Proceedings of the Annual Meeting of the Cognitive Science Society

A single view of an unfamiliar object typically provides enough information about the object's shape to permit recognition from a wide range of n e w viewpoints. A recent model by H u m m e l and Biederman (1990, 1992) provides a partial account of this ability in terms of the activation of viewpoint invariant structural descriptions of (even unfamiliar) objects. W e describe the Structural Description Encoder (SDE), a self-organizing feed-forward neural network that learns such descriptions in one or at most two exposures. Rapid, reliable learning results from the interactions among recruited and unrecruited units, whose response characteristics are differentiated through the use of dynamic thresholds and learning rates.