Spatial Span and Matrix Reasoning data from the UW-Madison Learning and Transfer Lab
Description
Matrices_SpatialSpan.csv 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, & 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.).
robustCor.R 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.
Files
Matrices_SpatialSpan.csv
Files
(2.1 MB)
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Additional details
References
- Matzen, L. E., Benz, Z. O., Dixon, K. R., Posey, J., Kroger, J. K., & Speed, A. E. (2010). Recreating Raven's: Software for systematically generating large numbers of Raven-like matrix problems with normed properties. Behavior Research Methods, 42(2), 525–541. https://doi.org/10.3758/BRM.42.2.525
- Pahor, A., Stavropoulos, T., Jaeggi, S. M., & Seitz, A. (2019). Validation of a matrix reasoning task for mobile devices. Behavior Research Methods, 51(5), 2256–2267. https://doi.org/10.3758/s13428-018-1152-2
- Cochrane, A., Simmering, V. R., & Green, C. S. (2019). Fluid intelligence is related to capacity in memory as well as attention: Evidence from middle childhood and adulthood. PLOS ONE, 14(8), e0221353. https://doi.org/10.1371/journal.pone.0221353