Proceedings of the Annual Meeting of the Cognitive Science Society

Elman (1990) proposed a connectionist architecture for the representation of temporal relationships. This approach is applied to the modeling of serial conditioning. El man's basic simple recurrent network (SRN) was modified to focus its attention on the prediction of important events (Unconditioned Stimuli, or USs) by limiting the connection weights for other events (the Conditioned Stimuli, or CSs). With this modification, the model exhibited blocking and serial conditioning to sequential stimulus compounds. A n exploration of the underlying mechanisms suggests that event terminations (CS offsets) were used in predicting U S occurrences following simple trace conditioning and event beginnings (CS onsets) were more important following serial conditioning. The results held true under a series of learning rate and momentum values.