Atkins Sharona M, Sprenger Amber M, Colflesh Gregory J H, Briner Timothy L, Buchanan Jacob B, Chavis Sydnee E, Chen Sy-Yu, Iannuzzi Gregory L, Kashtelyan Vadim, Dowling Eamon, Harbison J Isaiah, Bolger Donald J, Bunting Michael F, Dougherty Michael R
Department of Psychology, University of Maryland, <location>College Park, MD, USA</location>
Center for Advanced Study of Language, University of Maryland, <location>College Park, MD, USA</location>
Exp Psychol. 2014;61(6):417-38. doi: 10.1027/1618-3169/a000262.
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. We argue that Shapebuilder has many advantages over existing measures of WM, including the fact that it is largely language independent, is not prone to ceiling effects, and take less than 6 min to complete on average.
我们开发了一种名为Shapebuilder的新型四维空间任务，并使用它来预测各种认知任务的表现。在六个实验中，我们证明Shapebuilder：（1）与复杂工作记忆（WM）广度任务加载于共同因素，并且它能预测定量推理任务和瑞文渐进性矩阵的表现（实验1），（2）与传统复杂WM广度任务相关性良好（实验2），能预测条件性执行/不执行任务（实验3）和n-back任务（实验4）的表现，并且与注意力网络任务（实验5）和任务切换（实验6）的相关性较弱或不显著。Shapebuilder表明它具有最小的偏度和峰度，并且具有良好的可靠性。我们认为Shapebuilder相对于现有的WM测量方法有许多优势，包括它在很大程度上不依赖语言，不易出现天花板效应，并且平均完成时间不到6分钟。
