Spatial Span and Matrix Reasoning data from the UW-Madison Learning and Transfer Lab

<strong>Matrices_SpatialSpan.csv</strong> includes one row for every mouse click for every trial for each participant's spatial span performance (for similar spatial span methods see Cochrane, Simmering, &amp; Green, 2019, PLOS One). Participant IDs, trial numbers, the presence [f] or absence [n] of feedback, and task order (spatial span first or spatial span second) are included alongside by-click accuracy. Also included are each participants' average scores on a subset of items from the UCMRT (Pahor et al., 2019, Beh. Res. Meth) and from the matrices developed at Sandia National Laboratories (Matzen et al., 2010, Beh. Res. Meth.). <strong>robustCor.R </strong>is R code implementing a test of bivariate correlation. Univariate Yeo-Johnson transformations are applied, then bootstrapped correlations coefficients are calculated. Point estimates, CI, and Bayes Factors are each returned. Data were collected and code was developed as part of A. Cochrane's dissertation work at the University of Wisconsin - Madison under the supervision of C. Shawn Green.**Matrices_SpatialSpan.csv** 收录了每名参与者在空间广度任务中的各项试次的每一次鼠标点击所对应的行数据（类似空间广度任务的实验方法可参考Cochrane、Simmering与Green于2019年发表于《PLOS ONE》的研究）。数据集包含参与者ID、试次编号、反馈存在[f]与不存在[n]的标记、任务顺序（先执行空间广度任务还是后执行），以及逐次点击的正确率。此外还收录了参与者在UCMRT部分项目上的平均得分（Pahor等人，2019年，《行为研究方法(Behavior Research Methods)》），以及桑迪亚国家实验室(Sandia National Laboratories)开发的矩阵推理任务得分（Matzen等人，2010年，《行为研究方法(Behavior Research Methods)》）。 **robustCor.R** 是用于实现双变量相关性检验的R代码。该代码首先执行单变量Yeo-Johnson变换，随后计算自助法相关系数，最终返回点估计值、置信区间(CI)与贝叶斯因子(Bayes Factors)。 本数据集与配套代码由A. Cochrane在威斯康星大学麦迪逊分校于C. Shawn Green的指导下，作为其博士论文研究的一部分收集与开发。