Neural mechanisms of decision making - emotion vs. cognition

Neural mechanisms of decision makingNeural mechanisms of decision making -emotion vs. cognition

Prepared for Lab seminar

2008 06 09 9 #3239 CT2008.06.09, 9a.m. #3239, CT

Kyongsik Yun Ph D CandidateKyongsik Yun, Ph.D. CandidateKAIST

[email protected]@kaist.edu

“The mind is a charioteer driving twin horses of reason and emotion Excepthorses of reason and emotion. Except

cognition is a smart pony, and emotion an elephant”

Colin Camerer & George Loewenstein— Colin Camerer & George Loewenstein

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[Resarch interests]U d t di th l b f d i i ki i t t i i t ti

Research Summary

- 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 theoryg, y , y- Functional neuroimaging data analysis including fMRI and EEG

Understanding the Neural mechanisms of Decision Making

in the context of Emotion vs Cognitionin the context of Emotion vs. Cognition

Behavioral game theory and i Ulti t

Impaired decision making in Computational modeling and i l ti i f tneuroeconomics – Ultimatum

game

p gneuropsychiatric disorders –methamphetamine addiction, schizophrenia, Alzheimer’s Disease adolescence

simulation – reinforcement learning

Methods- Nonlinear dynamic analysis- EEG analysis for high temporal resolution information processing

Disease, adolescence

- EEG analysis for high temporal resolution information processing- fMRI analysis for functional connectivity and large-scale communication between the brain regions

I t t tiImportant questions

• Behavior: How do we valuate ‘fairness’ at the behavioral level?at the behavioral level?

• Physiology: what are the neural y gymechanisms within and between the brain that implement the decisionbrain that implement the decision making?Th C f ll d ib h• Theory: Can we formally describe how ‘fairness’ is computed within the brain p(i.e. can we build a model?)

What are the temporal dynamics of p ysocial interaction?

proposer responder

5


proposer responder

1. Make an offer: 9:1(send emotional cue)(send emotional cue)Reward anticipation (NAcc)

6


proposer responderresponder

1. Make an offer: 9:1(send emotional cue)

2. Conflict btwn emotion & cognitionACC Ins dlPFC acti ation (interaction)(send emotional cue)

Reward anticipation (NAcc)

ACC, Ins, dlPFC activation (interaction)

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proposer responderresponderresponder





3. Make a decision (reject: Ins)

8


proposer responderresponderresponderproposer





3. Make a decision (reject: Ins)

4. Reward prediction error

9

Normal behavior of social interactiono a be a o o soc a te act o- responder behavior

• Face to face interaction– Lower acceptance rate

100single interactionLower acceptance rate

– Different valuation mechanism between the

80

single interaction multiple interaction

single interaction and multiple interactions 60

e ra

tes

(%)

*40

Acc

epta

nce *

*

0

20A *

5:5 7:3 8:2 9:10

Offer

10

Yun et al. OHBM 2007

Normal behavior of social interaction – proposer behavior

• Face to face interaction– More fairness valuation

100

More fairness valuation

80

90

4

5

50

60

70

es (%

)3

ext o

ffer

30

40

50O

ffer r

ate

1

2Ne

10

200 1 2 3 4 5

0

Current offer

5:5 6:4 7:3 8:2 9:10

OfferSlope: 0.86, R:0.73, P<0.0001

Normal behavior of social interaction – dictator behavior

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100 Ultimatum Game Dictator Game

• In the dictator game– No wish to maximize

60

70

80

(%) *

No wish to maximize other’s benefit

– fairness

30

40

50

Offe

r rat

es (

– Avoid being seen as greedy

0

10

20

*5:5 6:4 7:3 8:2 9:1

0

Offer

P i t diPrevious studies: emotion vs cognitionemotion vs. cognition

Sanfey et al. Science, 2003 14

Previous studies:Previous studies: reward anticipation

Optimal investment strategy

Risk neutral

Risk Risk seeking aversion

mistake

seeking mistake

NAcc preceded risky choices aIns preceded riskless choices

Distinct neural circuits

Consideration of anticipatory neural mechanisms may addpredictive power to the rational actor model of economic decision makingpredictive power to the rational actor model of economic decision making

Kuhnen & Knutson, Neuron, 200515

What are the neural mechanisms of human decisionWhat are the neural mechanisms of human decision making in the context of emotion and cognition?

iAnterior insula

dlPFC

ACCCC

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How does the brain process reward o does t e b a p ocess e a danticipation in the decision making?

Anterior insulaNAcc

Proposer divides the pie as 9:1 8:2 7:3 6:4 5:5

Risk taking ---------------------------------- risk averse

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Electrophysiological correlates of decision ect op ys o og ca co e ates o dec s omaking in the Ultimatum game

Yun et al OHBM 2007

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Yun et al. OHBM 2007

Functional connectivity yin the Ultimatum game

19Yun et al. OHBM 2007

EEG hyperscanningEEG hyperscanning

Client Client

Hyperscan serverand database

Controller Controller

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Proposer

Proposer offer

Responder decision

Responder

Information processing in social interaction ( ff 2 1 )(proposer offer -2sec ~ -1sec)

ResponderProposer ppproposer responder

from to from to

FC4 FC3

proposer responder

from to from to

F5 FC3FC4 CP1

FC4 P1

CP6 FP1

CP6 C6

FC3 F5

FC3 C1

C1 FC3

FC3 CP1

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CP6 C6

C6 CP6

CP6 P6

FC3 CP1

CP1 FC3

FC3 P1

P1 FC3

Temporal difference learningδ(t) = r(t) + γV(s(t+1)) - V(s(t))

Schultz, Dayan, & Montague, Science, 199724


r

V

δ

r

V

δ

r

V

δ


δ

Emotion vs. Cognition interaction model

Anterior insuladlPFC

Vdlpfc = Ψ * ( ER )Vinsula = -Ψ*( (ERO-ER) * F )

dlpfc ( )

TD learning

Ψ: the hyperbolic tangent function

ACCACC

V = Ψ * ( V + V )

ER: expected reward

VACC = Ψ * ( Vdlpfc + Vinsula )

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pERO: expected reward of opponent (theory of mind)F: fairness representation (0~1)

Modeling results of each fairness valuation parameters d th d i i ki t t

fair (5:5)

and the decision making strategy

0.4

0.5( )

conflict (7:3) unfair (9:1)

0.2

0.3

0.0

0.1

n ra

tio

-0.2

-0.1

deci

sio

-0 4

-0.3

0.0 0.2 0.4 0.6 0.8 1.0-0.5

0.4

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Fairness

Modeling results of brain regional activationC fli t it ti (7 3) f i l 1Conflict situation (7:3) fairness value = 1High fairness valuation -> insula activation

0 4

0.5 dlPFCInsula

0 2

0.3

0.4

V)

Insula ACC

0.1

0.2

d va

lue

(V

-0.1

0.0

expe

cted

-0.3

-0.2

Mod

el

0 2 4 6 8-0.5

-0.4

30

0 2 4 6 8

Time steps (cycle)

I t t ti (1)Important questions (1)• What are the neural mechanisms of human decision making

in the context of emotion and cognition?H d th b i d ti i ti i th• How does the brain process reward anticipation in the decision making?

• What are the temporal dynamics of reward circuitry? (including reward anticipation, prediction error)Wh h l l f i l i i ?• What 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 2003other mechanisms? – Camerer, Trnds. Cog. Sci. 2003

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I t t ti (2)Important questions (2)• 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 Sciand where is it adjudicated? – Sanfey et al., Trnds, Cog. Sci. 2006

• Psychologists, neuroscientists and behavioral economists all seem to agree that various automatic forms of behaviorseem to agree that various automatic forms of behavior (including emotional responses) reflect the operation of a multiplicity of mechanisms. However, do higher-level d lib ti l i il l lti l h ideliberative processes rely similarly on multiple mechanisms, or a single, more tightly integrated (unitary) set of mechanisms? – Sanfey et al., Trnds, Cog. Sci. 2006y g

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F t i li tiFuture implications

• Prescriptive game theory• Better theories of how people behave

will help in the design of economicwill help in the design of economic institutions

• Treatment of patients with impaired decision makingdecision making

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Future applicationsppTreatments – Novel Approaches

• damage to the insula disrupts addiction to cigarette smoking

R h i d h ki i i hi i f i l 70

Naqvi et al. Science 2007

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

Fool me once, shame on you. oo e o ce, s a e o you.Fool me twice, shame on oxytocin.

Baumgartner et al. Neuron 2008

VM FC l i PFC TMS di tiVMpFC lesion vs. rPFC rTMS disruption

• The rejection rate of the VMPC group was higher than therejection rates of the comparison groups for each of themost unfair offers ($7/$3 $8/$2 $9/$1)most unfair offers ($7/$3, $8/$2, $9/$1).

• Disruption of the right, but not the left, dorsolateralDisruption of the right, but not the left, dorsolateralprefrontal cortex (DLPFC) by low-frequency repetitive transcranial magnetic stimulation substantially reduces subjects' willingness to reject their partners' intentionallysubjects willingness to reject their partners intentionally 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 roleunfair, which indicates that the right DLPFC plays a key role in the implementation of fairness-related behaviors.

Koenigs & Tranel J Neurosci 2007Koenigs & Tranel, J Neurosci. 2007Knoch et al. Science 2006

Emotion expression in human ppunishment behavior

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C l iConclusions• 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 inemotional fairness valuation and reward learning processes in human decision making.

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Neural mechanisms of decision making - emotion vs. cognition

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