# Presentation: 948482

## Paul Slovic and Ellen Peters
Decision Research
1201 Oak Street
Eugene, Oregon, USA
pslovic@oregon.uoregon.edu
Neuroeconomics and Aging Workshop
March 31-April 1,2006

- Decision Research
- 1201 Oak Street
- Eugene, Oregon, USA
- _[pslovic@oregon.uoregon.](mailto:pslovic@oregon.uoregon.edu)__[edu](mailto:pslovic@oregon.uoregon.edu)_
- Neuroeconomics and Aging Workshop
- March 31-April 1,2006

- Decision Making and Aging

## With thanks to 

- C.K.Mertz
- Judy Hibbard
- Melissa Finucane

## Source:

- Finucane &
- Lees(2005).

## Source:

- Finucane &
- Lees(2005).

## Some Studies of the “Affect Story”

- Evaluating gambles
- Destination preferences (vacations, jobs, retirement)
- Risk perception/communication (dangerousness)
- Marketing/advertising/promotion
- names, labels, images, packaging
- Protective measures/insurance/life saving
- Stigmatization of places, products, technologies
- Investment judgments and decisions
- Punitive damage awards
- Youth smoking
- Apathy toward genocide/compassion collapse

## Comprehension errors increase with age

-    The Comprehension Index : Reflects number of errors made out of 35 decision tasks involving interpretation of tables and graphs
- Two Study Samples:  Older adults aged 65+ (_n_ = 253) Employed Age < 65 (_n_ = 239)

- (Hibbard et al., 2001)

**Notes:**
Kirsch et al. (2002) suggest that about half of Americans lack the minimal math skills needed to use numbers embedded in printed materials like newspapers.

## EXAMPLE OF DECISION TASK:

**Which HMO requires the lowest copayment for a visit with a primary care  doctor?**

## Which HMO requires the lowest copayment? 

- % Errors

- Which HMO requires the lowest copayment?

**25**

**28**

**21**

**38**

## Age Differences in Comprehension(35 item index)

- r = .31, p < .001

**Notes:**
Educational level is a protective factor of this function.  Those with higher education show much smaller age differences.

## Slide 10

## Range of decision making competence  increases with age, making individual differences particularly important in older adults

- Decision Competence

- Age

- least competent

- most competent

## Slide 12

**Notes:**
authors_pan\paul\overhead materials\neuron72.jpg 11-18-3

## Neural Economics        P. Read Montague (Baylor College of Medicine)

-        Survival is about economic evaluation
-        The brain is an economic evaluation engine
-        The core of neural economics is the recognition that rapid, ongoing economic evaluation is a central... function carried out by the nervous systems of mobile creatures.
-        Without some kind of internal currency in the nervous system, a creature would be unable to assess the relative value of events like drinking water, searching for predators, or chasing prey.  The nervous system must estimate the value of each of these actions and convert it to a common scale. Recent work has shown that fluctuations in the delivery of the neurotransmitter dopamine may represent one such common currency.
-        The dopamine system is well designed to handle the kinds of decisions that an early human would have encountered; however it is grossly inadequate in many situations that have arisen in modern society – e.g. drug abuse.

## At the psychological levelthis “common currency”may be affect

## What is Affect?

- • A valenced feeling (e.g., goodness
- or badness) associated with a stimulus
- • A faint whisper of emotion

## Slide 16

**Notes:**
authors_pan\paul\overhead materials\ThatsGood72.jpg 11-18-3, reduced to 7” width
authors_pan\paul\overheads\that’s good.jpg 5-30-3

## Four functions of affect

- 1.   Acts as a _common currency_
- Acts as _information_
- Acts as a _spotlight_
- Acts as a _motivator of action_

- (Peters, in press)

**Notes:**
Affect plays a variety of functions in jdm.  I won’t go into its different functions in depth right now, but would be happy to talk to you about it later.  In general, affect can act as information as decision makers consult their feelings and ask “how do I feel about this?”.  Affect can also focus us like a spotlight on different information depending on the extent of our feelings.  It can motivate us to take action or do extra work in a decision.  And, finally, it can act as a common currency allowing us to compare apples to oranges more effectively than when it is absent. 

## Affect conveys meaning upon information

Without affect, information lacks meaning and will not be used in judgment and decision making

Affect is a key ingredient of rational behavior

Affect sometimes leads to poor decision making

-  Affect

- Information

- Affect conveys meaning upon information
- Without affect, information lacks meaning and will not be used in judgment and decision making
- Affect is a key ingredient of rational behavior
- Affect sometimes leads to poor decision making

- Meaning

## There is no dearth of evidence in everyday life that people apprehend reality in two fundamentally different ways, one variously labeled intuitive, automatic, natural, non-verbal, narrative, and experiential, and the other analytical, deliberative, verbal, and rational.

- Seymour Epstein; 1994, p. 710

## Slide 20

| Two Modes of Thinking:  Comparison of the Experiential and Analytic Systems |  |
| --- | --- |
| Experiential System | Analytic System |
|  Holistic |  Logical: reason oriented (what is sensible) |
|  Affective: pleasure-pain oriented |  Logical connections |
|  Associationistic connections |  Behavior mediated by conscious appraisal of events |
|  Behavior mediated by “vibes” from past experience |  Encodes reality in abstract symbols, words and numbers |
|  Encodes reality in concrete images, metaphors and narratives |  Slower processing: oriented towards delayed action |
|  More rapid processing: oriented towards immediate action |  Requires justification via logic and evidence |
|  Self-evidently valid: “experiencing is believing” |  |
| Note:  Adapted from Epstein, 1994 |  |

## Reliance on Feelings Increases With:

| Valuation 
By
Feelings(system 1) |  | Valuation
By 
Calculation(system 2) |
| --- | --- | --- |
|  |  |  |

_**Reliance on Feelings Increases With:**_
-   Innumeracy
-   Cognitive Load
- complexity of task & information
- amount of information
- memory demands
-   Stress
- time pressure
- pain
- poor health
-   Older age
-   Affect rich outcomes & images
- evaluable information displays

## Affective information may be more important as we age.

- _Socioemotional Selectivity Theory_.  Motivational shift
- Increased attention to emotional content
- Particularly positive information (Carstensen, 1990)
- _Dynamic Integration Theory_.
- 	Cognitive limitations cause shift
- Increase in disruptions from emotionally arousing stimuli
- Particularly to less resource-demanding positive information (Labouvie-Vief, 2003)

**Notes:**
SST is a motivational shift in processing rather than a deficiency in deliberative processing

## Affective resiliency? – relative affective change

**Notes:**
So now we’re talking about direct affect which might be learned through experience such as with Bechara and Damasio’s somatic markers or it might be the impact of mood or anecdotes.  In terms of lifespan changes in the relative weight of affective and deliberative information processes, there are at least two possible lifespan changes that might happen.  One is affective resiliency.  This is consistent with what happens to implicit learning and memory.  It is also consistent with Labouvie-Vief’s lifespan theory that I’ll review in a moment.

## Affective enhancement? – absolute affective change 

**Notes:**
Consistent with Carstensen’s lifespan theory

## Affect  			Response

- Probability
- Frequency
- Relative Frequency
- Risk
- Benefit
- Valuation (buying and selling prices)
- Choice/Preference

## Some Studies of the “Affect Story”

- Evaluating gambles
- Destination preferences (vacations, jobs, retirement)
- Risk perception/communication (dangerousness)
- Marketing/advertising/promotion
- names, labels, images, packaging
- Protective measures/insurance/life saving
- Stigmatization of places, products, technologies
- Investment judgments and decisions
- Punitive damage awards
- Youth smoking
- Apathy toward genocide/compassion collapse

## Evaluability

- Chris Hsee (University of Chicago)
- “To say that an attribute is hard to evaluate . . .  means that people do not know whether a given value on the attribute is good or bad . . .

## Gamble 1.	7/36 chance to win $9
			Otherwise win nothing
Gamble 2. 	7/36 chance to win $9
			29/36 chance to lose 5¢
Attractiveness is determined by probability in Gamble 1. The $9 payoff is not evaluable and carries little weight. 
In gamble 2, $9 “comes alive with feeling” as a very attractive outcome compared to the small loss. It then carries weight in the judgment.

- 			Otherwise win nothing
- Gamble 2. 	7/36 chance to win $9
- 			29/36 chance to lose 5¢
- Attractiveness is determined by probability in Gamble 1. The $9 payoff is not evaluable and carries little weight.
- In gamble 2, $9 “comes alive with feeling” as a very attractive outcome compared to the small loss. It then carries weight in the judgment.

- Affect, Evaluability and Attractiveness of Simple Gambles

- Mean attractiveness
*(0-20 scale)*
**9.4**
**14.3**

## An Affect Account of the 5¢ Loss Effect 

- Probability is evaluable.
- 7/36 is a _poor_ chance.
- Payoff is less evaluable.
- How good or bad is $9?
- Adding the 5¢ loss makes $9 “come alive with feeling” and it then becomes weighted in the judgment.

## Slide 30

## What Have We Learned from the Gambles?

-  the importance of contextual factors in determining affect and preference
-  the fact that meaning, utility, and weighting of even a very familiar and “well-understood” monetary outcome such as $9 is not fixed, but depends greatly on these contextual factors

## Numeracy (from the cancer literature)

- The ability to understand and use basic probability and mathematical concepts (Lipkus et al., 2001)

**Notes:**
Hamm et al. (2003) found that highly numerate men had more accurate judgments about the probabilities of events associated with prostate cancer screening.
Even highly educated people have difficulty with relatively simple numeracy questions.

## Numeracy in UO undergraduate sample

| Table 1. Eleven items in the Numeracy Scale (Lipkus et al., 2001). 
Mean (median) score in UO undergraduate sample = 8.2 (8) out of 11 possible |  |  |  |
| --- | --- | --- | --- |
|  | Items | % correct
UO undergrads | % correct older adults |
| 1. | Imagine that we roll a fair, six-sided die 1,000 times. Out of 1,000 roles, how many times do you think the die would come up even (2, 4, or 6)? | 61% | -- |
| 2. | In the BIG BUCKS LOTTERY, the chances of winning a $10.00 prize are 1%. What is your best guess about how many people would win a $10.00 prize if 1,000 people each buy a single ticket from BIG BUCKS? | 65% | -- |
| 3. | In the ACME PUBLISHING SWEEPSTAKES, the chance of winning a car is 1 in 1,000. What percent of tickets of ACME PUBLISHING SWEEPSTAKES win a car? | 42% | -- |
| 4. | Which of the following numbers represents the biggest risk of getting a disease? 1 in 100, 1 in 1000, 1 in 10 | 93% | 76% |
| 5. | Which of the following represents the biggest risk of getting a disease? 1%, 10%, 5% | 95% | 79% |
| 6. | If Person A’s risk of getting a disease is 1% in ten years, and Person B’s risk is double that of A’s, what is B’s risk? | 78% | 66% |
| 7. | If Person A’s chance of getting a disease is 1 in 100 in ten years, and person B’s risk is double that of A, what is B’s risk? | 70% | 57% |
| 8/9. | If the chance of getting a disease is 10%, how many people would be expected to get the disease out of 100?, out of 1000? | 90%, 86% | 72%, 67% |
| 10. | If the chance of getting a disease is 20 out of 100, this would be the same as having a ____% chance of getting the disease. | 85% | -- |
| 11. | The chance of getting a viral infection is .0005. Out of 10,000 people, about how many of them are expected to get infected? | 51% | -- |

**Notes:**
Many choices are numeric.  Imagine retirement choices.  The three main choices are how much to save, how to allocate savings, and at what age to retire.  Imagine you’re a senior and must make a Medicare prescription card choice.  At one point there were 72 options available, with different prices on different medications, and by the way the prices were allowed to change weekly.  What about choosing a treatment for cancer?  In today’s day of informed choices, you would be presented with the risks and benefits of multiple treatment options and asked to share in the decision making process with your doctor.  What happens to people who are less numerate?  That’s the applied end.  What about theoretically?  How might information processing differ depending upon how numbers are presented and how people draw meaning from those numbers?

## Numeracy Scale (Lipkus, Samsa, & Rimer, 2001) Which of the following numbers represents the biggest risk of getting a disease? 1 in 100, 1 in 1000, 1 in 10

**% correct**

**Notes:**
Use numeracy as an example. We’ve run a series of studies at Decision Research with different groups of people, ranging from college undergraduates, to employed age adults with more than HS degree and those with a high school degree or less education, and finally older adults who are 65 and above.  We asked numeracy questions like the one here and find that people vary quite a bit in their numerical ability.  This is a fairly easy question, but not everyone answers it correctly and fewer people answer it correctly with less education and older age.

## If Person A’s chance of getting a disease is 1 in 100 in ten years, and person B’s risk is double that of A, what is B’s risk?

**% correct**

**Notes:**
Here’s a somewhat harder numeracy question.  Fewer than half of our employed age adults with a high school education or less answered this correctly.  

## Lower numeracy linked with:

- Lower comprehension
- Greater framing effects in decisions
- Greater influence of direct (and irrelevant) sources of affect and emotion in decisions
- Drawing less meaning from numbers

- Peters, Västfjäll, Slovic, Mertz, Mazzocco, & Dickert (in press) _Psychological Science_;
- Peters, Mazzocco, & Lipkus (in preparation);
- Peters, Dieckmann, & Mertz (in preparation)

## The ability to understand numbers will influence:

- The precision of feelings about probabilistic information.
- The extent to which irrelevant affect influences choices.
- The accessibility and influence of nonsalient frames of the same number.
- The extent of affect elicited through a comparison of numbers.

**Notes:**
Precision of feelings.  People who are higher in numeracy will report have a more clear idea of the goodness or badness of probabilities.
Irrelevant affect.  Those lower in numeracy will be influenced more by irrelevant affect and less by numerical information present in the same decision.
Nonsalient frames of the same number.  Those high in numeracy will more quickly be able to access alternative formats of the same number so that a nonsalient format of a number will influence those in high in numeracy but not those low.
A comparison of numbers will produce greater affective feeling in those high in numeracy.

##   Are they the same or different in communicating risk?
	e.g., 1% chance
	vs.
		1 out of 100

- 	e.g., 1% chance
- 	vs.
- 		1 out of 100

- Probability and Relative Frequency

## RISK COMMUNICATION:

- A patient – Mr. James Jones – has been evaluated for discharge from an acute civil mental health facility where he has been treated for the past several weeks.  A psychologist whose professional opinion you respect has done a state-of-the-art assessment of Mr. Jones.  Among the conclusions reached in the psychologist’s assessment is the following:
**EITHER:**
- Patients similar to Mr. Jones are estimated to have a 20% probability of committing an act of violence to others during the first several months after discharge.
**OR:**
- Of every 100 patients similar to Mr. Jones, 20 are estimated to commit an act of violence to others during the first several months after discharge.

## QUESTION:

- If you were working as a supervisor at this mental health facility and received the psychologist’s report, would you recommend that Mr. Jones be discharged from the hospital at the present time?
**RESULTS:**

| Risk Communication | Do Not Discharge (%) |
| --- | --- |
| 
20% probability | 
21.3 |
| 
20 of every 100 patients | 
40.6 |

## Patient Evaluation

**A. 10%**
- Very few people are violent
- 10% = 1/10
- Probably won’t hurt anyone, though
**C. 10 out of 100**
- Visual of 100 people and 10 who commit crimes
- Mr. Jones committing an act of violence
- People being harmed by the 10/100 patients
**B. 1 out of 10**
- He could be the 1 out of 10
- Some guy going crazy and killing people
- The patient attacking someone
- An act of violence
- There has to be at least 1 in 10. Mr. Jones could very well be that 1

## High numerate hypothesized to have both formats accessible

- _Probability condition_
- Of every 100 patients similar to Mr. Jones, 10% are estimated to commit an act of violence to others during the first several months after discharge

- _Frequency condition_
- Of every 100 patients similar to Mr. Jones, 10 are estimated to commit an act of violence to others during the first several months after discharge

**Notes:**
In this experiment, we hypothesized that the high numerate would be more likely to transform the information from one format to another so that the given format would not make as much a difference to their risk perceptions compared to the low numerate who would be greatly influenced by the given format.

## Perceived risk to others

- F(3,42)=4.4, p<.01 (Frame, p<.05; Numeracy, n.s.; Interaction, p<.05)

**Notes:**
And that’s exactly what we found.  Subjects were asked to rate Mr. Jones’ risk of harming someone other than himself on a scale from 1=low risk to 6=high risk
Low numerates perceived Mr. Jones as being significantly more risky when that risk was described in frequency terms (10 out of 100) compared to probability terms (10%).  The format of the information did not make difference to how high numerates perceived the risk.
We also regressed numeracy scores onto a proxy for intelligence (self-reported SAT scores) and found that even after controlling for intelligence in this way, the interaction of numeracy and format remained significant.

Skip below.
Past studies have shown us that risk information shown in a frequency format elicits more affect and images of a dangerous patient and drives greater risk perceptions compared to the same information shown in a probability format.  We suspect that the less numerate are greatly influenced by the affect elicited by the frequentistic format but may not access this affect when given the % format.  High numerate may have access to this affect in both formats as they transform numbers more easily from one format to the other so that their risk perceptions don’t vary by format.

## Study 5: Jellybean task

**Notes:**
We used a paradigm called the ratio bias phenomenon or jelly bean task developed by Epstein and his colleagues.  In this task, subjects choose a bowl from which to pick a bean.  They’re told that if you selected a colored bean, you would WIN $5.  The rational choice is to select from Bowl B which has a greater objective probability of winning.  Many subjects in Epstein’s experiments, however, select Bowl A instead, saying that it “looked more inviting.”  Based on verbal reports, their feelings towards the number of winning beans seemed to dominate their understanding of objective probability.  We hypothesized that the low numerate would be more likely to choose from Bowl A and to have weaker feelings towards the objective probabilities compared to those higher in numeracy.

## Lower numeracy linked to more choices of the suboptimal bowl A

- Chi-square (N=46)=5.2, p<.05

**Notes:**
The less numerate were significantly more likely to choose from the objectively worse, but affectively appealing Bowl A.  This relationship remained significant after parceling out the effect of intelligence on numeracy (intelligence again was measured with self-reported SAT scores).

## There is a relationship between performance on the jellybean task and performance on the violence risk task.

- Jellybean imagers react more strongly to frequency framing of violence risk.
- (older adult sample)

## % Do not discharge (Violence Risk)

**% Do not discharge (Violence Risk)**

**Jellybean Choice**

**73**

**76**

**61**

**87**

## The ability to understand numbers will influence:

- The precision of feelings about probabilistic information.
- The extent to which irrelevant affect influences choices.
- The accessibility and influence of nonsalient frames of the same number.
- The extent of affect elicited through a comparison of numbers.

**Notes:**
A comparison of numbers will produce greater affective feeling in those high in numeracy.

## Younger Adults: Rating the attractiveness of a bet

- Subjects are asked to rate the attractiveness of a bet (scale=0-20).
- _Group 1_: The bet gives you 7/36 chances to win $9
- _Group 2_: The bet gives you 7/36 chance to win $9 and 29/36 chances to lose 5 cents
- Surprising effect - Group 2 rates their bet as more attractive and has stronger positive affect to $9 (Slovic et al., 2002)

**Notes:**
In Study 6 we examine a paradigm in which we expect the high numerate, rather than the low numerate, to be the less “rational.”  
We hypothesized that the $9:5cent comparison would create conditions similar to the 9% and 10% probabilities that could be compared in the previous jellybean study.  Thus, the high numerate should draw more affective meaning from the $9:5cent comparison when it was available so that the high numerate should show the surprising effect.

## Younger adults: High (and not low) numerate drive the effect and find the small loss bet more attractive

**Notes:**
High numerate are less “rational” in the sense that they find the objectively worse bet more attractive.  

## Older adults too!  High numerate are more likely to find the loss bet more attractive(note overall decrease in attractiveness)

- Interaction, p<.05; main effect of condition, p<.01

**Notes:**
Combining across the two datasets (college student and older), main effect of condition, main effect of age, main effect of numeracy, marginal interaction of age by condition (older show smaller effect of loss), significant interaction of condition by numeracy, , significant interaction of age and numeracy (high numerate younger adults find the bet more attractive than high numerate older adults).

## Younger adults: High numerate feel more positive affect about $9 when 5 cent loss is present

- Interaction F(1,40)=4.4, p<.05; main effect of numeracy is also significant

**Notes:**
It seems to be driven by affective feelings generated through a comparison of numbers by the high numerate but not the low numerate.  
In a mediation analysis, affect towards the $9 partially mediated the relation between numeracy and the attractiveness ratings of the bet in the two conditions.

##  Older adults show similar but weaker pattern with numeracy and overall decreased level of affect  

- Main effects and interaction are not significant

**Notes:**
Significant effect of age such that the older adults report less affect to $9 compared to younger adults.  It may be that sources of calculated affect have less influence on older adults compared to younger adults.  Let’s leave this topic though for now because there’s little to nothing in the literature and think about what we know about direct sources of affect and their potential impact on age differences in decision making.  

## How can we improve our judgment and decision skills 

• restructure the task or environment to correct
  known bias

- • restructure the task or environment to correct
-   known bias

## Perception--good as it is--

- Sometimes goes wrong and needs assistance

## Slide 56

## Slide 57

## Slide 58

## Coding affect in information displays may help older adults process information more effectively

## Highlighting meaning in a Complex Data Display

|  | Member satisfaction with doctor communication | Member Satisfaction with ease of getting referrals to a specialist | Premium cost per month | Co-pay per office visit | Co-pay per prescrip-tion |
| --- | --- | --- | --- | --- | --- |
|  |  |  |  |  |  |
| Plan C |  |  | $50 | $10 | $10 |
| Plan D |  |  | $50 | $12 | $10 |
| Plan E |  |  | $50 | $10 | $10 |
| Plan F |  |  | $100 | $7 | $5 |
| Plan G |  |  | $100 | $8 | $5 |
| Plan H |  |  | $100 | $7 | $5 |

- Highlighting meaning in a Complex Data Display

- 83

- 73

- 77

- 68

- 78

- 71

- 77

- 72

- 62

- 63

- 68

- 67

**Notes:**
In this study we presented older adults with a complex data display and asked them to choose one of the 6 health plans below.  For each health plan they were shown information on two quality attributes (shown in the bargraphs) and three cost attributes.

In another condition, we gave subjects the same numerical information, but we made subtle changes to how the info was presented.  Specifically, we added affective categories to give meaning to the data.

## Affective Categories Add Meaning to a Complex Data Display

|  | Member satisfaction with doctor communication | Member Satisfaction with ease of getting referrals to a specialist | Premium cost per month | Co-pay per office visit | Co-pay per prescrip-tion |
| --- | --- | --- | --- | --- | --- |
|  |  |  |  |  |  |
| Plan C |  |  | $50 | $10 | $10 |
| Plan D |  |  | $50 | $12 | $10 |
| Plan E |  |  | $50 | $10 | $10 |
| Plan F |  |  | $100 | $7 | $5 |
| Plan G |  |  | $100 | $8 | $5 |
| Plan H |  |  | $100 | $7 | $5 |

- Poor

- Fair

- Good

- Excellent

- 83

- 73

- 77

- 68

- 78

- 71

- 77

- 72

- 62

- 63

- 68

- 67

**Notes:**
This is the information shown in the affective categories condition. We expected a difference in choices compared to the bar graph only display.  Specifically, we predicted that subjects in the affective categories condition would be more likely to choose Plans D or G that are highlighted by the addition of the categories (they’re in a better affective category when the categories are present).

## Affective Categories Influenced Choices

- Category lines only did not
- (2(2) = 6.2, *p < *.05)

**35%**

**36%**

**53%**

**0%**

**10%**

**20%**

**30%**

**40%**

**50%**

**60%**

**% chose **

**quality-**

**maximizing **

**Plans D or G**

**No affective**

**categories**

**Category-**

**lines-only**

**Affective**

**categories**

- Participants slower in speed of processing were helped more by the affective coding on the categories.

## A Few Concluding Thoughts

- There are many interesting directions for behavioral and neuro research to pursue collaboratively
- Some questions:
- Can we study evaluability with neuro techniques? For example, when $9 “comes alive with feeling”, can we see it happen in the brain?
- What about the brain response to 10% vs. 10 of 100?
- How does brain activity differ in high vs. low numerate people as they make judgments and decisions?
- More generally, can we study analytic vs. affective processes with neuro methods?
- Challenges
- need for repetition in fMRI studies
- complexities of sampling in studies of older adults