We present a modified version of Schmajuk and Thieme's (1992) neural network model of spatial navigation. The new model differs from the original in several ways. First, whereas the early model assumed no a priori knowledge of the space to be explored, the present model assumes a repre sentation of the environment as a set of potentially connected locations. Second, whereas in the original model the decision as to what place to move to next is based on the comparison of the predictions of the goal when each of the alternative places is briefly entered; in the present paper this decision is based on the comparison of the activation of each of the alternative places when the goal is activated. Computer simulations show that the present network offers a novel descrip tion of latent learning in terms of the competition between exploration and exploitation.
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