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Collaborators
Improving Cognitive Function Through Gaming
Bradley C. Love
University College London
Tyler Davis
Ali Preston
Mike Mack
Todd Maddox
Brian Glass
BradLove.org
Outline
Gaming for Good
•  Boosting Cognitive Function through Gaming
–  Glass, B.D., Maddox, W.T. & Love, B.C. (2013). Real-Time Strategy
Game Training: Emergence of a Cognitive Flexibility Trait. PLOS
ONE.
•  Ways to assess changes in function using
brain measures.
–  Mack, M.L., Preston, A.R. & Love, B.C. (2013). Decoding the Brain's
Algorithm for Categorization from its Neural Implementation. Current
Biology.
Gaming for Good
Gaming Demographics
•  53% to 72% play video games
(US)
•  Majority (68%) are 18+
•  45% are female
–  In gaming population,
More Women 18+ than Boys <17
•  50% teens reported playing
yesterday
Entertainment Software Association,
Essential Facts Guide (2008 to 2013)
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Why the excitement over gaming?
Generalised Training?
•  Motivating and Engrossing
–  Gamification
•  Self-Paced/Personalised
•  Low cost
•  Possible generalisation of skills
Game Training
Lab Task vs. Rich Game
•  Goes back to efforts to overcome specificity in learning
–  Learning specificity is common (perceptual, motor, and
cognitive domains)
Blue
(Donchin, 1989)
Game Training
Game Training
•  Implementing these theories: Action Gaming
–  First person shooter
–  Move around in a 3D environment
–  Find others, shoot them first
–  Feedback & Difficulty Theories (Green & Bavelier, 2008)
•  Learning to use feedback properly to inform future actions
in order to navigate task spaces
•  Training on complex credit assignment problems, tune
networks responsible for allocating cognitive resources
•  Ramping up difficulty critical to find learning sweet spot
–  Main finding: Decreased RT
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Motion discrimination
Visual search
Multiple-object tracking
Useful field of view
Mental rotation
–  Robust results
–  Potential groundwork for how a complex, fast-paced game
might improve higher level cognition
•  Observational (VGP vs NVGP)
•  Experimental (Training)
–  Perceptual mechanisms
•  Various theories on how to overcome specificity
Dye, Green & Bavelier, 2009;
Green & Bavelier, 2008; Li,
Polat, Scalzo & Bavelier, 2010
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“Fast Perception” vs. “Fast Thinking”
•  Rather than rapid reaction to stimulus, the
hypothesis is that is that storing and
manipulating multiple pieces of information
while making rapid decisions is the key to
increasing cognitive flexibility.
Real-Time Strategy Games
•  Likely different effects than Action games
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Birds-eye command & control
Multi-agent system
Resource management
StarCraft is gold standard RTS
Multiple “fronts”
Fluid reorganization
Cognitive Flexibility
•  Being able to Assess & Adapt ongoing psychological
operations
•  Coordinate the allocation of cognitive resources
•  Likely not subserved by a single neural area
–  Representative of a broad functional network
•  Game training:
–  Engaging multiple processes at once
–  Tuning the distributed network responsible for allocating
cognitive resources
–  Broad vs. narrow training
(Duncan, 2001; Ravizza et al., 2010)
Shortcomings / Challenges in Game Training
Our Design
1.  Unspecified recruiting method
2.  Directionality problems
3.  Similarity of tasks vs. gaming experience
4.  No between-game manipulation
5.  No within-game manipulation
6.  Post-hoc and selective analyses.
Boot, Blakely, and Simmons (2011)
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Our Design: SC-1 , SC-2
Our Design
SC-2
Full-Map
Two Bases
SC-1
Half-Map
One base
Our Design: Task Battery
Our Design: Difficulty Titration
Stroop
Task Switching
Current Difficulty Level
Multi-Location Memory Test
Attentional Network Test (ANT)
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Operating Span (Ospan)
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Visual Search
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Digit Span – Working Memory
Filtering
Balloon Analogue Risk Taking Task
Multimedia Multitasking Index
Our Design: Difficulty Titration
Current Difficulty Level
Our Design: Difficulty Titration
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Current Difficulty Level
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Current Difficulty Level
Current Difficulty Level
Current Difficulty Level
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Our Design: Difficulty Titration
Our Design: Difficulty Titration
42.6% (SD = 8.8) for
SC-1
43.0% (SD = 8.7) for
SC-2
Current Difficulty Level
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Current Difficulty Level
Our Design: Game Mods
•  One-click experiment control software
•  Mini-map alerts suppressed
•  Keyboard use disabled
Current Difficulty Level
Our Design: Participants
•  Recruited for a general long-term
study (advert didn’t mention gaming)
•  Screened for gaming habits
<2 hours currently
<6 hours at most frequent
•  Sustainable female sample (n=72)
•  Males: 10.2 now, 25.7 highest
•  Females: 3.8 now, 11.0 highest
Shortcomings / Challenges in Game Training
1.  Unspecified recruiting method
2.  Directionality problems
3.  Similarity of tasks vs. gaming experience
4.  No between-game manipulation
5.  No within-game manipulation
6.  Post-hoc and selective analyses.
Our Design: Task Battery
Stroop
Task Switching
Multi-Location Memory Test
Attentional Network Test (ANT)
Operating Span (Ospan)
Visual Search
Digit Span – Working Memory
Filtering
Balloon Analogue Risk Taking Task
Multimedia Multitasking Index
Boot, Blakely, and Simmons (2011)
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Analysis: Methodology
Analysis Methods
•  Compare performance
– Against Sims condition
– Not just against pre-test
•  Weigh evidence for/against cognitive
flexibility
– Meta-analytical Bayes Factor approach
– H1/H0 (3:1 substantial, 10:1 strong, 100:1 decisive)
•  Common language
– Diffusion modelling where possible
•  Emergence of flexibility dimension
– Principal component analysis
Meta-Analytic Bayes Factor Approach
BF=
Pr( D∣ M 1 ) ∫ Pr (Θ1∣ M 1 )Pr( D∣Θ 1 ,M 1 ) dΘ 1
=
Pr( D∣ M 2 ) ∫ Pr(Θ 2∣ M 2 ) Pr( D∣Θ 2 ,M 2 )dΘ 2
•  Rouder and Morey (2011) provide method for combining t-statistics
across tasks to determine meta-analytic BF
•  The t-statistics compare StarCraft condition to The Sims Condition
Meta-BF
Meta-BF
Executive
Tasks
Other
Tasks
Aitkin, 1991
Meta-Analytical BF Approach
Analysis Methods
Meta-Analytic Bayes Factor Approach
Stroop
Task Switching
Multi-Location Memory Test
Jeffreys (1961) provided a popular qualitative interpretation of Bayes Factors:
Attentional Network Test (ANT)
Operating Span (Ospan)
Ratio
Interpretation
< 1:1
Supports Null
1:1 to 3:1
Insubstantial
3:1 to 10:1
Substantial
10:1 to 30:1
Strong
Visual Search
Digit Span – Working Memory
Filtering
Balloon Analogue Risk Taking Task
Multimedia Multitasking Index
BF = H1/H0
Meta-Analytical BF Approach
PCA
•  Does underlying dimension of cognitive flexibility emerge or strengthen?
•  Principal Components Analysis: a priori distinction
•  Pre-test C1 uncorrelated
•  Post-test C1 correlated for SC participants
Stroop
Task Switching
Multi-Location Memory Test
Attentional Network Test (ANT)
Operating Span (Ospan)
SIMS
Visual Search
Digit Span – Working Memory
Filtering
Balloon Analogue Risk Taking Task
Multimedia Multitasking Index
Evidence For H1
Evidence For H0
CF
Others
CF
= 1.17 for SC-1 vs. Sims
= 6.77 for SC-2 vs. Sims
= 40.76 for SC vs. Sims
StarCraft
Stroop
ANT
0.049
-0.095
Stroop
ANT
0.462
0.418
TSwitch
Multi
Ospan
BART
-0.126
0.515
-0.361
0.555
TSwitch
Multi
Ospan
BART
0.475
0.418
0.318
0.156
VSearch
Filter
0.285
0.404
VSearch
Filter
0.114
0.216
DigSpan
-0.156
DigSpan
0.161
r = .94, p <.001
Others
SC-1 v Sims
1.17
0.05
SC-1 v Sims
0.85
21.97
SC-2 v Sims
6.77
0.04
SC-2 v Sims
0.15
26.48
SC-B v Sims
40.76
0.02
SC-B v Sims
0.02
43.63
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In-Game Feature Recording
•  Recorded in-game Feature & Behavior
Game Feature:
e.g., Carrying minerals
In-Game Feature Recording
•  Recorded in-game Feature & Behavior
–  200+ binary features for each game “unit”
–  1 to 150 units available at any given time
–  Whether unit is selected by player
–  Recordings made every 250msec
–  40 hours of gaming per participant
~ 8,640,000,000
binary data points per participant
In-Game Feature Recording
•  Test size of attended feature set
Summary (Part I)
•  Overcoming Specificity in Learning.
•  Overcame a number of limitations in previous
studies.
•  “Fast Thinking” is possible to train.
Current and Future Efforts
•  Large scale game characterization project
•  mturk initially
–  Goal is millions of people
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Online psych battery
Game types
Gaming habits
Demographics
•  RTS gaming in older adults.
•  Platform to study development of expertise.
•  Changes in brain networks (e.g., DTI, default mode).
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