Virginia Tech Carilion Research InstituteDepartment of Physics, Virginia Tech2 Riverside Circle, Roanoke, VA 24014
Computational Psychiatry and the Mentalization of Others
Wellcome Trust Centre for NeuroimagingUniversity College London, 12 Queen Square, London, UK, WC1N 3BG
P. Read Montague
2
Did (lack of) free trade contribute to Neanderthal downfall?
Shogren, J, Horan, R, Bulte, E (2005) How Trade Saved Humanity from Biological Exclusion: An Economic Theory of Neanderthal Extinction.” Journal of Economic Behavior and Organization.
1. Response to ‘fair’ reciprocity (social norms)2. Depth-of-thought (excellent for ASD)3. Sensitivity to horizon (planning)4. Sensitivity to history of play (working memory)5. Ability to learn - to model other subject
Reciprocation exhibits patterns and requires lots of cognition
BA BA BA
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r
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Alan Turing
R1
0 01 0$
*0
Turing Machine
Hardware (physical interactions)
Patterns of information processing
Computation Computational Theory of Mind (CTOM)
Mind is equivalent to information processing supported by brains.
How does one get ‘mind-like’ stuff from ‘stuff-like’ stuff ?
Turing proposed a solution to an age-old problem
Growing body of cellular & molecular data
Behavior, thoughts,moods, etc.
ComputationalModels and methods
The prevailing model (ambition) of computational neuroscience
complex behaviors, thoughts, moods, etc ascomputations.
neuronal interactions as computations.
Computational Psychiatry
http://computationalpsychiatry.org/
translating computational neuroscience
1. Biochemical networks, synapses, cells
2. Neuronal networks
3. System-level brain responses (fMRI, fNIRS, MEG, EEG)
4. Cognitive variables and their dynamics (behavior, perception)
5. Groups, social interactions, interactions-with-culture
Complication 1 – many levels
but there are other big complications…
Complication 2: Validation in model organisms?
e.g. is rodent empathy a model for human empathy?
human
We can profit from reversing model organism pipeline.
hypothesis: There is a software gap
Use well-defined economic probes - normative solutions
What are we doing?
Other minds task battery
Uncertainty and rewardtask battery
Ultimatum game - simple probe of mentalizing
BA
Offer split ($60 : $40) ?
accept or reject
What is fair? What is fair?
What does bluethink I think is fair?
What does redthink I think is fair?2nd order beliefs
What does bluethink is fair?
What does redthink is fair?1st order beliefs
Why use economic games?
1. possess natural learning signals (deviations)
2. equipped with concept of optimal play
3. new variables and dynamics
economic variables and their dynamics
‘standard’ cognitive
constructs
RBS version 1 ‐Ultimatum‐ proposer role‐Dictator‐ proposer role‐Trust‐ investor role‐Risk Aversion‐Temporal Discounting‐Passive film viewing‐Structural scan‐Resting state scan
RBS version 2 ‐Ultimatum‐ responder role‐Trust‐ investor role‐Risk Aversion‐Temporal Discounting‐Filmotyping‐Structural scan‐Resting state scan
Reinforcement Learning US‐Go/No‐Go‐Two‐Step‐Skewness‐High/Low Card‐Structural scan‐Resting state scan
Social Exchange US‐Ultimatum‐ responder role‐Trust‐ investor role‐Bargaining‐ buyer and seller role‐Social hierarchy‐Structural scan‐Resting state scan
Social Exchange UK‐Ultimatum‐ responder role‐Trust‐ investor role‐Bargaining‐ buyer and seller role‐Social hierarchy‐Structural scan‐Resting state scan
Other minds task battery between UK and US
Borderline PD (BPD)Anti-social PD (ASPD)
Investor Trustee
$20
Measuring reciprocity and model-building with a10-round ‘trust’ game
repay
pay (x3)
investperiod
8 s 8 s10 s 10 s 10 s8 s8 s4 s4 s
cue to invest
repaymentrevealed toboth brains
Gave
13
Kept
29
investmentrevealed toboth brains
Kept Gave
6 14
repayperiod
cue to repay
delayperiod
delayperiod
delayperiod
delayperiod
investment phase repayment phase
Totals
2919
totalsrevealed toboth brains
inter-rounddelay period
investperiod
8 s 8 s10 s 10 s 10 s8 s8 s4 s4 s
cue to invest
repaymentrevealed toboth brains
Gave
13
Kept
29
Gave
13
Kept
29
investmentrevealed toboth brains
Kept Gave
6 14
Kept Gave
6 14
repayperiod
cue to repay
delayperiod
delayperiod
delayperiod
delayperiod
investment phase repayment phase
Totals
2919
Totals
2919
totalsrevealed toboth brains
inter-rounddelay period
Structure of a round
The game engages prediction systems consistent with mesostriatal dopaminergic responses
*
time (sec)-8 0 10
-0.2
0
0.2
Trustee ‘intention to increase trust’ shifts with reputation building
Reputationdevelops across
rounds
Trustee will increasetrust on next move
Trustee will decreasetrust on next move
submit reveal *
increases or decreasesin future trust by trustee
-0.2
0
0.2
time (sec)
Signal nowanticipatesoutcome
reciprocity modulatedvoxels
Adapted from King-Casas et al., 2005 Science 308:78-83
Why is this temporal shift provocative?
Midbrain dopamine neurons
burst
pause
R timenaive
R timeAfter learning
Pause, burst, and ‘no change’ responses representreward prediction errors
Cooperation always breaks…
Application to Borderline Personality Disorder
Borderline PD subjectsinduce a break in cooperation
Adapted from King-Casas et al., 2008 Science 321:806-810
BPD trustee
Healthy trustee
Borderline PD anterior insula response does not differentiate offer levels (input insensitivity)
unfair fair
unfair fair
Adapted from King-Casas et al., 2008 Science 321:806-810
receivesignal
sendsignal
BPD anterior insula response occurs only for ‘input’
control BPD
1. Behavioral signal – sustained cooperation breakdown
In BPD subjects, social exchange game exposes:
3. Selectivity of insula signal – social sensing deficit?
2. Neural signal – insular response unmodulated by fairness
All the effects disappear in proposer role.
Application across a range of psychopathology groups
Biosensor approach to psychopathology classification
R
I
proposer responder
Psychopathology group always played responder role
MDD, ADHD, ASD, BPD medicated, BPD unmedicated, controls
The idea: Humans are sensitive detectors of interpersonal exchange patterns –exploit this capacity as a kind of device.
BA BA BA
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20 numbers
Koshelev et al. (2010). PLoS Comput Biol 6(10), e1000966.
Biosensor approach to psychopathology classification
regress preceding I’s and r’s onto the next investment, cluster on the regression coefficients
over-represented in cluster
under-represented in cluster
Biosensor approach to psychopathology classification
Koshelev et al. (2010). PLoS Comput Biol 6(10), e1000966.
Theory-of-mind model based classification of behavior
Use observed exchanges and computational model to classify investors using depth-of-thought and inequality aversion
investor trustee
10 rounds$20
multi-round trust game
n = 195 pairs
Examine 1st and 2nd orderinterpersonal prediction errors in brains for each depth-of-thought level
investor trustee
Level 0
Level 1
Level 2
Strategy
30Ray D, et al. Bayesian model of behaviour in economic games. NIPS (2008)
How to determine a player’s type and depth-of-thought?
31
• Compute the probability of taking certain actions
• Fit model to actual behavior
• Maximize the likelihood of observing the actual behavior given all possible types and levels of depth-of-thought
050
100150200250300
Level 0 Level 1 Level 2
Tota
l ear
ning
s (m
onet
ary
units
)
Depth-of-thought of investor
*
ns
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10
Inve
stm
ent r
atio
Round number
investor trustee
0%10%20%30%40%50%60%
Level 0 Level 1 Level 2
Perc
enta
ge
Depth-of-thought of investor
Depth-of-thought classification captures distinct behavioral characteristics
Xiang T, Ray D, et al. (2012). Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought. PLoS Comput Biol 8(12).
Depth‐of‐thought (investor)
level 0 level 2level 1
Y = 0
70corrected p < 0.01
Y = 0
Y = 0
Y = 0
p < 0.0001p < 0.001uncorrected
1st orderInterpersonal prediction error
R - mi (R)
Depth‐of‐thought (investor)
level 0 level 2level 1
70
corrected p < 0.01
X = ‐48
X = ‐48
X = ‐48X = ‐48
p < 0.0001p < 0.001uncorrected
2nd orderInterpersonal prediction error
I - mi (mj (I))
Distinct neural signals that emerge after depth-of thought phenotyping
R
I
player i player j
Computational phenotyping with reciprocation games
1. Shows promise neurally and behaviorally
2. Requires lots of normative data
3. Tools and methods are now the pressing need
Collaborators
Virginia Tech - Kevin Hill, Terry Lohrenz, Ken Kishida, Amin Kayali, Ann Harvey, Justin King, Meghana Bhatt, Rosalyn Moran
University College London - Peter Dayan, Karl Friston, Xiaosi Gu, Andreas Hula, Peter Fonagy, Ray Dolan, Tobi Nolte, Sarah Carr, and UCL interns
Baylor College of Medicine - James Lu, Richard Gibbs, Josepheen Cruz
Acknowledgments: The Wellcome Trust, The Kane Family Foundation, NIDA, NIMH, NIA, DARPA, MacArthur Foundation, The Dana Foundation
Baylor College of MedicinePearl Chiu
Xu Ciu (Stanford U)Brooks King-Casas
Josepheen CruzKen Kishida
Misha KoshelevJian Li (PKU)Terry Lohrenz Damon Tomlin
Ting XiangDongni Yang
CaltechCedric Anen
Colin CamererSteve Quartz
Antonio RangelDeb Ray
Emory UniversityGreg Berns
University of HoustonAmin Kayali
UCLPeter DayanPeter Fonagy
Tobi NolteXiaosi Gu
Andreas Hula
