6. Critically important for success
- in school (Gathercole et al., 2003)
- at work (Higgins et al., 2007).
Adaptive and Challenging
Useful assay of cognitive plasticity
INTRODUCTION
7. How do individual difference factors, such as age, baseline
ability, and n-back training domain (spatial vs. verbal), influence
the outcome of training?
How does one’s pattern of performance on the training task
influence transfer gains on untrained tasks?
QUESTIONS
8. • Anguera, J. A., Bernard, J. A., Jaeggi, S. M., Buschkuehl, M., Benson, B., L., Jennett, S., M., L.,
et al. (2012). The effects of working memory resource depletion and training on sensorimotor
adaptation. Behavioral Brain Research, 228(1), 107-115.
• Buschkuehl, M., Hernandez-Garcia, L., Jaeggi, S. M., Bernard, J. A., & Jonides, J. (2014). Neural
effects of short-term training on working memory. Cognitive, Affective, and Behavioral
Neuroscience, 14(1), 147-160.
• Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits of
cognitive training. Proceedings of the National Academy of Sciences of the United States of
America, 108(25), 10081-10086.
• Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011, April 2-5). Working memory training
in typically developing children and children with Attention Deficit Hyperactivity Disorder: Evidence
for plasticity in executive control processes. Paper presented at the Eighteenth Annual Cognitive
Neuroscience Society Meeting, San Francisco.
• Jaeggi, S.M., Buschkuehl, M., Shah, P., & Jonides, J. (2014). The role of individual differences in
cognitive training and transfer. Memory and Cognition, 42(3), 464-480.
DATA SOURCES
9. • Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with
training on working memory. Proceedings of the National Academy of Sciences of the United
States of America, 105(19), 6829-6833.
• Jaeggi, S. M., Studer, B., Buschkuehl, M., Su, Y.-F., Jonides, J., & Perrig, W. J. (2010). On The
Relationship Between N-back Performance and Matrix Reasoning - Implications for Training and
Transfer. Intelligence, 38(6), 625-635.
• Jonides, J., Jaeggi, S. M., & Buschkuehl, M. (2010). Improving Fluid Intelligence by Training
Working Memory. Presented at the Office of Naval Research - Contractor's Meeting, Arlington,
VA.
• Katz, B., Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (under review). Money can’t buy
you fluid intelligence (but it might not hurt either): The effect of compensation on transfer
following a working memory intervention.
DATA SOURCES
10. DEMOGRAPHICS
Sample size: 418
- 51% female
- 49% male
Age Range: (7-78)Years
N – back task variants
- Spatial
-Verbal
- Dual
- Object
Context – Supervision: At home, at school,
in the lab setting, in a classroom 0
20
40
60
80
100
120
140
160
180
200
0-10 10.01-20 20.01-30 30.01-40 40.01-50 50.01-60 60.01-70 70.01-80
Numberofparticipants
Age Range
Participants age profile
12. RESULTS
Table 1. Descriptive statistics
Overall
M SD
Age 22.512 16.109
Age Centered 0 16.109
Age Centered
Squared 258.886 627.567
Age Squared 765.675 1313.593
Baseline 2.636 0.958
Final 3.743 1.639
Gain 1.106 1.044
Observations 418
Note. All variables raw scores are shown in the table.
Supervision (proportion) 0.43
Location (US proportion) 0.81
Domain (proportion)
spatial 0.35
verbal 0.11
dual 0.40
object 0.14
Single N-back (proportion) 0.60
13. RESULTS
-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
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)
18. MACHINE LEARNING
A routine technique in psychology and neuroscience
- Computer vision applications
- predictive behavior of future alcohol abusers
- computational models of human learning
- manipulating game elements
22. Baseline characteristics allow us to predict whether an individual will
perform in the upper 50% of participants.
Weightage of each baseline character needs to be evaluated
DISCUSSION
23. Regression analysis shows individual difference factors, including
age(squared) and starting performance, predict slope of training
performance (R-Squared=0.30)
However, non-linear mixed effect models may allow a more accurate
account for a participant’s actual training performance (which is almost
certainly not linear in nature).
DISCUSSION
25. Identification of all contributing
factors to improve prediction
accuracy.
Designing a method to tailor
working memory training to
individuals.
FINAL GOALS
