Holographic Declarative Memory: Using Distributional Semantics within ACT-R

We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distributional semantics models can process millions of data points to infer semantic similarities from language data or to infer product recommendations from patterns of user preferences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on iterated decisions with initially unknown payoffs. The model we propose provides a flexible, scalable alternative to ACT-R's declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition. Our intent is to advance toward a cognitive architecture capable of modeling human performance at all scales of learning.