A note on Computational Models of Psycholinguistics
I would like to sum up a clear synthesis and state of the art of scientific traditions and ways to deal with language features as a whole. In a chapter entitled ‘Computational Models of Psycholinguistics’ and published in the Cambridge Handbook of Psycholinguistics, Nick Chater and Morten H. Christiansen distinguish three main traditions in psycholinguistic language modeling :
- a symbolic (Chomskyan) tradition
- connectionnist psycholinguistics
- probabilistic models
They state that the Chomskyan approach (as well as nativist theories of language in general) outweighed until recently by far any other one, setting the ground for cognitive science :
“Chomsky’s arguments concerning the formal and computational properties of human language were one of the strongest and most influential lines of argument behind the development of the field of cognitive science, in opposition to behaviorism.” (p. 477)
The Symbolic Tradition
They describe the derivational theory of complexity (the hypothesis that number and complexity of transformations correlate with processing time and difficulty) as proving ‘a poor computational model when compared with empirical data’ (p. 479). Further work on generative grammar considered the relationship between linguistic theory and processing as indirect, this is how they explain that this Chomskyan tradition progressively disengaged from work on computational modeling …
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