Distributed representations of semantic knowledge in the brain

Steven L. Small, John Hart, Tran Nguyen, Barry Gordon

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

Category-specific language impairments have been postulated to require the existence of an explicit category organization within semantic memory. However, it may be possible to demonstrate analytically that this is not necessary. We hypothesize that category-specific organization can emerge from perceptual, functional, and associative feature information about objects that is maintained in order to process language. In this paper, we conduct several experiments to test the computational validity of this hypothesis. Physical objects were encoded in terms of semantic features, based on basic perceptual and motor modalities and higher level knowledge of function, for use in artificial neural networks. Mathematical methods were used to analyse the encodings and the neural networks. The results demonstrate the emergence of semantic categories in the networks. although such information was not preprogrammed. We conclude that category-specific language organization can emerge from the inherent nature of semantic features themselves, and does not require special internal categorical organization of semantic memory.

Original languageEnglish (US)
Pages (from-to)441-453
Number of pages13
JournalBrain
Volume118
Issue number2
DOIs
StatePublished - Apr 1995

Fingerprint

Semantics
Brain
brain
neural networks
Language
Neural networks
Data storage equipment
Categorical
Modality
Demonstrate
Artificial Neural Network
Encoding
Knowledge
Neural Networks
Internal
testing
Necessary
Experiment
methodology
Experiments

Keywords

  • Language
  • Memory
  • Model
  • Network
  • Temporal lobe

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Mathematics(all)
  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • Clinical Neurology
  • Neuroscience(all)

Cite this

Distributed representations of semantic knowledge in the brain. / Small, Steven L.; Hart, John; Nguyen, Tran; Gordon, Barry.

In: Brain, Vol. 118, No. 2, 04.1995, p. 441-453.

Research output: Contribution to journalArticle

Small, Steven L. ; Hart, John ; Nguyen, Tran ; Gordon, Barry. / Distributed representations of semantic knowledge in the brain. In: Brain. 1995 ; Vol. 118, No. 2. pp. 441-453.
@article{0bc550360e3f449f814cae2295c0b81e,
title = "Distributed representations of semantic knowledge in the brain",
abstract = "Category-specific language impairments have been postulated to require the existence of an explicit category organization within semantic memory. However, it may be possible to demonstrate analytically that this is not necessary. We hypothesize that category-specific organization can emerge from perceptual, functional, and associative feature information about objects that is maintained in order to process language. In this paper, we conduct several experiments to test the computational validity of this hypothesis. Physical objects were encoded in terms of semantic features, based on basic perceptual and motor modalities and higher level knowledge of function, for use in artificial neural networks. Mathematical methods were used to analyse the encodings and the neural networks. The results demonstrate the emergence of semantic categories in the networks. although such information was not preprogrammed. We conclude that category-specific language organization can emerge from the inherent nature of semantic features themselves, and does not require special internal categorical organization of semantic memory.",
keywords = "Language, Memory, Model, Network, Temporal lobe",
author = "Small, {Steven L.} and John Hart and Tran Nguyen and Barry Gordon",
year = "1995",
month = "4",
doi = "10.1093/brain/118.2.441",
language = "English (US)",
volume = "118",
pages = "441--453",
journal = "Brain",
issn = "0006-8950",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Distributed representations of semantic knowledge in the brain

AU - Small, Steven L.

AU - Hart, John

AU - Nguyen, Tran

AU - Gordon, Barry

PY - 1995/4

Y1 - 1995/4

N2 - Category-specific language impairments have been postulated to require the existence of an explicit category organization within semantic memory. However, it may be possible to demonstrate analytically that this is not necessary. We hypothesize that category-specific organization can emerge from perceptual, functional, and associative feature information about objects that is maintained in order to process language. In this paper, we conduct several experiments to test the computational validity of this hypothesis. Physical objects were encoded in terms of semantic features, based on basic perceptual and motor modalities and higher level knowledge of function, for use in artificial neural networks. Mathematical methods were used to analyse the encodings and the neural networks. The results demonstrate the emergence of semantic categories in the networks. although such information was not preprogrammed. We conclude that category-specific language organization can emerge from the inherent nature of semantic features themselves, and does not require special internal categorical organization of semantic memory.

AB - Category-specific language impairments have been postulated to require the existence of an explicit category organization within semantic memory. However, it may be possible to demonstrate analytically that this is not necessary. We hypothesize that category-specific organization can emerge from perceptual, functional, and associative feature information about objects that is maintained in order to process language. In this paper, we conduct several experiments to test the computational validity of this hypothesis. Physical objects were encoded in terms of semantic features, based on basic perceptual and motor modalities and higher level knowledge of function, for use in artificial neural networks. Mathematical methods were used to analyse the encodings and the neural networks. The results demonstrate the emergence of semantic categories in the networks. although such information was not preprogrammed. We conclude that category-specific language organization can emerge from the inherent nature of semantic features themselves, and does not require special internal categorical organization of semantic memory.

KW - Language

KW - Memory

KW - Model

KW - Network

KW - Temporal lobe

UR - http://www.scopus.com/inward/record.url?scp=0028933077&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028933077&partnerID=8YFLogxK

U2 - 10.1093/brain/118.2.441

DO - 10.1093/brain/118.2.441

M3 - Article

C2 - 7735885

AN - SCOPUS:0028933077

VL - 118

SP - 441

EP - 453

JO - Brain

JF - Brain

SN - 0006-8950

IS - 2

ER -