Individual differences in working memory
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INDIVIDUAL DIFFERENCES IN WORKING MEMORY
A DATA MINING APPROACHShafee Mohammed
Working Memory and Plasticity LabUniversity of California Irvine
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AGENDAMotivationIntroductionQuestionsData SourcesMethodResultsDiscussionFuture Directions
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The problem
&Why I care?
MOTIVATION
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Information Manipulation
Attention
And executive
control
Temporary storage
INTRODUCTION
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N-back
INTRODUCTION
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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
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How do individual difference factors, such as age, baselineability, and n-back training domain (spatial vs. verbal), influencethe outcome of training?
How does one’s pattern of performance on the training taskinfluence transfer gains on untrained tasks?
QUESTIONS
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• 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 sensorimotoradaptation. Behavioral Brain Research, 228(1), 107-115.
• Buschkuehl, M., Hernandez-Garcia, L., Jaeggi, S. M., Bernard, J. A., & Jonides, J. (2014). Neuraleffects of short-term training on working memory. Cognitive, Affective, and BehavioralNeuroscience, 14(1), 147-160.
• Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits ofcognitive training. Proceedings of the National Academy of Sciences of the United States ofAmerica, 108(25), 10081-10086.
• Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011, April 2-5). Working memory trainingin typically developing children and children with Attention Deficit Hyperactivity Disorder: Evidencefor plasticity in executive control processes. Paper presented at the Eighteenth Annual CognitiveNeuroscience Society Meeting, San Francisco.
• Jaeggi, S.M., Buschkuehl, M., Shah, P., & Jonides, J. (2014). The role of individual differences incognitive training and transfer. Memory and Cognition, 42(3), 464-480.
DATA SOURCES
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• Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence withtraining on working memory. Proceedings of the National Academy of Sciences of the UnitedStates of America, 105(19), 6829-6833.
• Jaeggi, S. M., Studer, B., Buschkuehl, M., Su, Y.-F., Jonides, J., & Perrig, W. J. (2010). On TheRelationship Between N-back Performance and Matrix Reasoning - Implications for Training andTransfer. Intelligence, 38(6), 625-635.
• Jonides, J., Jaeggi, S. M., & Buschkuehl, M. (2010). Improving Fluid Intelligence by TrainingWorking 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 buyyou fluid intelligence (but it might not hurt either): The effect of compensation on transferfollowing a working memory intervention.
DATA SOURCES
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DEMOGRAPHICSSample size: 418
- 51% female- 49% male
Age Range: (7-78) YearsN – 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
Num
ber o
f par
tici
pant
s
Age Range
Participants age profile
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METHOD
Gathering the data and cleaning
Exploratory descriptive statistical analysis
Data mining using ML techniques–Logistic classifier and Decision tree analysis
Non-linear mixed effects models (planned)
Nearest neighbor analysis (planned)
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RESULTSTable 1. Descriptive statistics
OverallM SD
Age 22.512 16.109Age Centered 0 16.109Age Centered Squared 258.886 627.567Age Squared 765.675 1313.593Baseline 2.636 0.958Final 3.743 1.639Gain 1.106 1.044Observations 418Note. All variables raw scores are shown in the table.
Supervision (proportion) 0.43
Location (US proportion) 0.81Domain (proportion)
spatial 0.35verbal 0.11dual 0.40object 0.14
Single N-back (proportion) 0.60
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RESULTS
-2
0
2
4
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0 10 20 30 40 50 60 70 80
N-B
AC
K L
EV
EL
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)
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RESULTS
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RESULTSy = 0.5415x - 0.9366
R² = 0.6892
0
2
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6
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0 2 4 6 8 10
Ave
rage
per
form
ance
of f
irst t
hree
ses
sion
s (n
-bac
k le
vel)
Average performance of last three sessions (n-back level)
Individual Training curves
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RESULTSTraining Slope Beta SE
Centered Age 0.257 .011
Centered Age Squared -0.487 0.00
Female 0.028 .088
Location (US/Elsewhere) -0.230 .013
Domain (Spatial/Verbal/Dual) -.033 .013
Baseline performance .112 .054
Supervision 0.085 .011
Training Slope = F (age, stimuli domain, gender, training context, location, baseline)
R2 0.300
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RESULTS
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MACHINE LEARNINGA routine technique in psychology and neuroscience
- Computer vision applications- predictive behavior of future alcohol abusers- computational models of human learning- manipulating game elements
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RESULTSError Matrix
Target Label Predicted Label Count0 0 340 1 101 1 331 0 12
Accuracy: 0.76(0.03)
+-------+---------------------+| class | probability |+-------+---------------------+| 1 | 0.715902924538 || 0 | 0.678073525429 || 0 | 0.870646208525 || 0 | 0.567122161388 || 1 | 0.502207100391 || 0 | 0.503329485655 || 0 | 0.875528343022 || 1 | 0.557602643967 || 1 | 0.537259638309 || 1 | 0.760314226151 |+-------+---------------------+
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RESULTS
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RESULTS
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Baseline characteristics allow us to predict whether an individual willperform in the upper 50% of participants.
Weightage of each baseline character needs to be evaluated
DISCUSSION
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Regression analysis shows individual difference factors, includingage(squared) and starting performance, predict slope of trainingperformance (R-Squared=0.30)
However, non-linear mixed effect models may allow a more accurateaccount for a participant’s actual training performance (which is almostcertainly not linear in nature).
DISCUSSION
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FUTURE DIRECTIONS
Picture courtesy – freshbiostats.com
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Identification of all contributingfactors to improve predictionaccuracy.
Designing a method to tailorworking memory training toindividuals.
FINAL GOALS
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Thank You!
I’ll take questions now