Proceedings of the Annual Meeting of the Cognitive Science Society

A challenging problem in the computational modeling of meaning is representing abstract words and emotional connotations. Three simulations are presented that demonstrate that the Hyperspace Analogue to Language (HAL) model of memory encodes the meaning of abstract and emotional words in a cognitively plausible fashion. In this paper, HAL's representations are used to predict human judgements from word meaning norms for concreteness, pleasantness, and imageability. The results of a single-word priming experiment that utilized emotional and abstract words was replicated. These results suggest that it is unnecessary to posit separate lexicons to account for dissociations in priming results. HAL uses global co-occurrence information from a large corpus of text to develop word meaning representations. Representations of words that are abstract or emotional are formed no differently than concrete words.