Neuroeconomics: The Neurobiology of Decision-Making
Transcript of Neuroeconomics: The Neurobiology of Decision-Making
Neuroeconomics: The Neurobiology of Decision-Making
Ifat Levy Section of Comparative Medicine
Department of Neurobiology Interdepartmental Neuroscience Program
Yale School of Medicine
Harnessing eHealth and Behavioral Economics for HIV Prevention and Treatment
April 2012
Overview
• Introduction to neuroeconomics
• Decision under uncertainty – Brain and behavior
– Adolescent behavior
– Medical decisions
Overview
• Introduction to neuroeconomics
• Decision under uncertainty – Brain and behavior
– Adolescent behavior
– Medical decisions
Neuroscience Psychology Economics
Neuroeconomics
Abstraction
“as if” models Mental states Neuronal architecture
Neuroscience Psychology Economics
Neuroeconomics
Abstraction
Behavioral Economics
Neuroscience
VISUAL STIMULUS
functional MRI
functional MRI: Blood Oxygenation Level Dependent signals
Changes in oxygen consumption, blood flow and blood volume
Signal from each point in space at each point in time
Neural activity
Change in concentration of deoxyhemoglobin
Change in measured signal
t = 1 t = 2
t = 3 t = 4
t = 5 t = 6
anterior posterior
dorsal
ventral
lateral
anterior posterior
dorsal
ventral
medial
Medial Prefrontal Cortex (MPFC)
Orbitofrontal Cortex (OFC)
Posterior Cingulate Cortex (PCC)
Ventromedial Prefrontal Cortex (vMPFC)
Anterior Cingulate Cortex (ACC)
The cortex
Sub-cortical structures
fMRI signal
• Spatial resolution: ~3x3x3mm3
• Temporal resolution: ~1-2s
• Number of voxels: ~150,000
• Typical signal change: 0.2%-2%
• Typical noise: more than the signal…
Low
Low
High
Low
High
But…
• Intact human brain
• Behaving human
• Whole brain
• Non-invasive
Neuroscience Psychology Economics
Neuroeconomics
Abstraction
Behavioral Economics Cognitive Neuroscience
New challenge: how do you make sense of such huge
amounts of data??
Neuroeconomics
Behavioral Economics
Cognitive Neuroscience
Economic models as normative theory
Mechanistic constraints of the human brain
Overview
• Introduction to neuroeconomics
• Decision under uncertainty – Brain and behavior
– Adolescent behavior
– Medical decisions
100%
partial relief
50%
full remission
unknown
full remission
certainty risk ambiguity
Most people choose A, implying that B has fewer red than blue chips: red < blue
Most people choose A, implying that B has fewer blue than red chips: blue < red
OR
OR
A
A B
B
Choose one:
Risky Ambiguous
Risky Ambiguous
The Ellsberg paradox: a bag cannot have
fewer red chips and fewer blue chips at the same time
• Risk – probabilities of different outcomes are known
• Ambiguity – probabilities of different outcomes are not known
• Partial ambiguity – partial information
Non-certain outcomes
risk aversion
high probability low reward
low probability high reward
$40
known probability low reward
unknown probability high reward
unknown probability ambiguity aversion
known probability
$110
$0
$0
$110
Value of risk and ambiguity
Risk and ambiguity affect the subjective value of an option
in very different ways
Overview
• Introduction to neuroeconomics
• Decision under uncertainty – Brain and behavior
– Adolescent behavior
– Medical decisions
single system multiple systems
neural representation of value
Research Question
reward punishment
immediate delayed
cognitive
emotional
reward punishment immediate
delayed cognitive
emotional . . .
single system multiple systems
neural representation of value
vs. ambiguity risk ambiguity & risk
x,y,0.5,5
Research Question
Experimental design
Experimental design
OR:
Parametric design
Amount
Probability
Ambiguity level
Winning color
$5 - -
Real bags One trial played for real money
Amount [$]
p (c
hose
ris
ky)
p = 0.75
subject 1
$5
Gain-risk trials
Amount [$]
p (c
hose
ris
ky)
p = 0.75
subject 1
$5
Gain-risk trials
Amount [$]
p (c
hose
ris
ky)
Gain-risk trials
p = 0.75
subject 1
$5
Amount [$]
p = 0.75
subject 1
$5
p (c
hose
ris
ky)
Gain-risk trials
Amount [$]
p = 0.75
subject 1
$5
p (c
hose
ris
ky)
Gain-risk trials
Amount [$]
p (c
hose
ris
ky)
p = 0.75
subject 1
$5
p = 0.50
Gain-risk trials
Amount [$]
p (c
hose
ris
ky)
p = 0.75
subject 1
$5
p = 0.50 p = 0.38
Gain-risk trials
Amount [$]
p (c
hose
ris
ky)
p = 0.75
subject 1
$5
p = 0.50 p = 0.38 p = 0.25
Gain-risk trials
Amount [$]
p (c
hose
ris
ky)
p = 0.75
subject 1
$5
p = 0.50 p = 0.38 p = 0.25 p = 0.13
Gain-risk trials
Amount [$]
p (c
hose
am
bigu
ous)
A = 0.25
subject 1
$5
Gain ambiguity trials
Amount [$]
p (c
hose
am
bigu
ous)
A = 0.25
subject 1
$5
Gain ambiguity trials
Amount [$]
p (c
hose
am
bigu
ous)
A = 0.25
subject 1
$5
A = 0.50
Gain ambiguity trials
Amount [$]
p (c
hose
am
bigu
ous)
A = 0.25
subject 1
$5
A = 0.50 A = 0.75
Gain ambiguity trials
) 2
( A β − amount
V p probability
α risk
preference
· subjective value
ambiguity aversion
ambiguity level
stochastic choice model
Behavioral model MaxMin, Gilboa and Schmeidler 1989
p (c
hose
lott
ery)
Amount [$] Amount [$]
p = 0.75 p = 0.50 p = 0.38 p = 0.25 p = 0.13 A = 0.25 A = 0.50 A = 0.75
S1: gains
α = 0.55, β = 0.89 α = 0.58, β = -0.03
S2: gains
p = 0.75 p = 0.50 p = 0.38 p = 0.25 p = 0.13 A = 0.25 A = 0.50 A = 0.75
p (c
hose
lott
ery)
Amount [$]
S1: gains
α = 0.55, β = 0.89
Amount [$]
α = 0.58, β = -0.04
S1: losses
Ambi
guity
ave
rsio
n
Risk aversion Risk aversion
Risk aversion Ambiguity aversion
Loss
es
Gains Gains
Losses Gains
αβ VAp ⋅− )2
(
… … time
unde
r am
bigu
ity
unde
r ris
k
subj
ectiv
e va
lue
Subjective value under ambiguity
19 subjects, random effect analysis P<0.002 P<0.0001
R L
ACC / MPFC
caudate posterior cingulate amygdala
Subjective value under risk
19 subjects, random effect analysis P<0.01 P<0.001
R L
ACC / MPFC
caudate posterior cingulate amygdala
PCC amygdala
Unique areas for SV under ambiguity?
ambiguity risk
% s
igna
l cha
nge
% s
igna
l cha
nge
ambiguity risk
No…
single system multiple systems
neural representation of value
vs. ambiguity risk ambiguity & risk
x,y,0.5,5
Research Question
Uncertainty Summary 1
• High variability in risk and ambiguity attitudes across individuals
• Areas in MPFC and striatum represent subjective value under both risk and ambiguity
Can attitudes towards risk and ambiguity explain phenomena like risk-taking in adolescents and overeating in obese
individuals?
Overview
• Introduction to neuroeconomics
• Decision under uncertainty – Brain and behavior
– Adolescent behavior
– Medical decisions
• 200% increase in morbidity and mortality rates in adolescence compared to childhood (Dahl, 2004)
• Adolescents are physically healthier and stronger than both children and adults (Dey et al., 2004)
• Increase mostly attributed to risky behaviors: car accidents, alcohol and substance abuse, violence, eating disorders, unsafe sex (Reyna and Farley, 2006)
• Not due to flawed reasoning capabilities, poor decision-making skills or failure to understand the consequences of their actions (Reyna and Farley, 2006)
Adolescents take risks
Subjects
Age \ Gender
Female Male Total
12-17 17 16 33
21-25 18 16 34
30-50 17 15 32
65-90 18 17 35
Total: 70 64 134
In collaboration with Paul Glimcher
Adolescents vs. adults
Uncertainty Summary 2
• Adolescents are more risk averse, but less ambiguity averse than adults
• Young organisms need to learn about their world
Do people treat risk and ambiguity similarly in different domains?
In collaboration with Terri Fried
Overview
• Introduction to neuroeconomics
• Decision under uncertainty – Brain and behavior
– Adolescent behavior
– Medical decisions
“You were involved in a car accident and as a result suffered traumatic brain injury. You were immediately rushed to the nearest hospital and were informed by the doctor that without immediate treatment you will not survive.”
Gains and losses in medical decisions
Gains: cognitive improvement
Major improvement = Mild cognitive disability: mild memory impairment resulting in forgetting some appointments, forgetting people’s names, needing a list to do food shopping
No effect Slight improvement Moderate improvement Major improvement Recovery
Worst outcome Best outcome
Slight improvement
No effect
recovery
or
Major improvement
No effect
or Slight
improvement
Gains and losses in medical decisions
Losses: headache as an adverse effect
Moderate headache: improves but does not resolve with acetaminophen (Tylenol); requires you to lie down occasionally to relieve pain; occurs a couple of times a week.
Critical headache Severe headache Moderate headache Mild headache Recovery
Worst outcome Best outcome
Mild headache
moderate headache
recovery
or
Mild headache
Severe headache
recovery
or
Decision under risk
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.13 0.25 0.38 0.5 0.75 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.13 0.25 0.38 0.5 0.75
Prop
ortio
n of
lott
ery
choi
ces
Outcome probability Outcome probability
Money Medical N = 29
Positive outcomes Negative outcomes
Decision under ambiguity Pr
opor
tion
of lo
tter
y ch
oice
s
0.75 N = 29
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
gain loss
Money
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
improvement adverse effect
Medical
0 0.50 0.25 Ambiguity level:
Uncertainty Summary 3
• Ambiguity aversion was observed both under gains and under losses when making medical decisions
Summary • Economic models can be used to make sense
of neural data
• High variability across subjects in attitudes towards risk and ambiguity
• Adolescents are more risk averse and less ambiguity averse than adults
• Ambiguity aversion in medical decision-making for both positive and negative outcomes
Acknowledgements Collaborators
Paul Glimcher, NYU
Aldo Rustichini, Minnesota U
Agnieszka Tymula, NYU
Amy Roy, Fordham
Terri Fried, Yale
Scott Huettel, Duke
Linda Mayes, Yale
Michael Crowley, Yale
Ashley Gearhardt, Yale
Eric Jackson, Yale
Daniela Schiller, Mount Sinai
Lab
Sarah Abdallah
Jennifer Fanning
Ellen Furlong
Patrick Kenney
Genny Ladiges
Kirk Manson
Helen Pushkarskaya
Lior Rosenberg Belmaker
Lital Ruderman
Sana Samnani
Jeannie Tran
Zhihao Zhang
Funding
NIA, Pepper Center
Sense of incompetence (Heath and Tversky, 1991)
People prefer to bet on events in their field of expertise, even when they judge the probabilities as equal
Comparative ignorance (Fox and Tversky, 1995)
How much will you pay for playing the lottery?
Within subject
>
Between subject
= More recent study: Ambiguity aversion is reduced but not abolished (Chow and Sarin 2001)
Informed opponent (Kuhberger and Perner, 1991)
Subjects chose the ambiguous option more when the person who filled the bag was a partner than when it was an opponent
Experimental design
Real bags!
Risk
Ambiguity
• Subjects were endowed with $125 • Gain and loss trials • Choice between a lottery and a certain amount (±$5) • 3 ambiguity levels: 0.25, 0.5, 0.75 • 5 risk levels: 0.75, 0.5, 0.38, 0.25, 0.13 • 5 outcome levels: ±$5, ±$8, ±$20, ±$50, ±$125 • 320 trials • 1 trial randomly selected and played for real money
Experimental design
) 2
( A β − amount
V p probability
α risk
preference
· subjective value
ambiguity aversion
ambiguity level
stochastic choice model
Behavioral model MaxMin, Gilboa and Schmeidler 1989
Amount [$] Amount [$]
p = 0.75 p = 0.50 p = 0.38 p = 0.25 p = 0.13 A = 0.25 A = 0.50 A = 0.75
S2: gains
α = 0.58, β = -0.03 α = 0.86, β = 0.72
S2: losses p
(cho
se lo
tter
y)
MPFC
ambiguity risk
Subjective value in ambiguity defined regions %
sig
nal c
hang
e
striatum
% s
igna
l cha
nge
ambiguity risk
Subjective value in risk defined regions
ambiguity risk
% s
igna
l cha
nge
striatum
% s
igna
l cha
nge
ambiguity risk
MPFC
• 200% increase in morbidity and mortality rates in adolescence compared to childhood (Dahl, 2004)
• Adolescents are physically healthier and stronger than both children and adults (Dey et al., 2004)
• Increase mostly attributed to risky behaviors: car accidents, alcohol and substance abuse, violence, eating disorders, unsafe sex (Reyna and Farley, 2006)
• Not due to flawed reasoning capabilities, poor decision-making skills or failure to understand the consequences of their actions (Reyna and Farley, 2006)
Adolescents take risks
And in the brain…
• Gray matter maturation processes in PFC and striatum continue into adolescence (Giedd et al., 1996, 1999, 2004)
• Frontal increase in white matter occurs late and extends into adulthood (Fuster, 2002)
• Structural atrophy and decline in dopamine receptors in striatum and PFC in aging (Backman et al., 2000; Volkow et al., 1998)
• Altered striatal activation during gain anticipation in adolescents compared to adults (Ernst et al., 2005; Galvan et al., 2006; Bjork et al., 2004)
• Reduction in activation in striatal areas during loss anticipation in older adults (Samanez-Larkin et al., 2007).
Adolescents vs. adults
Controls
Controls
Controls
“Cognitive” blocks No effect = The treatment failed. You end up in a vegetative state. Slight improvement = Severe cognitive disability: severe memory impairment resulting in inability to recognize your loved ones. Moderate improvement = Moderate cognitive disability: moderate memory impairment resulting in inability to work and participate in leisure activities such as playing cards or doing crossword puzzles. Major improvement = Mild cognitive disability: mild memory impairment resulting in forgetting some appointments, forgetting people’s names, needing a list to do food shopping. Recovery = return to your initial cognitive ability prior to the accident.
“Headache” blocks
Recovery = successful treatment with no side effects.
Mild headache: responds to acetaminophen (Tylenol); does not interfere with daily activities; occurs a couple of times a week.
Moderate headache: improves but does not resolve with acetaminophen (Tylenol); requires you to lie down occasionally to relieve pain; occurs a couple of times a week.
Severe headache: not responsive to acetaminophen (Tylenol); requires stronger pain medication, which does not fully relieve pain; requires you to lie down frequently to relieve pain; occurs daily.
Critical Headache: Severe headache (as above) accompanied by other symptoms, such as nausea and vomiting.