We developed a novel four-dimensional spatial task called Shapebuilder and used it to predict performance on a wide variety of cognitive tasks. In six experiments, we illustrate that Shapebuilder: (1) Loads on a common factor with complex working memory (WM) span tasks and that it predicts performance on quantitative reasoning tasks and Ravens Progressive Matrices (Experiment 1), (2) Correlates well with traditional complex WM span tasks (Experiment 2), predicts performance on the conditional go/no go task (Experiment 3) and N-back (Experiment 4), and showed weak or nonsignificant correlations with the Attention Networks Task (Experiment 5), and task switching (Experiment 6). Shapebuilder shows that it exhibits minimal skew and kurtosis, and shows good reliability.
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