People face a problem similar to that faced by algorithms that manage the memory of computers: trying to organize information to maximize the chance it will be available when needed in the future. In computer science, this problem is known as “caching”. Inspired by this analogy, we compared the properties of a model of human memory proposed by Anderson and Schooler (1991) and caching algorithms used in computer science. We tested each algorithm on a dataset relevant to human cognition: headlines from the New York Times. In addition to overall performance, we investigated whether the algorithms from computer science replicated the well-documented effects of recency, practice, and spacing on human memory. Anderson and Schooler's model performed comparably to the worst caching algorithms, but was the only model that captured the spacing effects seen in human memory data. All models showed similar effects of recency and practice.