Computational Psychiatry and the Mentalization of Others

Transcription

1 Computational Psychiatry and the Mentalization of Others P. Read Montague Virginia Tech Carilion Research Institute Department of Physics, Virginia Tech 2 Riverside Circle, Roanoke, VA Wellcome Trust Centre for Neuroimaging University College London, 12 Queen Square, London, UK, WC1N 3BG

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. 2

3 Reciprocation exhibits patterns and requires lots of cognition i i A B A r B A B 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

4 Alan Turing 1 $ R * 0 0 Turing Machine Computation Computational Theory of Mind (CTOM) Patterns of information processing Hardware (physical interactions)

5 Turing proposed a solution to an age-old problem How does one get mind-like stuff from stuff-like stuff? Mind is equivalent to information processing supported by brains.

6 The prevailing model (ambition) of computational neuroscience Growing body of cellular & molecular data Behavior, thoughts, moods, etc. Computational Models and methods complex behaviors, thoughts, moods, etc as computations. neuronal interactions as computations.

7 Computational Psychiatry translating computational neuroscience

8 Complication 1 many levels 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 but there are other big complications

9 Complication 2: Validation in model organisms? e.g. is rodent empathy a model for human empathy? hypothesis: There is a software gap human We can profit from reversing model organism pipeline.

10 What are we doing? Use well-defined economic probes - normative solutions Uncertainty and reward task battery Other minds task battery

11 Ultimatum game - simple probe of mentalizing What is fair? What does blue think is fair? What does blue think I think is fair? 1 st order beliefs 2 nd order beliefs What is fair? What does red think is fair? What does red think I think is fair? Offer split ($60 : $40)? accept or reject A B

12 Why use economic games? 1. possess natural learning signals (deviations) 2. equipped with concept of optimal play 3. new variables and dynamics standard cognitive constructs economic variables and their dynamics

13 Other minds task battery between UK and US Social Exchange UK Ultimatum responder role Trust investor role Bargaining buyer and seller role Social hierarchy Structural scan Resting state scan Borderline PD (BPD) Anti-social PD (ASPD) 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

14 Measuring reciprocity and model-building with a 10-round trust game pay (x3) $20 repay Investor Trustee

15 Structure of a round Kept Gave Kept Gave Totals cue to invest invest period delay period investment revealed to both brains delay period cue to repay repay period delay period repayment revealed to both brains delay period totals revealed to both brains inter-round delay period 4 s 8 s 10 s 8 s 4 s 8 s 10 s 8 s 10 s investment phase repayment phase

16 The game engages prediction systems consistent with mesostriatal dopaminergic responses

17 Trustee intention to increase trust shifts with reputation building reciprocity modulated voxels 0.2 increases or decreases in future trust by trustee * submit reveal Trustee will increase trust on next move Trustee will decrease trust on next move time (sec) Reputation develops across rounds Signal now anticipates outcome * Adapted from King-Casas et al., 2005 Science 308:78-83 time (sec)

18 Why is this temporal shift provocative?

19 Midbrain dopamine neurons Pause, burst, and no change responses represent reward prediction errors burst pause R R time time naive After learning

20 Application to Borderline Personality Disorder Cooperation always breaks

21 Borderline PD subjects induce a break in cooperation Adapted from King-Casas et al., 2008 Science 321:

22 Borderline PD anterior insula response does not differentiate offer levels (input insensitivity) Healthy trustee unfair fair BPD trustee unfair fair Adapted from King-Casas et al., 2008 Science 321:

23 BPD anterior insula response occurs only for input control BPD receive signal send signal

24 In BPD subjects, social exchange game exposes: 1. Behavioral signal sustained cooperation breakdown 2. Neural signal insular response unmodulated by fairness 3. Selectivity of insula signal social sensing deficit? All the effects disappear in proposer role.

25 Application across a range of psychopathology groups

26 Biosensor approach to psychopathology classification proposer I R 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. i i A B A r B A B

27 Biosensor approach to psychopathology classification 20 numbers regress preceding I s and r s onto the next investment, cluster on the regression coefficients Koshelev et al. (2010). PLoS Comput Biol 6(10), e

28 Biosensor approach to psychopathology classification over-represented in cluster under-represented in cluster Koshelev et al. (2010). PLoS Comput Biol 6(10), e

29 Theory-of-mind model based classification of behavior

30 Strategy multi-round trust game $20 10 rounds n = 195 pairs investor trustee Use observed exchanges and computational model to classify investors using depth-of-thought and inequality aversion Level 0 Level 1 Level 2 Examine 1 st and 2 nd order interpersonal prediction errors in brains for each depth-of-thought level investor trustee Ray D, et al. Bayesian model of behaviour in economic games. NIPS (2008) 30

31 How to determine a player s type and depth-of-thought? 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 31

32 Depth-of-thought classification captures distinct behavioral characteristics Percentage 60% 50% 40% 30% 20% 10% 0% Level 0 Level 1 Level 2 Depth-of-thought of investor investor trustee Investment ratio 100% 80% 60% 40% 20% 0% Round number Total earnings (monetary units) ns * Level 0 Level 1 Level 2 Depth-of-thought of investor 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).

33 Distinct neural signals that emerge after depth-of thought phenotyping Depth of thought (investor) level 0 level 1 level 2 1 st order Interpersonal prediction error p < p < R - m i (R) Y = 0 Y = 0 Y = 0 uncorrected player i I R player j 0 Y = 0 7 corrected p < 0.01 Depth of thought (investor) level 0 level 1 level 2 2 nd order Interpersonal prediction error I - m i (m j (I)) X = 48 X = 48 X = 48 p < p < uncorrected corrected p < X = 48 7

34 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

35 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

36 Baylor College of Medicine Pearl Chiu Xu Ciu (Stanford U) Brooks King-Casas Josepheen Cruz Ken Kishida Misha Koshelev Jian Li (PKU) Terry Lohrenz Damon Tomlin Ting Xiang Dongni Yang Caltech Cedric Anen Colin Camerer Steve Quartz Antonio Rangel Deb Ray Emory University Greg Berns University of Houston Amin Kayali UCL Peter Dayan Peter Fonagy Tobi Nolte Xiaosi Gu Andreas Hula