Strategic Preferences for Expected Utility vs. Overall Probability of Winning in Risky

Decision Making: Effects of Modes of Thought and Neural Evidence

Jim Bettman

Collaborators: John Payne, Adriana Samper, Mary Frances Luce, Vinod Venkatraman, Scott Huettel

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Adaptive Decision Making in Risky Choice

• A key idea in the Adaptive Decision Maker paradigm (Payne, Bettman, Johnson 1993) is that people use different strategies depending upon different task properties

• Today I will examine the use of strategies focusing on either maximizing expected utility or on maximizing the probability of winning something

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Choice of Expected Utility vs. Overall Probability of Winning in Risky Decision Making

• Payne (JRU, 2005) argues that decision makers faced with a risky choice often choose between options that maximize expected utility or maximize the probability of winning something

• One task used to examine this involves choosing where to add money to a multiple-outcome gamble to “improve” that gamble (we will also use other variants below)– Each choice has two options involving adding money to

either an extreme (the maximum loss or maximum gain amount) or to an intermediate outcome that would change the overall probability of winning

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4

e.g., add $15 to ($0) or -$85 or +$85?

$85

$40

$0

-$45

-$85

Gamble =

How attractive is the gamble below? -10 to + 10 scale.

.2

.2

.2

.2

.2

Imagine that you could improve this gamble byadding $15 to one of the outcomes. How would you preferto “improve” this risky option?

Base problem

Conflict between EV and Probability of Winning

• Note that such choices put EU and probability of winning something into conflict.

• Consider choosing between adding $15 to $0 vs. adding $15 to -$85. Given standard assumptions about utility functions, adding the $15 to the maximum loss to make that value be -$70 would maximize EU. Adding the $15 to the intermediate $0 outcome, however, increases the overall probability of winning something from .4 to .6.

• Payne (2005) and our studies show that people are sensitive to this overall probability of winning

• Indeed, people choose to add to this intermediate option about 60-70% of the time

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Overview

• I will now examine two aspects of the interaction between type of task and type of strategy for these risky choices.

• First, I consider how such choices are affected by different modes of thought (self-paced conscious thought, fixed time conscious thought, unconscious thought)

• Second, I examine what we can learn by using neural evidence as a “process-tracing” methodology relating to these strategic differences

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Unconscious Thought and Risky Decision Making

• Much consumer behavior occurs under conditions of minimal attention (e.g., Ferraro et al., JCR, 2/09)

• Dijksterhuis (Perspectives in Psychological Science, June 2006) proposes the idea of unconscious thought as a possible umbrella conceptual framework for such phenomena

• What exactly is unconscious thought? Is it “good” at making risky decisions?

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Dijksterhuis’ Perspective

• Dijksterhuis defines unconscious thought as object or task-relevant cognitions or affective thought processes that occur while conscious attention is directed elsewhere

• He argues that such thought is good at making complex decisions and adept at weighting

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Dijksterhuis’ Paradigm (Typical Task)

• Participants see values for 4 cars on 12 attributes– “The Hatsdun has good mileage” (each attribute has only two distinct

levels)– The 48 pieces of information are presented randomly – Each piece is shown for 8 seconds

• Task Instructions: form an impression of each car, you will choose one later– Thus, information acquisition is “separated” from choice and at full

attention (but hard to separate psychologically)

• DV: Decision “Accuracy” Criterion: proportion choosing the “best” option– “best” car: has 9 positive attributes out of 12– two cars have 6 out of 12 positive attributes – one car has 3 out of 12 positive attributes

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Mileage HandlingTrunkSpace

New(Age)

ColorAvailability Service Legroom

Ease ofShiftingGears

Cup holders Sunroof?Environ.Friendliness

SoundSystemCar

Dasuka

Hatsdun

Kaiwa

Nabusi

Attributes

Poor

Poor Poor

Poor

Poor

Poor

Poor

Good

Good

Good

Good

GoodSmall

Small

Large

Large

New

New

Old

Old

Available in very fewcolors

Available in manydifferentcolors

Available in manydifferentcolors

Available in manydifferentcolors

Poor

Poor

LittleExcellent

Excellent

Little

Plenty

Plenty

Easy

Easy

Difficult

Difficult

Yes

Yes

Yes

Yes

Yes

No No

No

Relativelygood

Not very good

Relativelygood

Not very good

“Based on the information, you should try to form an impression of each of these cars.You will be asked to choose one of these cars at a later stage.”

Dijksterhuis’ Paradigm (Independent Variables

• Between Ps: Thought Conditions–immediate choice–conscious thought (think about the cars for four

minutes before choosing)–unconscious thought (do n-back task or anagrams

for four minutes)

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Dijksterhuis’ Results

• Unconscious thought does “better” in decision making tasks (e.g., apartment or car studies; JPSP 2004, Science 2006)

• For example (Study 1 in Science), choice of the best car was – 22% in conscious thought– 60% in unconscious thought (Ps solved anagrams)

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Dijksterhuis’ View: The Nature of Unconscious Thought

• The unconscious has higher capacity, is bottom-up, slowly integrates information, naturally develops relative weights, gives rough estimates, and is more divergent

• Dijksterhuis’ Primary Conclusions– conscious thought will be better for simple decisions – unconscious thought will be better for more complex

decisions: this is a highly controversial and perhaps dangerous assertion

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Our View

• Instantiation of Conscious Thought (Independent Variable)– forcing someone to think about their decision for a relatively

long period of time may reflect a “bad” form of conscious thought due to dilution or other effects

– Implies that Dijksterhuis’ results may reflect the particular conscious thought procedure used

• Benchmark for Accuracy (Experimental Task)– Unconscious thought may not perform well when

performance depends upon attribute magnitudes to a greater extent

– Implies that Dijksterhuis’ results may depend upon “accurate” choice being a function of frequency of positive attribute values (e.g., probability of winning)

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Our Contribution

• We develop a new risky choice task to analyze more processing detail

• We replicate Dijksterhuis’ results and add a new self-paced conscious choice condition

• We show that unconscious thought only performs well when magnitudes do not matter, whereas self-paced conscious thought performs well across different task environments (Psychological Science, November 2008)

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Developing a Risky Choice Task

• We developed a variant related to the task used by Payne that – allowed presentation of information one piece at a time– allowed magnitude information to be included– allowed weights to be held constant or varied in a controlled

fashion– enabled inferences about processing based on choice

patterns

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The New Task

• Task – Select among four options – Options defined by payoffs for outcomes of 12 equiprobable “events” – Implementation: participant receives a real payoff for the outcome

associated with one of twelve numbered balls drawn from a bingo cage

• Option Definitions (neutral labels were applied in the studies)– HiEV –highest expected value – P(win) –highest probability of winning a non-zero amount– Decoy –lower expected value than HiEV, the same number of non-zero

payouts as HiEV, and the same highest payoff value as HiEV– Filler – low in both expected value and probability of winning

• This task allows us to focus on choice of expected value (requires tradeoffs involving magnitudes) and probability of winning

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• Event• 1• 2• 3• 4• 5• 6• 7• 8• 9• 10• 11• 12• EV• P(win)• HiEV• 0• 0• 0• 0• 0• 0• 8• 9• 10• 11• 12• 13• 5.25• .50• P(win)• 0• 0• 0• 2• 4• 5• 6• 7• 8• 9• 10• 11• 5.17• .75• Decoy• 0• 0• 0• 0• 0• 0• 3• 5• 6• 7• 12• 13• 3.83• .50• Filler• 0• 0• 0• 0• 0• 0• 0• 0• 0• 2• 3• 4• .75• .25

• Game B

• Event• 1• 2• 3• 4• 5• 6• 7• 8• 9• 10• 11• 12• EV• P(win)• HiEV• 0• 0• 0• 0• 0• 0• 8• 9• 10• 12• 14• 16• 5.75• .50• P(win)• 0• 0• 0• 2• 3• 4• 5• 6• 7• 8• 9• 10• 4.50• .75• Decoy• 0• 0• 0• 0• 0• 0• 3• 5• 6• 7• 14• 16• 4.25• .50• Filler• 0• 0• 0• 0• 0• 0• 0• 0• 0• 2• 4• 12• 1.50• .25

Examples of New Task

Game AEvent 1 2 3 4 5 6 7 8 9 10 11 12 EV P(win)

HiEV 0 0 0 0 0 0 8 9 10 11 12 13 5.25 .50

P(win) 0 0 0 2 4 5 6 7 8 9 10 11 5.17 .75

Decoy 0 0 0 0 0 0 3 5 6 7 12 13 3.83 .50

Filler 0 0 0 0 0 0 0 0 0 2 3 4 .75 .25

Game BEvent 1 2 3 4 5 6 7 8 9 10 11 12 EV P(win)

HiEV 0 0 0 0 0 0 8 9 10 12 14 16 5.75 .50

P(win) 0 0 0 2 3 4 5 6 7 8 9 10 4.50 .75

Decoy 0 0 0 0 0 0 3 5 6 7 14 16 4.25 .50

Filler 0 0 0 0 0 0 0 0 0 2 4 12 1.50 .25

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Bingo cage used in unconscious thought experiments

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Dependent Variables

• Critical choices are – P(win) – maximize the overall probability of winning

something, identical to Dijksterhuis’ accuracy criterion of number of positive attributes

– HiEV – maximizing expected value

• Our games differ in P(win) vs. HiEV Tradeoff– Negligible in Game A (P(win) = 5.17, HiEV = 5.25)– More significant in Game B (P(win) = 4.50, HiEV = 5.75)– One would expect a shift from P(win) toward HiEV from

Game A to Game B, regardless of risk attitude

• EVgain overall performance index = (EV chosen-EV Decoy)/(EV of HiEV-EV Decoy)

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Thought Conditions

• We use three conditions for both Game A and Game B:– Conscious thought fixed time (CT-FT; 4 minutes) – as in

Dijksterhuis– Unconscious thought (UCT; 4 minutes, hard anagrams) – as

in Dijksterhuis– Conscious thought self-paced (CT-SP; decide when ready)

– our new condition• We briefly compare an immediate choice condition to CT-

SP for Game B

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Hypotheses - I

• For both P(win) and HiEV choices, we expect an interaction of game and condition

• Game A: Designed to focus on P(win) –H: UCT > CT-FT, replicating Dijksterhuis–H: CT-SP = UCT > CT-FT, illustrating that CT-FT is

a deficient instantiation of Conscious Thought–No differences for HiEV

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Hypotheses - II

• Game B: Designed to Focus on HiEV– H: CT-SP > UCT = CT-FT, illustrating that CT-SP is more

sensitive to magnitude than UCT – No difference for P(win)

• EVgain– H: CT-SP will perform well in both Games A and B– H: CT-FT will perform less well in both games– H: UCT will perform well in Game A and less well in Game

B– Overall: Main effects of game (A more lenient), condition

(CT-SP > CT-FT), complex contrast comparing CT-SP and UCT across games

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Study

• Participants – 120 student participants for Game A, 133 student participants for Game B, run via computer using Media Lab

• Procedure – – participants told they would see information about outcomes

of four possible games (labeled with casino names) – one item at a time (6 seconds per item), – Task instructions: form an impression, will choose one later.

Will play chosen game for real money.

• DVs: Proportion choosing the Hi EV and Hi P(win) options across conditions, EVgain

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Results - I

Game A - Condition N HiEV P(win) Decoy Filler EV Gain

Conscious, Fixed-time (CT-FT) 38 42% 21% 32% 5% .62

Unconscious (UCT) 42 36% 45% 19% 0% .79

Conscious, Self-paced (CT-SP) 40 28% 50% 15% 8%  .75

Game B - Condition N HiEV P(win) Decoy Filler EV Gain

Conscious, Fixed-time (CT-FT) 46 30% 37% 28% 4% .36

Unconscious (UCT) 41 27% 37% 34% 2% .33

Conscious, Self-paced (CT-SP) 46 52% 26% 17% 4% .56

Immediate (IMM) 27 33% 26% 41% 0% .38

Results - II

• P(win) and HiEV– Overall analyses of P(win) and HiEV by game and condition

show no main effects and significant game by condition interactions (P(win), p<.03; HiEV, p<.05)

– Game A • Main effect of condition for P(win), p<.03

– UCT>CT-FT (p<.03), replicating Dijksterhuis; – CT-SP>CT-FT (p<.02), illustrating boundary condition– UCT and CT-SP not different (p>.60)

• HiEV: no effect of condition (p>.4)• Median time in CT-SP was 24 seconds• Thus, we both replicate Dijksterhuis and show a boundary condition:

self-paced conscious thought outperforms fixed-time conscious thought and is not different from unconscious thought

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Results - III

• P(win) and HiEV– Game B

• Main effect of condition for HiEV, p<.04– CT-SP>CT-FT (p<.04)

– CT-SP>UCT (p<.02), illustrating boundary condition

– UCT and CT-FT not different (p>.70)

• P(win): no effect of condition (p>.4)• Median time in CT-SP was 18 seconds• Thus, we show a second boundary condition: CT-SP performs

better than both UCT and CT-FT when magnitudes matter more

– Shifts from Game A to Game B• Only CT-SP had significant shifts, with P(win) decreasing from A to B

(p<.02) and HiEV increasing from A to B (p<.03)

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Results IV

• EVgain– Main effects of game (A=.72, B=.42, p<.001), condition (CT-FT=.49,

UCT=.56, CT-SP=.66, p<.05), CT-SP>CT-FT, p<.02– No overall interaction (p>.11), but significant complex contrast for CT-SP

vs. UCT across games, p<.05.– Performance of CT-SP is consistently higher, relative performance of

UCT shifts across games.

• Immediate vs. CT-SP– Immediate has median time of 7 seconds– For immediate, more time taken leads to higher performance, whereas

for CT-SP, more time taken leads to worse performance – CT-SP and immediate seem to be different thought conditions.

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Discussion

• Two main boundary conditions on Dijksterhuis’ conclusions about the efficacy of unconscious thought for complex decisions– Self-paced conscious thought performs as well as unconscious thought in some

environments (Game A) and better in others (Game B) and performs better than fixed-time conscious thought • Fixed-time conscious thought may be a poor way to structure conscious

thought – Unconscious thought may be sensitive to the frequency of positive values but

appears to be relatively insensitive to magnitude information

• In a recent study, we find that people do much better with open boxes (seeing all information simultaneously) and then making a self-paced choice. Seeing all the information cuts down on choice of the decoy (from about 23% to about 5%) and increases EV choice in game B and P(win) choice in game A. Thus, one piece of advice might be to try to avoid sequential information acquisition, perhaps by writing information down if need be.

• These findings imply that it is critical to take both form of processing and task characteristics into account when giving prescriptive advice

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Neural Evidence on Choice of Expected Utility vs. Probability of Winning

• We examine choices involving tradeoffs between expected utility and probability of winning in an fMRI study (Venkatraman, Payne, Bettman, Luce, Huettel, Neuron, May 28, 2009)

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fMRI Study Design

• N = 23, young adults, Maximization trait data collected.• Subjects paid, including an initial monetary endowment to which a

proportion of their obtained outcomes could be added to or subtracted from depending on the outcomes of their choices.

• 120 decision trials broken into 6 blocks of 20, each decision on where to add value.

• Variations in types of problems, e.g., eu equal or unequal. • Subjects had 6 seconds to make each decision, no feedback

provided. • At end of decision trials, 40 of the improved gambles were resolved

to an actual monetary gain or loss, with feedback provided. One of the gambles was selected at random to determine payout.

• Choices – Add to maximum loss (Lmin), intermediate option (Pmax), or maximum gain (Gmax)

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$80 p=0.25

$40 p=0.15

$0 p=0.20

-$25 p=0.15

-$75 p=0.25

$80 p=0.20

$40 p=0.20

$0 +$15 = $15 p=0.20

-$25 p=0.20

-$75 +$15 = -$60 p=0.20

One option was the central referent outcome. Adding

money to it often changed the overall probability of winning

and losing.

The other option was an extreme value outcome; adding money to it altered the worst or

best outcome that could be obtained.

$80 p=0.20

$40

p=0.25

$0 p=0.20

-$25 p=0.10

-$75

p=0.25

+$15=$95

+$15=$15

+$15=-$60

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$80 p=0.25

$40 p=0.15

$0 p=0.20

-$25 p=0.15

-$75 p=0.25

$80 p=0.25

$40 p=0.15

$0 p=0.20

-$25 p=0.15

-$75 p=0.25

$80 p=0.25

$40 p=0.15

$0 +$15 = $15 p=0.20

-$25 p=0.15

-$75 +$15 = -$60 p=0.25

$80 p=0.25

$40 p=0.15

$0 p=0.20

-$25 p=0.15

-$75 p=0.25

$80 p=0.25

$40 p=0.15

$0 +$15 = $15 p=0.20

-$25 p=0.15

-$75 +$15 = -$60 p=0.25

(4-6s)

(6s)

ITI = 4-8s

fMRI Experimental Paradigm

Behavioral Results

• Participants chose the intermediate response 65%-70% of the time

• Choice of the intermediate response decreased if– The probability of the extreme option is higher than the

probability of the intermediate option – Adding the to intermediate option does not change the

overall probability of winning (e.g., adding $10 to an intermediate option of -$15_

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Regions Associated with Choices

• Distinct sets of regions predict different choices

• Expected utility choices are predicted by activations in right anterior insula (Lmin) and vmPFC (Gmax)

– vmPFC associated with gain seeking behavior, insula with loss aversion

• Probability of winning choices (Pmax) are predicted by activations in dlPFC and posterior parietal cortex– Both areas have been associated with focus on probabilities

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Insula

vmPFC

PPC

•Activation levels of different regions of the brain correlated with choices•across individuals and trials during the decision making process.

Regions Associated with Strategy Selection• The dmPFC is associated with strategy selection, not choices

• Differences in dmPFC activation between expected utility choices and probability choices correlate significantly with maximizer-satisficer scale values

• When Ps who normally preferred EU choices selected the probability option or those preferring probability choices selected the EU option, activation increased in dmPFC

• Functional connectivity of dmPFC differed depending upon choice– For probability choices, connectivity with dmPFC increased in dlPFC and

PPC– For EU choices, connectivity with dmPFC increased in anterior insula

• dmPFC supports strategic decision making– Venkatraman, Rosati, Taren, and Huettel (J of Neuroscience, 10/21/09)

suggest anterior dmPFC supports strategic control

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0.16

-0.04

Co

nn

ecti

vity

Str

eng

th(a

.u.)

Insula

dLPFC

OPFValue-FStrategic Variability

0.5

-0.5

Sig

nal

Ch

an

ge

-0.25 0.25

r = 0.76P<0.0001

(dmPFC)

(dLPFC)

(aINS)

Y=22

Strategy Conflict and Selection

Individual Differences in Strategy and Neural Reward Sensitivity

• The greater the individual’s difference in response in the ventral striatum to gains and losses when gambles are played (greater neural reward sensitivity), the more likely s/he is to choose the intermediate option

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40

1.5

-1

-0.25 0.25

ΔS

ign

al (W

in-

Loss)

Overall Prob.-focused

r = 0.62p<0.01

Anticipation Gains Losses

0.4

-0.5

Sig

nal C

han

ge

(%)

People differed in their brain activation (Ventral Striatum) as a function of the anticipated outcomes (gains vs. losses) experienced when the gambles were to be played

Discussion

• Brain regions that predict choices are distinct from those that predict preferred strategies

• dmPFC is activated when people respond contrary to their preferred response tendency. This does not seem to be simple response conflict and suggests that people have multiple strategies that they can use adaptively as the task merits

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Summary

• Choice of option maximizing expected utility vs. probability of winning affected by– Mode of thinking in interaction with task structure

• More EU for self-paced conscious thought when expected utility tradeoffs are greater; unconscious thought tends to favor probability of winning option

• Choice of EU vs. Pmax associated with different brain regions– EU: right anterior insula (Lmin), vmPFC (Gmax)

– Pmax: dlPFC,PPC

• Strategy differences associated with dmPFC

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Future Research - I

• Will choices between EU and P(win) be affected by incidental emotion (e.g., anger (more p(win) vs. sadness (more EU))?

• We can examine how the nature of the processing in the unconscious thought condition might affect strategy selection/efficacy by manipulating the nature of the intervening task for unconscious thought, e.g., – analytic vs. global processing (Lerouge)– affective vs. calculative processes– priming close vs. distant associates (perhaps using a remote associates

task of varying difficulty)– This may affect the sensitivity of unconscious thought to various features

of the options (i.e., may interfere with doing different types of processing)

• Anagrams could be used to prime constructs, goals, or emotions in the unconscious condition by choice of words (e.g., priming affective vs. calculative processes via anagrams)

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Future Research – II

• We can study how various factors might influence the choice of P(win) vs. EU– Vary the difficulty of the “counting” operation

• e.g., give participants an option of having X for sure or playing the gamble. Then P (win) may involve outcomes ≥ X.

• Will this make P(win) more difficult and switch choices toward EU?• Will any such switches be moderated by mode of thought?

– We can have options with both gains and losses. • Will choice of EU vs. P(win) be sensitive to the relative number of gains and

losses? Will this vary by mode of thought?

– We can vary the probabilities associated with outcomes by varying the number of events that lead to each outcome. • Will unconscious thought be sensitive to this type of weighting?

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