Connectionist semantic systematicity.
Fodor and Pylyshyn [Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71] argue that connectionist models are not able to display systematicity other than by implementing a classical symbol system. This claim entails that connectionism cannot compete with the classical approach as an alternative architectural framework for human cognition. We present a connectionist model of sentence comprehension that does not implement a symbol system yet behaves systematically. It consists in a recurrent neural network that maps sentences describing situations in a microworld, onto representations of these situations. After being trained on particular sentence-situation pairs, the model can comprehend new sentences, even if these describe new situations. We argue that this systematicity arises robustly and in a psychologically plausible manner because it depends on structure inherent in the world.
Frank
Stefan L
SL
Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, P.O. Box 9104, 6500 HE, Nijmegen, The Netherlands. S.L.Frank@uva.nl
Haselager
Willem F G
WF
van Rooij
Iris
I
D000465:Algorithms; D001185:Artificial Intelligence; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007802:Language; D008960:Models, Psychological; D016571:Neural Networks, Computer; D010551:Personality; D011578:Psycholinguistics; D012660:Semantics