The Hyperspace Analogue to Language (HAL) model of memory learns the representations of meaning in a 140,000 dimensional semantic space after experiencing 320 million words of text. Although large scale, the model utilizes a cognitively plausible, very simple learning algorithm that captures the contexts in which words appear. The model has been used to investigate semantic and associative memory, proper name semantics, semantic and grammatical categorization, cerebral hemispheric specialization of memory function, semantic constraints during syntactic processing, developmental psycholinguistics, normal aging, schizophrenia, and Alzheimer's disease. Some of these results will be discussed in the context the features of the model as well as some of the recent criticisms of the model.

Burgess, C., & Lund, K. (2000). The Dynamics of Meaning in Memory. In E. Dietrich & A. Markman (Eds.), Cognitive dynamics: Conceptual and Representational Change in Humans and Machines (pp. 117 - 156). Hillsdale, N.J.: Lawrence Erlbaum Associates, Inc.
Burgess, C. (1998). From Simple Associations to the Building Blocks of Language: Modeling Meaning in Memory with the HAL Model. Behavior Research Methods, Instruments, and Computers, 30, 188 - 198.