The underlying assumption in much of categorization research is that effects such as typicality are reflective of stored conceptual structure. This paper questions this assumption by simulating typicality effects by the use of a distributional model of language, Latent Semantic Analysis (LSA). Despite being a statistical tool based on simple word co-occurrence, LSA successfully simulates participant data relating to typicality effects and the effects of context on categories. Moreover, it does so without any explicit coding of categories or semantic features. In the light of the findings reported here, we question the traditional interpretation of typicality data: are these data reflective of underlying structure in people's concepts, or are they reflective of the distributional properties of the linguistic environments in which they find themselves.
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