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Related Experiment Video

Updated: Jun 26, 2026

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

Connectionist semantic systematicity.

Stefan L Frank1, Willem F G Haselager, Iris van Rooij

  • 1Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, P.O. Box 9104, 6500 HE, Nijmegen, The Netherlands. S.L.Frank@uva.nl

Cognition
|January 13, 2009
PubMed
Summary
This summary is machine-generated.

This study presents a connectionist model demonstrating systematicity in sentence comprehension without relying on classical symbol systems. The recurrent neural network learns to map sentences to situations, generalizing to novel sentences and underscoring the role of world structure.

Related Experiment Videos

Last Updated: Jun 26, 2026

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Fodor and Pylyshyn argued connectionist models require symbol systems for systematicity.
  • This challenges connectionism's viability as a cognitive architecture framework.

Purpose of the Study:

  • To present a connectionist model exhibiting systematicity without implementing a symbol system.
  • To challenge the Fodor-Pylyshyn argument regarding connectionism and cognitive architecture.

Main Methods:

  • Developed a recurrent neural network for sentence comprehension.
  • Trained the model on sentence-situation pairs in a microworld.
  • Evaluated the model's ability to generalize to novel sentences and situations.

Main Results:

  • The connectionist model demonstrated systematicity in sentence comprehension.
  • The model successfully mapped novel sentences to new situations after training.
  • Systematicity emerged robustly and plausibly, dependent on world structure.

Conclusions:

  • Connectionist models can achieve systematicity without classical symbol systems.
  • Psychologically plausible systematicity can arise from inherent world structure.
  • This work offers an alternative architectural framework for human cognition.