Holographic Declarative Memory: Using Distributional Semantics within ACT-R

We explore replacing the declarative memory system of theACT-R cognitive architecture with a distributional semanticsmodel. ACT-R is a widely used cognitive architecture, butscales poorly to big data applications and lacks a robust modelfor learning association strengths between stimuli. Distribu-tional semantics models can process millions of data pointsto infer semantic similarities from language data or to in-fer product recommendations from patterns of user prefer-ences. We demonstrate that a distributional semantics modelcan account for the primacy and recency effects in free recall,the fan effect in recognition, and human performance on it-erated decisions with initially unknown payoffs. The modelwe propose provides a flexible, scalable alternative to ACT-R’s declarative memory at a level of description that bridgessymbolic, quantum, and neural models of cognition.