The inverse list length effect: A challenge for pure exemplar models of recognition memory

Abstract

When multiple items from a category are presented at study during a recognition memory procedure, false alarms to lures drawn from the same category increase. Distributed exemplar models of recognition assume that these effects occur because the representations of related lures are similar to the representations of studied items thus increasing the global matching strength of these items. We show that if list length is manipulated by keeping the number of categories constant, but increasing the number of exemplars in each category, then the unrelated false alarm rate decreases - thus inducing an inverse list length effect. Furthermore, if category structure is made less obvious by distributi..