Proceedings of the Annual Meeting of the Cognitive Science Society

The principal aim of a cognitive model is to infer the processby which the human mind acts on some select set ofenvironmental inputs such that it produces the observed set ofbehavioral outputs. In this endeavor, one of the centralrequirements is that the input to the model be represented asfaithfully and accurately as possible. However, this is ofteneasier said than done. In the study of recognition memory, forinstance, words are the environmental input of choice—yetbecause words vary on many different dimensions, andbecause the problem of quantifying this variation has longbeen out of reach, modelers have tended to rely on idealized,randomly generated representations of their experimentalstimuli. In this paper, we introduce new resources from large-scale text mining that may improve upon this practice,illustrating a simple method for deriving feature informationdirectly from word pools and lists.