Proceedings of the Annual Meeting of the Cognitive Science Society

Symbolic and associative theories ha\e l)ccn claimed to be able to account for concept learning from examples. Given that there seems to be enough empirical evidence supiwrting both claims, we have tried to integrate associative and symbolic Tomiulations into a single com|Mitational model that abstracts infonmation Trom empirical data at the same time that it takes into account the strength with which each hypothesis is associated with lewanL The model is tested in a simulation of pigeon data in a fuzz\ concept learning task, where only a few abstractions are stored in representation of ail the training patterns and strengthcd or weakened depending on their predictive value.