Abstract
The cognitive science that emerged in the 1970s was based mainly on the serial information processing paradigm of artificial intelligence (AI) and the symbol-manipulation approach to linguistics, and had rather little contact with work in brain theory. However, there is now a growing interest in what is called the connectionist approach or Parallel Distributed Processing (PDP), the study of ways in which simple units may be interconnected to solve hard problems. This approach may to some extent be characterized as a reaction against the domination of AI by the paradigms of serial computation and, in some cases, explicit symbolic structures; but it can also be seen as the result of probing the microstructure of “symbols” and thus stressing the parallel processes underlying behavior. Of course, to the reader of this book, the approach is also a direct continuation of the work on adaptive pattern-recognition networks that we have sampled in Chapter 4.
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Arbib, M.A. (1987). Learning Networks. In: Brains, Machines, and Mathematics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4782-1_5
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DOI: https://doi.org/10.1007/978-1-4612-4782-1_5
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