The similarity choice model of identification (Luce, 1963; Shepard, 1957) and the context model of categorization (Medin & Schaffer, 1978; Nosofsky, 1986) are shown to be closely related to a variety of likelihood-based models. In particular, it is shown that: 1) for category distributions defined over independent dimensions, general versions of the context model and Estes' (1986) similarity-likelihood model are formally identical; 2) the context model and similarity choice model can be given interpretations as exemplar-based likelihood models; 3) an independent feature addition-deletion model is a special case of the similarity choice model; and 4) a perception/likelihood-based decision model of identification generates predictions that are characterizable by the similarity choice model.