INDIVIDUAL DIFFERENCES IN WORKING MEMORY TRAINING

A DATA MINING APPROACH

Shafee MohammedSchool of Education – UC Irvine

Working Memory and Plasticity Lab

0 10 20 30 40 50 60 70 80

-2

0

2

4

6

8

10 effect of Age on performance gains

Age (IN Years)

N-B

ack

Lev

el

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)

• Not every person improves equally on a WM training task.

• Weight of each contributing feature.

• Non-linear mixed effects model

• Long Term Goal - Tailor working memory training to individuals.

Conclusions

Picture Courtesy - http://datamining.typepad.com

Picture Courtesy - http://www.version2.dk