A computational approach to modeling population differences.

Abstract

Four experiments were conducted to determine whether the Hyperspace Analogue to Language (HAL) model of semantic memory could differentiate between two different populations. An analysis of the differences in densities (or average distances between word neighbors in semantic space) in HAL matrices--generated from text corpora derived from younger and older adults--confirmed that HAL was able to distinguish between the two age groups. This difference was again detected when structured interview data were used to build the corpora. A third experiment, designed to test the specificity of HAL in detecting differences between groups, did not detect any difference in the densities of the memory representations when older adults generated both the test corpora. The final experiment, conducted on the language of adults with Alzheimer's and normal adults, again demonstrated that HAL could discriminate between the two populations. These results suggest that HAL is capable of modeling, on the basis of changes in mean density, some of the differences between populations without modifying the model itself but, rather, by changing the text corpus from which the model creates its representations in semantic space.