Individual differences in working memory

INDIVIDUAL DIFFERENCES IN WORKING MEMORY

A DATA MINING APPROACHShafee Mohammed

Working Memory and Plasticity LabUniversity of California Irvine

AGENDAMotivationIntroductionQuestionsData SourcesMethodResultsDiscussionFuture Directions

The problem

&Why I care?

MOTIVATION

Information Manipulation

Attention

And executive

control

Temporary storage

INTRODUCTION

N-back

INTRODUCTION

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

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

• 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

• 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

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

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60

80

100

120

140

160

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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

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)

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

RESULTS

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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)

RESULTS

RESULTSy = 0.5415x - 0.9366

R² = 0.6892

0

2

4

6

8

10

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

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

RESULTS

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

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 |+-------+---------------------+

RESULTS

RESULTS

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

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

FUTURE DIRECTIONS

Picture courtesy – freshbiostats.com

Identification of all contributingfactors to improve predictionaccuracy.

Designing a method to tailorworking memory training toindividuals.

FINAL GOALS

Thank You!

I’ll take questions now