cognitive - In the beginning, there was language, and it...

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In the beginning, there was language, and it was good. Then two eminent scholars debated its origin, and this debate gave rise to the Cognitive Revolution . . . 1 Like most approaches to the mind, cognitive science can be traced back to philosophical questions, especially about the nature of knowledge. For instance, Plato 's dialog Meno , where he investigates the source of knowledge, can be seen as a foundational text. The modern culture of cognitive science can be traced back to the early cyberneticists in the 1930s and 1940s, such as Warren McCulloch and Walter Pitts , who sought to understand the organizing principles of the mind . McCulloch and Pitts essentially invented the neural network , but did not have the computational tools to develop it into modern form. Another precursor was the early development of the theory of computation and the digital computer in the 1940s and 1950s. Alan Turing and John von Neumann were instrumental in these developments. The modern computer, or Von Neumann machine , would play a central role in cognitive science, both as a metaphor for the mind, and as a tool for investigation. In 1959 , Noam Chomsky published a scathing review of B. F. Skinner 's book Verbal Behavior . At the time, Skinner's behaviorist paradigm dominated psychology: Most psychologists focused on functional relations between stimulus and response, without positing internal representations. Chomsky's work showed that in order to explain language, we needed a theory like his generative grammar , which not only attributed internal representations but characterized their underlying order. This hugely successful theory would inspire much later cognitive science. In the 1970s and early 1980s, much cognitive science research focused on the possibility of artificial intelligence . Researchers such as Marvin Minsky would write computer programs in languages such as LISP to attempt to formally characterize the steps that human beings went through, for instance, in making decisions and solving problems, in the hope of better understanding human thought, and also in the hope of creating artificial minds. This approach is known as "symbolic AI". Eventually the limits of the symbolic AI research program became apparent. For instance, it seemed to be unrealistic to comprehensively list human knowledge in a form usable by a symbolic computer program. The late 80s and 90s saw the rise of neural networks and connectionism as a research paradigm. Under this point of view, often attributed to James McClelland and David Rumelhart , the mind could be characterized as a set of complex associations, represented as a layered network. Critics argue that there are some phenomena which are better captured by symbolic models, and that connectionist models are often so complex as to have little explanatory power. But the flexibility and biological plausibility of these models made them very successful. Today a plurality of approaches exist, from connectionism, to a focus on dynamical systems
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This note was uploaded on 09/23/2009 for the course PSYC 407 taught by Professor Amponsah during the Spring '09 term at American InterContinental University Illinois.

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cognitive - In the beginning, there was language, and it...

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