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A simple recurrent network model of sequence prediction (based on Cleeremans & McClelland [32]). (a) The network is predicting A or B as the next output, but instead, C is the actual output. This results in prediction error, which drives learning and leads, for example, to an increase in connection weights from units representing the current context to the output for C (b).
