In most recognition models a decision is based on a global measure, a measure often termed familiarity. However, in typical studies it is not possible to measure changes in familiarity that occur when length and strength are varied, because the response criterion is free to vary. In an effort to hold the criterion constant, we used a single long study list with exemplars of many categories spaced throughout the list. We varied across categories the number of exemplars (category-length), the number of spaced repetitions of an exemplar (item-strength), and the number of repetitions of other exemplars in the category of the test item (category-strength). False alarms rose with category-length but remained constant across variations in category-strength. We suggest that distributions of familiarity do not change much with changes in strength of other items, but grow when additional items are studied. The results were fit by an extension to the present category paradigm of the SAM model presented in Shiffrin, Ratcliff and Clark (1990).