ch9/

dyslex simulates normal and disordered (dyslexic) reading performance in terms of a distributed representation of word-level knowledge across Orthography, Semantics, and Phonology.

sem is trained using Hebbian learning on paragraphs from an early draft of the *Computational Explorations..* textbook, allowing it to learn about the overall statistics of when different words co-occur with other words, and thereby learning a surprisingly capable (though clearly imperfect) level of semantic knowlege about the topics covered in the textbook.

sg is the sentence gestalt model, which learns to encode both syntax and semantics of sentences in an integrated "gestalt" hidden layer.

ss explores the way that regularities and exceptions are learned in the mapping between spelling (orthography) and sound (phonology), in the context of a "direct pathway" mapping between these two forms of word representations.