Read Montague

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Cognition and Decision-Making
Read Montague Baylor College of Medicine
Houston, TX www.hnl.bcm.tmc.edu
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Fundamental difference between brains and
computers
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(No Transcript)
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DEEP BLUE
32 nodes 8 dedicated chess processors per node
(total 256) 1.4 tons (lots of AC) 200,000,000
positions per second
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Deep Blue was wildly inefficient
D
Kasparov stayed warm to the touch
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functional MRI (fMRI)
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Relating brain responses to behavior perception
Current work is moving away from psychological
categories to hypothesized computational
functions
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Why should brands influence tastes?
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Goal-directed choice
Select goal
Sustain goal
Pursue goal
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Sharks dont go on hunger strikes
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Goal pursuit requires guidance signals ? dopamine
Valuation
Dopamine neurons
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Relating valuation to choice
Response to stimulus (art, brand name)
Internal Valuation
Make movement or reveal choice
In scanner
Outside scanner
?
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The brain has guidance signals related to ongoing
valuation
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Goal-directed choice
Select goal
Sustain goal
Pursue goal
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Midbrain dopamine neurons
Pause, burst, and no change responses
represent reward prediction errors ongoing
emission of information
ERROR SIGNAL current reward g next
prediction - current prediction
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Can we detect a reward prediction error signal in
a human subject?
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Neural correlates of these error signals have
been observed in conditioning and decision tasks
Social and economic exchange tasks
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Induce reward prediction error in a passive
conditioning task ? measure brain response
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y 3
BETTER THAN EXPECTED
WORSE THAN EXPECTED
McClure et al., Neuron, 2003
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Midbrain dopamine neurons
burst and pause responses encode reward
prediction errors
ERROR SIGNAL current reward g next
prediction - current prediction
Can these systems be re-deployed for abstractly
defined rewards (ideas)?
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What about the something more abstract like the
expression of trust?
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Trust
Modeling
Must involve risk (uncertainty)
Social lubricant
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TRUST
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Simplifying and quantifying Trust(Berg et al.,
1995 Weigelt and Camerer, 1988)
Trust is the amount of money a sender sends to a
receiver without external enforcement.
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A dynamic version of the Trust game (10 rounds)
X 3
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Investor
Trustee
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Scan all brains during the interaction
information www.hnl.bcm.tmc.edu/hyperscan softw
are download www.hnl.bcm.tmc.edu/nemo
client
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Structure of a round
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What is the behavioral signal that most strongly
influences changes in trust (money sent) ?
Reciprocity TIT-FOR-TAT
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Reciprocity TIT-FOR-TAT
money sent to partner
Neutral signal
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Trustee Brain intention to increase trust
shifts with reputation building
Signal is now anticipating the outcome
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Temporal shift resembles value transfer in
reward learning experiments
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What about something more culturally dependent
like a brand image?
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Taste and preference are perceptual constructs
Numerous variables
Subject to illusions
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orientation
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3 pairs Or 15 pairs
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No correlation between stated preference and
revealed preference
n1 18
n2 18
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Ave. Coke Selections (out of 15)
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2
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Prefer Coke
Prefer Pepsi
Carbonated
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Imaging part of experiment
outside scanner
inside scanner
6s
P
C
P
6s
C
train
P
C
6s
4s
P
P
C
6s
4s
C
test
Contrast surprising Coke delivery to surprising
Pepsi delivery
Analogous to Smiling Crying contrast from before
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Response that correlates with anonymous
behavioral preference
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Coke must taste different when you think its
coke, do you think we could image that?
Latane Montague high school student
What does the idea of the brand taste like?
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Same systems engaged by visual art during passive
viewing
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Simply look at art in the scanner
5s
4-14s (random)
5s
4-14s (random)
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Liking and familiarity ratings outside scanner
-4
4
0
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Common valuation response?
Use idiosyncratic ratings for preference and
familiarity for each individual
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1. All decisions derive from biological
mechanisms and will have some kind of associated
brain responses
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2. Abstractions that become goals appear to
re-deploy reward-harvesting circuitry that we
share with all vertebrates
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3. Relationship between choice and brain response
is statistical, never deterministic
However, choices can be systematically
predictable using brain imaging data
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Collaborators
Baylor College of Medicine Pearl Chiu Amin
Kayali Brooks King-Casas Terry Lohrenz Sam
McClure (Princeton) Read Montague Damon
Tomlin Caltech Cedric Anen Colin Camerer Steve
Quartz UCL Peter Dayan Nathaniel Daw Salk
Institute Terry Sejnowski Princeton
University Jon Cohen
Emory University Greg Berns University of
Alabama Laura Klinger Mark Klinger Families of
autistic subjects
Funding Sources NIDA NIMH Dana Foundation Kane
Family Foundation Angel Williamson Imaging
Center Institute for Advanced Study, Princeton NJ
www.hnl.bcm.tmc.edu/trust