| Sumario: | Collective memory broadly refers to thememories shared by a group of people. Interest in collective memory among cognitive psychologists has boomed in recent years, with many studies leveraging fluency tasks to probe what events and people come to mind given a prompt. As other research using fluency tasks has benefitted greatly from network analysis (e.g., semantic memory research), it seems there is an opportunity to deepen our understanding of collective cognition and changes in collective cognition by adopting a network perspective. In the current article, we ask whether collectivememory investigations could be enriched by harnessing the tools of network science. We start by reviewing the relevant collectivememory literature and touch on the deep semantic memory literature to the extent it provides ties to network analysis for present goals. Our novel contributions to the topic include the introduction of a large fluency data set collected over the course of a decade as part of a task embeddedwithin several research projects. We conduct several descriptive analyses and initial, proof-of-concept network analyses examining collective memory for U.S. cities. Some cities--those that are recalled most frequently--are recalled at similar rates and in similar output positions across time and task contexts. Our network approach suggests that recall transitions (e.g., recalling Los Angeles and San Francisco in adjacent positions) aremade at similar rates as well. Together, these complementary approaches suggest a striking stability in both what people recall and their ordering, providing a window into the composition of collective memories.
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