A Connectionist Model Of Instruction Following
In this paper we describe a general connectionist model of “learning by being told”. Unlike common network models of inductive learning which rely on the slow modification of connection weights, our model of instructed learning focuses on rapid changes in the activation state of a recurrent network. We view stable distributed patterns of activation in such a network as internal representations of provided advice – representations which can modulate the behavior of other networks. We suggest that the stability of these configurations of activation can arise over the course of learning an instructional language and that these stable patterns should appear as articulated attractors in the activation space of the recurrent network. In addition to proposing this general model, we also report on the results of two computational experiments. In the first, networks are taught to respond appropriately to direct instruction concerning a simple mapping task. In the second,networks receive instructions describing procedures for binary arithmetic, and they are trained to immediately implement the specified algorithms on pairs of binary numbers. While the networks in these preliminary experiments were not designed to embody the attractor dynamics inherent in our general model, they provide support for this approach by demonstrating the ability of recurrent backpropagation networks to learn an instructional language in the service of some task and thereafter exhibit prompt instruction