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AGE (IN YEARS)

PERFORMANCE GAINS BY AGEBaseline 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)

Individual Differences in Working Memory TrainingShafee Mohammed1; Benjamin Katz2; Chelsea M. Parlett1; Martin Buschkuehl3; and Susanne M. Jaeggi1

University of California, Irvine1, University of Michigan2, MIND Research Institute3

INTRODUCTION

RESEARCH QUESTIONS

• How do individual difference factors, such as age, baseline

ability, and n-back training type (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?

• Data:

• 418 participants from varied age range, education level,

and geographic locations.

• At least 15 sessions of working memory training.

Anguera et al. (2012). Behavioral Brain Research.

Buschkuehl et al. (2014). Cognitive, Affective, and Behavioral Neuroscience.

Jaeggi et al. (2011). Proceedings of the National Academy of Sciences of the

United States of America.

Jaeggi et al. (2011). Paper presented at the Eighteenth Annual Cognitive

Neuroscience Society Meeting.

Jaeggi et al. (2014). Memory and Cognition.

Jaeggi et al. (2008). Proceedings of the National Academy of Sciences of the

United States of America.

Jaeggi et al. (2010). Intelligence.

Jonides et al. (2010). Presented at the Office of Naval Research - Contractor's

Meeting, Arlington, VA.

Katz et al. (under review).

Seidler et al. (2010). Technical Report No. M-CASTL 2010-01, University of

Michigan, Ann Arbor.

Stepankova et al. (2014). Developmental Psychology.

Tsai et al. (in preparation)

Zhang et al. (2014). 26th Annual Convention of the Association for Psychological

Science, San Francisco, CA.

METHOD

RESULTS

• Age, country in which the study was conducted, and baseline abilities

predict one’s ability to improve at WM training.

• Gender, N-back type, and supervision have no significant effect.

• Prediction accuracy is significantly different from chance with nearly

76% (0.02) (200 iterations) on an average.

DISCUSSION

CONCLUSIONS

• Baseline characteristics, and age are important factors.

• Age, and baseline performance, are good predictors of training

performance (R2 = 0.30).

• Further analysis will include non-linear mixed effect models and

cluster analyses.

• Working memory is critically important for success in school

and at work (Gathercole et al., 2003; Higgins et al., 2007).

• One’s performance on an adaptive and challenging

longitudinal working memory intervention may serve as a

useful assay to investigate cognitive plasticity.

Error Matrix

Target

Label

Predicted

LabelCount

0 0 34

0 1 10

1 1 33

1 0 12

Accuracy: 76% (0.02)

Authors’ note: MB is employed at MIND Research Institute, whose interest is related tothis work. SMJ has an indirect financial interest in the MIND Research Institute.

REFERENCES

Gathercole, S. E., Brown, L., & Pickering, S. J. (2003). Working memory assessments at school entry

as longitudinal predictors of National Curriculum attainment levels. Educational and Child

Psychology, 20(3), 109-122.

Higgins, D. M., Peterson, J. B., Pihl, R. O., & Lee, A. G. (2007). Prefrontal cognitive ability,

intelligence, Big Five personality, and the prediction of advanced academic and workplace

performance. Journal of Personality and Social Psychology, 93(2), 298.

Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (2014). The role of individual differences in

cognitive training and transfer. Memory & Cognition, 42(3), 464-480.

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

• Identifying the factors that potentially contribute to explaining

such individual differences is the key to improving training.

• Data mining and machine learning can be viable approaches to

investigating plasticity.

Figure 2. Average performance on the first three sessions(blue), performance on last three sessions (gray), and

performance gain (purple) as a function of Age.

Figure 1. An example showing visual and auditory 2-back tasks. The stimuli are

presented chronologically from left to right as a visual stimulus or an auditory

stimulus respectively. Participants should match the current stimulus with the nth

stimulus (2nd in this case.)

Table 1. The accuracy of the algorithm to detect whether or not a

subject is in the top fifty percentile.

Table 4. Values in the descriptive table are raw scores and standardized units for regression. Numbers in bold are statistically

significant (in this case, p<0.01). Subjects aged 10-20, from US, Male, Supervised (as opposed to Unsupervised), Trained on

spatial n-back, are left out as controls.

Descriptive statistics Regression ResultsSample Characteristics Mean SD Beta SE

Age (Range 7-78 years) 22.51 16.11

Age Squared 765.68 1313.59

Age Centered 0 16.11 0.25 0.01

Age Centered Squared 258.89 627.57 -0.48 0.00

Gender (Female Proportion) 0.51 0.02 0.08

Baseline N-Back Level 2.64 0.95 0.11 0.05

Final N-Back level 3.74 1.64

Gain 1.11 1.044

Training Characteristics

Supervised participants 0.43 0.08 0.01

Location (US Participants) 0.81 -0.23 0.01

N-back Type -0.03 0.01

Spatial 0.35

Verbal 0.11

Object

Total Single N-back 0.60

Total Dual N-back 0.40

Total Observations 418 R-Squared 0.300

Gathering the data and cleaning

Descriptive statistical analysis

Data mining methods – Logistic classifier and Decision tree analysis

Non-linear mixed models (planned)

Nearest neighbor analysis (planned)

Class Probability

1 0.715

0 0.678

0 0.870

0 0.567

1 0.502

0 0.503Table 2. The prediction probability of each

individual’s accuracy to detect whether or not

each subject is in the top fifty percentile. The

individual’s in the bold have a low predictive

probabilities.

Figure 3. The prediction accuracies over 200 iterations. Attained prediction range is 0.69 - 0.81.

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Training gains based on training locations

Non-US Participants

US Participants

Figure 4. The difference between the US (in red) and non-US (blue) participant training gains. The individuals

with negative post training gains are exclusively from the US.

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Figure 7. (right) shows a

decision tree model used to

predict and classify individuals.

Each individual starts at the

purple dot and moves along the

branches of the tree based on a

decision choice for each

condition. Only a single branch

is shown in full detail for

illustration purposes.

Figure 5. (left) The baseline

performance (top graph) and the

gains (bottom graph) of the top

50% of the participants (in green)

and bottom 50% of the participants

(in red).

Tru

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ate

False Positive Rate

Schema

Number of examples 315

Number of features 7

Number of unpacked features 7

Number of classes 2

Model Properties

Number of Trees 10

Max tree depth 6

Training accuracy 0.92

Validation Accuracy 0.85

Training log-loss 0.27

Validation log-loss 0.39

Figure 6.(left) ROC curve obtained by aggregating all

predictions from 200 repetitions. Table 3.(above) Shows

the model characteristics and the validation estimates.

DATA SOURCES