A probabilistic threshold model: analyzing semantic categorization data with the Rasch model.

According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation. The adequacy of this theory is assessed by applying a formalization of the theory, known as the Rasch model (Rasch, 1960; Thissen & Steinberg, 1986), to categorization data for eight natural language categories and subjecting it to a formal test. In validating the model special care is given to its ability to account for inter- and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Rasch model that can be used to uncover the nature of category representations and the sources of categorization differences are discussed.