Prototype- and exemplar-based information in natural language categories

Abstract

Two experiments are reported in which four dependent variables; typicality ratings, response times, category-naming frequencies, and exemplar-generation frequencies of natural language concepts, were predicted by two sorts of prototype predictors and by an exemplar predictor related to Heit and Barsalou's (1996) instantiation principle. In the first experiment, the exemplar predictor was compared to a prototype predictor calculated as in Hampton (1979). The four dependent variables were either predicted better by the exemplar measure than by the prototype predictor or the predictive value was about equal. In the second experiment, a new prototype predictor was calculated based on Rosch and Mervis' (1975) classic family resemblance measure. The results showed that the exemplar predictor accounted better for the dependent variables than Hampton's and Rosch and Mervis' prototype measures. The differences between the prototype measures were not significant.

Dutch norm data for 13 semantic categories and 338 exemplars

“…This procedure had been successfully applied in previous studies, such as Hampton (1979), Rosch and Mervis (1975), Storms et al (2000), and Storms, Ruts, and Vandenbroucke (1998). The participants were asked to write down 10 or more features for one to three unrelated categories.…”

Human Category Learning: Toward a Broader Explanatory Account

“…There are many ways to look at categorization more expansively including: the role of prior knowledge (e.g., Murphy & Allopena, 1994;Wisniewski, 1995), comparison across groups and cultures (e.g., Lynch, Coley, & Medin, 2000;Medin & Atran, 2004), neurobiological constraints (e.g., Ashby & Maddox, 2005), category-based induction (e.g., Murphy & Ross, 2010), knowledge partitioning (e.g., Yang & Lewandowsky, 2004), the internal structure of natural concepts (e.g., Storms, De Boeck, & Ruts, 2000), and the role of causal structure (e.g., Rehder, 2010). That is, we need to move beyond a focus on mapping from features to class labelsdeven while this remains part of the overall explanatory scope.…”

Deriving semantic structure from category fluency: Clustering techniques and their pitfalls

“…Repetitions and intrusions (non-category words) were not counted in the global score (see Table 1 for score). For the present purpose, we only examined “animals” because there is considerable blurring of semantic boundaries between the other two categories, namely fruits and vegetables (e.g., an avocado and tomato are examples of fruits, but they are often generated as exemplars of the vegetable category; see Storms, De Boeck and Ruts, 2000) and consequently the semantic search process can be expected to be somewhat more complex. Furthermore, the vast majority of neuropsychological studies that used category fluency data to study semantic deficits have focused on animals (Chan et al 1993; Storms et al, 2003a).…”