1. Neural mechanisms of decision making -
emotion vs. cognition
Prepared for
Lab seminar
2008 06 09 9a.m. #3239, CT
2008.06.09, 9 #3239
Kyongsik Yun Ph D Candidate
Yun, Ph.D.
KAIST
yunks@kaist.edu

2. “The mind is a charioteer driving twin
horses of reason and emotion Except
emotion.
cognition is a smart pony, and
emotion an elephant”
— Colin Camerer & George Loewenstein
2

3. Research Summary
[Resarch interests]
U d t di th lb fd i i ki i t t i i t ti
- Understanding the neural bases of decision making in strategic interaction
- Computational modeling of neural networks underlying reward and learning
- Behavioral game theory and neuroeconomics
- Theoretical neuroscience with reinforcement learning, nonlinear dynamics, information theory
g, y , y
- Functional neuroimaging data analysis including fMRI and EEG
Understanding the Neural mechanisms of
Decision Making
in the context of Emotion vs Cognition
vs.
Behavioral game theory and Impaired decision making in
p g Computational modeling and
neuroeconomics – Ulti t
i Ultimatum neuropsychiatric disorders – i l ti i f t
simulation – reinforcement
game methamphetamine addiction, learning
schizophrenia, Alzheimer’s
Disease,
Disease adolescence
Methods
- Nonlinear dynamic analysis
- EEG analysis for high temporal resolution information processing
- fMRI analysis for functional connectivity and large-scale communication between the brain regions

4. I
Important questions
t t ti
• Behavior: How do we valuate ‘fairness’
at the behavioral level?
• Physiology: what are the neural
y gy
mechanisms within and between the
brain that implement the decision
making?
• Th
Theory: C we formally d
Can f ll describe h
ib how
‘fairness’ is computed within the brain
p
(i.e. can we build a model?)

5. What are the temporal dynamics of
p y
social interaction?
proposer responder
5

6. What are the temporal dynamics of
p y
social interaction?
proposer responder
1. Make an offer: 9:1
(send emotional cue)
Reward anticipation
(NAcc)
6

7. What are the temporal dynamics of
p y
social interaction?
proposer responder
1. Make an offer: 9:1 2. Conflict btwn emotion & cognition
(send emotional cue) ACC, Ins
ACC Ins, dlPFC activation (interaction)
acti ation
Reward anticipation
(NAcc)
7

8. What are the temporal dynamics of
p y
social interaction?
proposer responder
1. Make an offer: 9:1 2. Conflict btwn emotion & cognition
(send emotional cue) ACC, Ins
ACC Ins, dlPFC activation (interaction)
acti ation
Reward anticipation
(NAcc) 3. Make a decision (reject: Ins)
8

9. What are the temporal dynamics of
p y
social interaction?
proposer responder
1. Make an offer: 9:1 2. Conflict btwn emotion & cognition
(send emotional cue) ACC, Ins
ACC Ins, dlPFC activation (interaction)
acti ation
Reward anticipation
(NAcc) 3. Make a decision (reject: Ins)
4. Reward prediction
error
9

10. Normal be a o of soc a interaction
o a behavior o social te act o
- responder behavior
• Face to face interaction
– Lower acceptance rate
100
single interaction
multiple interaction
– Different valuation
mechanism between the
80
single interaction and
Acceptance rates (%)
multiple interactions
60
*
e
40
*
A
20
0
5:5 7:3 8:2 9:1
Offer
Yun et al. OHBM 2007
10

11. Normal behavior of social interaction
– proposer behavior
• Face to face interaction
– More fairness valuation
100
5
90
80
4
70
Ne offer
3
Offer rate (%)
60
ext
es
50
2
40
1
30
20
0
0 1 2 3 4 5
10
Current offer
0
Slope: 0.86, R:0.73, P<0.0001
5:5 6:4 7:3 8:2 9:1
Offer

12. Normal behavior of social interaction
– dictator behavior
100
Ultimatum Game
• In the dictator game
90 Dictator Game
– No wish to maximize
other’s benefit
80
70
* – fairness
Offer rates (%)
60
(
50
– Avoid being seen as greedy
40
30
20
10 *
0
5:5 6:4 7:3 8:2 9:1
Offer

13. I
Important questions
t t ti
• Behavior: How do we valuate ‘fairness’
at the behavioral level?
• Physiology: what are the neural
y gy
mechanisms within and between the
brain that implement the decision
making?
• Th
Theory: C we formally d
Can f ll describe h
ib how
‘fairness’ is computed within the brain
p
(i.e. can we build a model?)

14. P i
Previous studies:
t di
emotion vs cognition
vs.
Sanfey et al. Science, 2003 14

15. Previous studies:
reward anticipation
Optimal investment strategy
Risk neutral
Risk Risk
seeking aversion
mistake mistake
NAcc preceded risky choices aIns preceded riskless choices
Distinct neural circuits
Consideration of anticipatory neural mechanisms may add
predictive power to the rational actor model of economic decision making
Kuhnen & Knutson, Neuron, 2005
15

16. What are the neural mechanisms of human decision
making in the context of emotion and cognition?
dlPFC Anterior
i
insula
ACC
CC
16

17. How does the brain process reward
o t e b a p ocess e a d
anticipation in the decision making?
Anterior
NAcc insula
Proposer divides the pie as 9:1 8:2 7:3 6:4 5:5
Risk taking ---------------------------------- risk averse
17

18. Electrophysiological correlates of decision
ect op ys o og ca co e ates o dec s o
making in the Ultimatum game
Yun et al OHBM 2007
al.
18

19. Functional connectivity
y
in the Ultimatum game
Yun et al. OHBM 2007 19

20. EEG hyperscanning
Client Client
Hyperscan server
and database
Controller Controller
20

21. Proposer
Proposer offer
Responder decision
Responder

22. Information processing in social interaction
(proposer offer -2sec ~ -1sec)
( ff 2 1 )
Proposer
p Responder
p
proposer responder proposer responder
from to from to from to from to
FC4 FC3 F5 FC3
FC4 CP1 FC3 F5
FC4 P1 FC3 C1
CP6 FP1 C1 FC3
CP6 C6 FC3 CP1
C6 CP6 CP1 FC3
CP6 P6 FC3 P1 22
P1 FC3

23. I
Important questions
t t ti
• Behavior: How do we valuate ‘fairness’
at the behavioral level?
• Physiology: what are the neural
y gy
mechanisms within and between the
brain that implement the decision
making?
• Th
Theory: C we formally d
Can f ll describe h
ib how
‘fairness’ is computed within the brain
p
(i.e. can we build a model?)

24. Temporal difference learning
δ(t) = r(t) + γV(s(t+1)) - V(s(t))
24
Schultz, Dayan, & Montague, Science, 1997

25. Temporal difference learning
δ(t) = r(t) + γV(s(t+1)) - V(s(t))
25
Schultz, Dayan, & Montague, Science, 1997

26. Temporal difference learning
δ(t) = r(t) + γV(s(t+1)) - V(s(t))
r
V
δ
r
V
δ
r
V
δ
Schultz, Dayan, & Montague, Science, 1997

27. Emotion vs. Cognition interaction model
Anterior
dlPFC insula
Vinsula = -Ψ*( (ERO-ER) * F )
Vdlpfc = Ψ * ( ER )
TD learning
Ψ: the hyperbolic tangent function
ACC
VACC = Ψ * ( Vdlpfc + Vinsula )
ER: expected reward
p
ERO: expected reward of opponent (theory of mind)
F: fairness representation (0~1) 27

28. 28

29. Modeling results of each fairness valuation parameters
and th d i i
d the decision making strategy
ki t t
fair (5:5)
( )
0.5 conflict (7:3)
unfair (9:1)
0.4
0.3
0.2
decision ratio
0.1
0.0
-0.1
-0.2
-0.3
-0 4
0.4
-0.5
0.0 0.2 0.4 0.6 0.8 1.0
Fairness
29

30. Modeling results of brain regional activation
Conflict it ti
C fli t situation (7:3) fairness value = 1
(7 3) f i l
High fairness valuation -> insula activation
0.5
dlPFC
0.4
04 Insula
ACC
0.3
V)
Model expected value (V
0.2
02
0.1
d
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
0 2 4 6 8
Time steps (cycle)
30

31. I
Important questions (1)
t t ti
• What are the neural mechanisms of human decision making
in the context of emotion and cognition?
• H
How does the brain process reward anticipation in the
d th b i d ti i ti i th
decision making?
• What are the temporal dynamics of reward circuitry?
(including reward anticipation, prediction error)
What h l l f i li i ?
• Wh are the neural correlates of social interaction?
(personal interaction)
• Are ultimatum rejections due to emotions, learned
heuristics, evolved modules, or combinations of these and
other mechanisms? – Camerer Trnds Cog Sci. 2003
Camerer, Trnds. Cog. Sci
31

32. I
Important questions (2)
t t ti
• Under what circumstances do these various systems
cooperate or compete? When there is competition, how
and where is it adjudicated? – Sanfey et al., Trnds Cog Sci.
al Trnds, Cog. Sci
2006
• Psychologists, neuroscientists and behavioral economists all
seem to agree that various automatic forms of behavior
(including emotional responses) reflect the operation of a
multiplicity of mechanisms. However, do higher-level
deliberative
d lib ti processes rely similarly on multiple mechanisms,
l i il l lti l h i
or a single, more tightly integrated (unitary) set of
mechanisms? – Sanfey et al., Trnds, Cog. Sci. 2006
y g
32

33. F t
Future implications
i li ti
• Prescriptive game theory
• Better theories of how people behave
will help in the design of economic
institutions
• Treatment of patients with impaired
decision making
33

34. Future applications
pp
Treatments – Novel Approaches
• damage to the insula disrupts addiction to cigarette
smoking
Naqvi et al. Science 2007
R h i d h ki i i hi i f i l
Researchers monitored the smoking quitting histories of approximately 70
smokers who had suffered various brain injuries, and found that smokers with
specific damage to the insula were much more likely to quit easily and
immediately and to remain abstinent than those with damage to other brain
areas

35. Fool me o ce, s a e o you.
oo e once, shame on
Fool me twice, shame on oxytocin.
Baumgartner et al. Neuron 2008

36. VM FC l i
VMpFC lesion vs. rPFC rTMS di
PFC TMS disruption
ti
• The rejection rate of the VMPC group was higher than the
rejection rates of the comparison groups for each of the
most unfair offers ($7/$3, $8/$2, $9/$1).
($7/$3 $8/$2 $9/$1)
• Disruption of the right, but not the left, dorsolateral
prefrontal cortex (DLPFC) by low-frequency repetitive
transcranial magnetic stimulation substantially reduces
subjects
subjects' willingness to reject their partners' intentionally
partners
unfair offers, which suggests that subjects are less able to
resist the economic temptation to accept these offers.
• Importantly, however, subjects still judge such offers as very
unfair, which indicates that the right DLPFC plays a key role
in the implementation of fairness-related behaviors.
Koenigs & Tranel J Neurosci 2007
Tranel, Neurosci.
Knoch et al. Science 2006

37. Emotion expression in human
p
punishment behavior
37

38. C
Conclusions
l i
• My research will provide evidence for behavioral, physiological
and computational approaches to social interaction and decision
making that stress the fundamental role of cortical and
subcortical areas in neural networks that support deliberative and
emotional fairness valuation and reward learning processes in
human decision making.
38