Hyperspace analog to language (HAL) is a high-dimensional model of semantic space that uses the global co-occurrence frequency of words in a large corpus of text as the basis for a representation of semantic memory. In the original HAL model, many parameters were set without any a priori rationale. We have created and publicly released a computer application, the High Dimensional Explorer (HiDEx), that makes it possible to systematically alter the values of these parameters to examine their effect on the co-occurrence matrix that instantiates the model. We took an empirical approach to understanding the influence of the parameters on the measures produced by the models, looking at how well matrices derived with different parameters could predict human reaction times in lexical decision and semantic decision tasks. New parameter sets give us measures of semantic density that improve the model’s ability to predict behavioral measures. Implications for such models are discussed.
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