Proceedings of the Annual Meeting of the Cognitive Science Society

The connectionist model lAK (Information evaluation using configurations) for classification learning is presented here. The model can be placed between feature based (e.g. Gluck & Bower, 1988) and exemplar based models (e.g. ALCOVE , Kruschke, 1992). Specific to this model is that during learning, sets of input features are probabilistically sampled. These sets are represented, in a hidden layer, by configuration nodes. These configuration nodes are connected to output nodes that represent category labels. A further characteristic of the lAK model is a mechanism which enhances retrieval of information. Simulations with the lAK model can explain different phenomena of classification learning which have been found in experimental studies: A Type 2 advantage without dimensional attention learning observed by Shepard et al. (1961); a generalisation of prototypes; a generalization based on similarity to learned exemplars; a differential forgetting of prototypes and exemplars; a moderate interference (fan effect) caused by stimulus similarity; and the missing of catastrophic interference even in A-B/A-Brdesigns.