# Presentation: 270279

## Reward Processing and Social Exchange 

- Read Montague
- Baylor College of Medicine
- Houston, TX
- _[www.hnl.bcm.tmc.edu](http://www.hnl.bcm.tmc.edu/)__ _

***Reward Processing and Social Exchange***

## Slide 2

## Natural visual statistics

***Natural visual statistics***

- Specific algorithms evolved to solve efficiently the array of problems of early vision

## The real world imposes another

-  difficult requirement on all mobile organisms

## Natural reward-harvesting statistics?

***Natural reward-harvesting statistics?***

- generic
- woodland
- creature

## What computations should we expect?

- can provide insight

- Reward harvesting is an economic problem involving both valuation and choice

## Goal-directed choice

- Select goal

- Pursue goal

## Midbrain dopamine neurons

- Pause, burst, and ‘no change’ responses represent
-  reward prediction errors – ongoing emission of information

- ERROR SIGNAL = current reward  +   next prediction  -  current prediction

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

## Neural correlates of these error signals have been observed in conditioning and decision tasks

- Social and economic exchange tasks

## Midbrain dopamine neurons

- burst and pause responses encode reward prediction errors

- ERROR SIGNAL = current reward  +   next prediction  -  current prediction

- Can these systems be re-deployed for abstractly defined rewards (ideas)?

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

## Trust

- Modeling

- Must involve risk (uncertainty)

- Harvesting returns from another agent

## TRUST

## 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.

## X 3

**X**** ****3**

**Investor**

**Trustee**

**$20**

- A dynamic version of the Trust game (10 rounds)

## Structure of a round

## What is the behavioral signal that most strongly influences changes in trust (money sent) ?

**Reciprocity** = TIT-FOR-TAT

## Reciprocity = TIT-FOR-TAT

- money sent
- to partner

- Neutral
- signal

## Questions about brain response

- Responses that differentiate benevolent from neutral?

- Responses that differentiate malevolent from neutral?

- Responses that differentiate benevolent from malevolent?

## Trustee Brain:

**Trustee Brain:**
** ‘****intention to increase trust’ shifts with reputation building**

- Signal is now
- anticipating the
- outcome

## Temporal shift resembles value transfer in 

- reward learning experiments

## Why should intentions to increase 

- -1

- -.9

- -.8

- -.7

- -.6

- -.5

- -.4

- -.3

- -.2

- -.1

- 0

- -1

- -.8

- -.6

- -.4

- -.2

- 0

- .2

- .4

- .6

- .8

- 1

**r = .00**

- TRUSTEE decreases repayment

- Trustee decreases 
- No information

- 0

- .1

- .2

- .3

- .4

- .5

- .6

- .7

- .8

- .9

- 1

- -1

- -.8

- -.6

- -.4

- -.2

- 0

- .2

- .4

- .6

- .8

- 1

**r = .27**

- Change in next investment

- TRUSTEE increases repayment

- Trustee decreases 
- positive correlation

- Why should intentions to increase
- repayment burn more energy?

- Change in next investment

## Trust?

- 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...)

## Collaborators

- 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

- _[www.hnl.bcm.tmc.edu/trust](http://www.hnl.bcm.tmc.edu/trust)__ _

- 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

***Collaborators***

## How do we know a reputation is forming?

**Investor**
**events**

**Trustee**
**events**

- Trustee

**Notes:**
Two-tailed, nonparametric correlations (Spearman rho) of current change in investment (ΔI(r)) with past change in repayment (ΔR(r-1)) and future change in repayment (ΔR(r)) were computed for rounds 3 through 10 (ΔR(r-1) can not be computed for rounds 1 and 2). Of 384 investments, 173 did not differ from previous investments (ΔI = 0) and were excluded from analysis. Consequently, rho degrees of freedom for each round ranged from 23 to 31. Significant relationships are indicated with * (p < .05) and ** (p < .01).

## What is Fair?

- Split the total
-     *10 + i  each*

- i

- Investor

- Trustee

- 20
- -i

- collective ownership?  common goods?