Language taps the coordinated function of many of the brain areas discussed above. Language requires highly sophisticated perceptual abilities, to be able to discriminate different speech sounds and different letters and combinations thereof (just listen and look at an unfamiliar foreign language to experience how amazing your own native language perceptual abilities are, which you take for granted). Likewise, sophisticated motor output abilities are required to produce the sounds and write the letters and words of language. In between, language requires some of the most demanding forms of cognitive processing, to keep track of the grammatical and semantic information streaming past you, often at high speed. This requires sophisticated executive function and working memory abilities, in addition to powerful distributed posterior-cortical semantic representations to integrate all the semantic information.
Our exploration of language starts with a small-scale model of reading, that interconnects orthographic (writing), phonological (speech), and semantic representations of individual words, to form a distributed lexicon -- there isn't one place where all word information is stored -- instead it is distributed across brain areas that are specialized for processing the relevant perceptual, motor, and semantic information. Interestingly, we can simulate various forms of acquired dyslexia (e.g., from stroke or other forms of brain injury) by damaging specific pathways in this model, providing an important way of establishing neural correlates of language function in humans, where invasive experiments are not possible.
We then zoom in on the orthography to phonology pathway to explore issues with regularities and exceptions in this spelling-to-sound mapping, which has been the topic of considerable debate. We show that the object recognition model from the perception chapter has an important blend of features that support both regular and exception mappings, and this model pronounces nonword probe inputs much like people do, demonstrating that it has extracted similar underlying knowledge about the English mapping structure.
Next, we zoom in on the semantics pathway, exploring how a self-organizing network can learn to encode statistical regularities in word co-occurance, that give rise to semantic representations that are remarkably effective in capturing the similarity structure of words. We train this network on an early draft of the first edition of this text, so you should be familiar with the relevant semantics!
Finally, we tackle the interactions between syntax and semantics in the context of processing the meaning of sentences, using the notion of a sentence gestalt representation, that uses coarse-coded distributed representations to encode the overall meaning of the sentence, integrating both syntactic and semantic cues. This is a distinctly neural approach to syntax, as contrasted with the symbolic, highly structured approaches often employed in linguistic theories.