A data mining approach to getting at individual differences in working memory training. This work is presented at the GEL network conference at Boston University, MA.

1. INDIVIDUAL DIFFERENCES IN WORKING MEMORYTRAINING
A DATA MINING APPROACH
Shafee Mohammed
School of Education – UC Irvine
Working Memory and Plasticity Lab

2. -2
0
2
4
6
8
10
0 10 20 30 40 50 60 70 80
N-BACKLEVEL
AGE (IN YEARS)
EFFECT OF AGE ON PERFORMANCE GAINS
R2 = 0.171 (N = 386)
Training Slope Beta SE
Age -0.180 0.000
Gender -0.520 0.110
Location(Elsewhere/US) 0.236 0.140
Training Domain -0.033 0.013
Baseline Performance 0.169 0.006
Supervision -0.183 0.015
Training Slope = F(Age, Gender, Location, Domain, Baseline performance,
Supervision)
Training Accuracy = 0.707 (0.03)
Baseline Performance
Average Performance in last three
sessions
Gain in Performance
Baseline (2nd Order Poly)
Last three sessions (2nd Order Poly)
Gain in performance (2nd Order Poly)

3. • Not every person improves
equally on aWM training task.
• Weight of each contributing
feature.
• Non-linear mixed effects
model
• LongTerm Goal -Tailor
working memory training to
individuals.
Conclusions
Picture Courtesy - http://datamining.typepad.com
Picture Courtesy - http://www.version2.dk