We present Holographic Declarative Memory (HDM), a new memory module for ACT-R and alternative to ACT-R’s Declarative Memory (DM). ACT-R is a widely used cognitive architecture that models many different aspects of cognition, but is limited by its use of symbols to represent concepts or stimuli. HDM replaces the symbols with holographic vectors. Holographic vectors retain the expressive power of symbols but have a similarity metric, allowing for shades of meaning, fault tolerance, and lossy compression. The purpose of HDM is to enhance ACT-R’s ability to learn associations, learn over the long-term, and store large quantities of data. To demonstrate HDM, we fit performance of an ACT-R model that uses HDM to a benchmark memory task, the fan effect. We analyze how HDM produces the fan effect and how HDM relates to the standard DM model of the fan effect.