Proceedings of the Annual Meeting of the Cognitive Science Society

Understanding how we acquire semantic knowledge is a central topic in cognitive science. In a now classic paper, Rogersand McClelland (2003) explored how a parallel distributed processing (PDF) model could recreate several important phe-nomena in semantic memory including how concepts are acquired, lost due to semantic dementia, and become organizedhierarchically. One well known limitation of this model, which was acknowledge by the original authors, is that thefeatures used in the model were largely hand coded. In this project we revisit this classic PDP account in light of mod-ern advances in neural network techniques. In particular, we show that we can recreate several of the predictions of theRogers and McClelland (2003) model in a network trained directly on raw pixel information from category exemplars.These results add realism to the original model while also showing how the principles of the model generalize to higherdimensional input spaces.