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Read Montague Baylor College of Medicine Houston, TX hnl.bcm.tmc

Reward Processing and Social Exchange. Read Montague Baylor College of Medicine Houston, TX www.hnl.bcm.tmc.edu. I love you. Let’s do lunch. A. A. A. B. B. B. Natural visual statistics. Specific algorithms evolved to solve efficiently the array of problems of early vision.

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Read Montague Baylor College of Medicine Houston, TX hnl.bcm.tmc

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  1. Reward Processing and Social Exchange Read Montague Baylor College of Medicine Houston, TX www.hnl.bcm.tmc.edu

  2. I love you Let’s do lunch A A A B B B

  3. Natural visual statistics Specific algorithms evolved to solve efficiently the array of problems of early vision

  4. Recharge or die The real world imposes another difficult requirement on all mobile organisms

  5. Natural reward-harvesting statistics? generic woodland creature

  6. What computations should we expect? Reward harvesting is an economic problem involving both valuation and choice Models of reinforcement learning can provide insight

  7. guidance signals Actual experience Counterfactual experience ‘what could have been’ Goal-directed choice Select goal Sustain goal Pursue goal

  8. naive R time burst After learning R time pause After learning (catch trial) time 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

  9. Can we detect a reward prediction error signal in a human subject?

  10. Passive conditioning tasks Instrumental conditioning tasks Sequential decision tasks Neural correlates of these error signals have been observed in conditioning and decision tasks Social and economic exchange tasks

  11. “I want to solve Fermat’s last theorem” 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)?

  12. What about the something more abstract like the expression and repayment of trust?

  13. Trust Modeling Must involve risk (uncertainty) Harvesting returns from another agent

  14. I love you Let’s do lunch A A A B B B TRUST

  15. I Agent 1 Agent 2 R 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.

  16. A dynamic version of the Trust game (10 rounds) X3 $20 Investor Trustee

  17. Structure of a round

  18. I Agent 1 Agent 2 R What is the behavioral signal that most strongly influences changes in trust (money sent) ? Reciprocity = TIT-FOR-TAT

  19. Reciprocity = TIT-FOR-TAT Benevolent signal + - Malevolent signal money sent to partner Neutral signal

  20. Surprise response Questions about brain response Responses that differentiate benevolent from neutral? Responses that differentiate malevolent from neutral? Responses that differentiate benevolent from malevolent?

  21. increases or decreasesin future trust by trustee Trustee will increase trust on next move * Trustee will decrease trust on next move submit reveal time (sec) Reputation develops 0.2 0.2 0 0 * -0.2 -0.2 time (sec) -8 0 10 Trustee Brain: ‘intention to increase trust’ shifts with reputation building Signal is now anticipating the outcome

  22. naive R time burst After learning R time pause After learning (catch trial) time Temporal shift resembles value transfer in reward learning experiments

  23. Why should intentions to increase repayment burn more energy? 1 0 .9 -.1 .8 -.2 .7 -.3 TRUSTEE increases repayment .6 TRUSTEE decreases repayment -.4 .5 -.5 .4 -.6 -.7 .3 -.8 .2 -.9 .1 r = .00 r = .27 -1 0 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 Change in next investment Change in next investment Trustee decreases  No information Trustee decreases  positive correlation

  24. Trust? • Social trust is about modeling others - always some underlying currency. • Re-deploy reward-harvesting machinery that we share with all vertebrates (abstractions gain reward status) • Use to probe pathologies (addicted state, autism spectrum disorder, borderline…)

  25. 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

  26. Investment phase cue to invest investperiod delayperiod investment revealed to both brains . . . 4 s 8 s 10 s cue to guess guessperiod synchronized on submission time of both partners .4 .3 fraction of highly accurate guesses by trustee .2 .1 3 4 5 6 7 8 9 10 rounds How do we know a reputation is forming? Investor events Trustee events Trustee

  27. i Split the profit i Split ‘loaf’ i What is Fair? Trustee Investor 20 -i i Split the total 10 + i each collective ownership? common goods?

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