. Gentner & B. Kokinov (Eds.), Advances in analogy research: Integration of theory and data from the cognitive, computational, and neural sciences. Sofia, Bulgaria: New Bulgarian University. (http://cogprints.ecs.soton.ac.uk/archive/00000500/ see also 501)

Gayler, R. W., & Wales, R. (2000, February). Multiplicative binding, representation operators and analogical inference. Paper presented at the 5th Australasian Cognitive Science Conference, Melbourne, Australia.

Jackendoff, R. (2002). Foundations of language: Brain, meaning, grammar, evolution. Oxford, UK: Oxford University Press.

Kanerva, P. (1997). Fully distributed representation. In Proceedings of 1997 Real World Computing Symposium (RWC'97, Tokyo). Tsukuba-city, Japan: Real World Computing Partnership. (http://www.rni.org/kanerva/rwc97.ps.gz)

Marcus, G. (2001). The algebraic mind. Cambridge, MA, USA: MIT Press.

Plate, T. A. (1994). Distributed representations and nested compositional structure. Doctoral dissertation. Department of Computer Science, University of Toronto, Toronto, Canada. (http://pws.prserv.net/tap/papers/plate.thesis.ps.gz)

Plate, T. A. (1997). A common framework for distributed representation schemes for compositional structure. In F. Maire, R. Hayward & J. Diederich (Eds.) Connectionist systems for knowledge representation and deduction. Brisbane, Australia: Queensland University of Technology Press. (http://pws.prserv.net/tap/papers/drep-framework.ps.gz)

Rachkovskij, D. A., & Kussul, E. M. (2001). Binding and normalization of binary sparse distributed representations by Context-Dependent Thinning. Neural Computation, 13(2), 411-452. (http://cogprints.ecs.soton.ac.uk/archive/00001240/)

Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159-216.

Metadata

Repository Staff Only: item control page