Modeling Working Memory Tasks on the Item Level.

Item responses to Digit Span and Letter-Number Sequencing were analyzed to develop a better-refined model of the two working memory tasks using the finite mixture (FM) modeling method. Models with ordinal latent traits were found to better account for the independent sources of the variability in the tasks than those with continuous traits, and the discretely distributed factors appeared to represent short-term storage (STS), general attention control (GAC), and the specific control mechanisms initiated by the interfering operations of mental sorting (MS) and backward ordering (BO). When related to the general ability factor (G) defined by the WISC-R verbal and performance scores and the total achievement score, the general working memory factors STS and GAC both seemed to share substantial variances with G, but the roles of specific factors MS and BO were less definitive. These WM factors accounted for the majority of the variability in G, with the multiple correlation between the factor mean scores of the WM factors and that of G above 0.80. Moreover, there seemed to be a discontinuity in the distribution of the ordinal GAC factor, as the two lowest subcategories of GAC were separated from the rest of the overall sample by the virtually empty third lowest subcategory, and the two outlying low subcategories contained the majority (80%) of the cases with mild mental retardation. The theoretical implications of these results were discussed.