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Suppose, for instance, that all the features of the letter 'E' are presented (this actually only works well when the absence of a feature is also coded by the activation of some feature node). The letter 'F shares many of its line features with 'E'. Both letter representation nodes in the second layer will get activated, but not equally strong. Because the 'E' node has a higher activation than the 'F' node, it will give off more inhibition than it receives. For the 'F' node it will be the other way around.

The internal state is usually characterized by a single unidimensional variable called the activation. , above two) the models appear to shift from a connectionist to a symbolic framework. The (real-valued) activation of node i will be denoted by ai here. An important division between network models is between models with discrete, usually binary, activation values, and models with continuously valued, graded, activations. The The connectionist language 23 former type waS representative (with a few notable exceptions) for the first wave of network models in the fifties and sixties, whereas the latter is characteristic for many of the models of the last decade.

Part 3 will describe the kind of empirical data that are used here to constrain connectionist models. A number of experiments will be presented which lead to a few assumptions that can be readily implemented in a connectionist model. A specific model based on the building block of part 2 and the interpretation of part 3 will be presented in part 4. With this model a number of the experiments from part 3 and from other sources will be simulated. The adequacy of this model and possible extensions to it will also be discussed in this part.

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AGS Experiments [1994-1996]

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