We describe the DSHM (Dynamically Structured Holographic Memory) model of human memory, which uses high dimensional vectors to represent items in memory. The complexity and intelligence of human behavior can be attributed, in part, to our ability to utilize vast knowledge acquired over a lifetime of experience with our environment. Thus models of memory, particularly models that can scale up to lifetime learning, are critical to modeling human intelligence. DHSM is based on the BEAGLE model of language acquisition (Jones & Mewhort, 2007) and extends this type of model to general memory phenomena. We demonstrate that DHSM can model a wide variety of human memory effects. Specifically, we model the fan effect, the problem size effect (from math cognition), dynamic game playing (detecting sequential dependencies from memories of past moves), and time delay learning (using an instance based approach). This work suggests that DSHM is suitable as a basis for learning both over the short-term and over the lifetime of the agent, and as a basis for both procedural and declarative memory. We argue that cognition needs to be understood at both the symbolic and sub-symbolic levels, and demonstrate that DSHM intrinsically operates at both of these levels of description. In order to situate DSHM in a familiar context, we discuss the relationship between DHSM and ACT-R.
All Science Journal Classification (ASJC) codes
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Artificial Intelligence