Download Algebraic Mind: Integrating Connectionism and Cognitive by Gary F. Marcus PDF

By Gary F. Marcus

An try to combine theories approximately how the brain works, person who says that the brain is a computer-like manipulator of symbols, and one other that says that the brain is a big community of neurons operating jointly in parallel.

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Multiplenodes-per-variable models can learn UQOTOMs, and they can learn arbitrary mappings. But what they learn depends on the nature of the learning algorithm. Back-propagation—the learning algorithm most commonly used—does not allocate special status to UQOTOMs. Instead, a many-nodes-per-variable multilayer perceptron that is trained by back-propagation can learn a UQOTOM—such as identity, multiplication, or concatenation—only if it sees that UQOTOM illustrated with respect to each possible input and output node.

In the bottom panel, both the number of weights and the distance from the fulcrum are encoded in distributed fashion, using banks of nodes. 0 the 3 node in the bank of weights representing the number of weights. Hidden units and output units are not shown. localist and distributed representations. While all models that use distributed representations allocate more than one variable per node, it is not the case that all localist models allocate a single node per variable. In fact, most localist models allocate more than one node per variable.

But what I want to do in this brief section is to persuade you that it is not terribly valuable to think of the difference between competing accounts of cognitive architecture as hinging on whether the mind represents symbols. The trouble is that there are too many different ways of defining what is meant by a symbol. It is certainly possible to define the term symbol in a way that means that symbol-manipulators have them and multilayer perceptrons do not, but it is just as easy to define the term in a way that entails that both symbol-manipulators and multilayer perceptrons have them.

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