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Recent semantic space models learn vector representations for word meanings by observing statistical redundancies across a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space, and semantic similarity between words is quantified by a function of their spatial proximity (typically the cosine of the angle between their corresponding vector representations). Recently, Griffiths, Steyvers, and Tenenbaum (2007) demonstrated that spatial models are unable to simulate human free association data due to the constraints placed upon them by metric axioms which appear to be violated in association norms. However, it is important to note that free association data is the product of a retrieval process operating on a semantic representation, and the failures of spatial models are likely be due to mistaking the similarity metric (cosine) for an appropriate process model of the association …