Comparing Categorization Models
Jeffrey N. Rouder
University of Missouri—Columbia
Roger Ratcliff
The Ohio State University
Four experiments are presented that competitively test rule- and exemplar-based models of human
categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2
categories. The stimuli did not consistently belong to a category; instead, they were probabilistically
assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for which
exemplar- and rule-based explanations made qualitatively different predictions. F. G. Ashby and J. T.
Townsend’s (1986) rule-based general recognition theory provided a better account of the data than R. M.
Nosofsky’s (1986) exemplar-based generalized context model in conditions in which the to-be-classified
stimuli were relatively confusable. However, generalized context model provided a better account when
the stimuli were relatively few and distinct. These findings are consistent with multiple process accounts
of categorization and demonstrate that stimulus confusion is a determining factor as to which process
mediates categorization.
In this article we present an empirical paradigm to test different
theories of categorization behavior. One theory we test is the
exemplar-based theory in which categories are represented by sets
of stored exemplars. Category membership of a stimulus is deter-
mined by similarity of the stimulus to these exemplars (e.g., Medin
& Schaffer, 1978; Nosofsky, 1986, 1987, 1991). An exemplar-
based process relies on retrieval of specific trace-based informa-
tion without further abstraction; for example, a person is judged as
“tall” if he or she is similar in height to others who are considered
“tall.” The other theory we test is rule-based or decision-bound
theory. Decisions are based on an abstracted rule. The relevant
space is segmented into regions by bounds, and each region is
assigned to a specific category (e.g., Ashby & Gott, 1988; Ashby
& Maddox, 1992, 1993; As