Cognition in Context Understanding “Biases” in Reasoning, Learning, and Decision Making

Cognition in Cognition in ContextContextUnderstanding “Biases” in Understanding “Biases” in Reasoning, Learning, and Reasoning, Learning, and Decision MakingDecision Making

Craig R. M. McKenzieCraig R. M. McKenzieRady School of Management and Rady School of Management and Department of PsychologyDepartment of PsychologyUC San DiegoUC San Diego

Brief background…Brief background… Social scientists often compare how people Social scientists often compare how people

behave with how they ought to behavebehave with how they ought to behave When systematic differences (biases) occur, When systematic differences (biases) occur,

heuristics often invoked as explanation heuristics often invoked as explanation Much research has argued that some of Much research has argued that some of

these conclusions misleadingthese conclusions misleading– Rational analyses can be incomplete or incorrectRational analyses can be incomplete or incorrect– People make assumptions about task structurePeople make assumptions about task structure

My theme: Taking into account real-world My theme: Taking into account real-world conditions, combined with normative conditions, combined with normative principles that make sense under these principles that make sense under these conditions, can help explain purported biasesconditions, can help explain purported biases

Types of framing Types of framing effects effects (Levin et al., 1998)(Levin et al., 1998)

Attribute framingAttribute framing– e.g., “25% fat” vs. “75% lean”; Levin & e.g., “25% fat” vs. “75% lean”; Levin &

Gaeth, 1988; Levin, 1987Gaeth, 1988; Levin, 1987 Risky choice framingRisky choice framing

– e.g., Asian Disease problem; Tversky & e.g., Asian Disease problem; Tversky & Kahneman, 1981Kahneman, 1981

Goal framingGoal framing– e.g., breast self-examination; Meyerowitz e.g., breast self-examination; Meyerowitz

& Chaiken, 1987& Chaiken, 1987

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Traditional view of Traditional view of framing effectsframing effects Framing effects violate Framing effects violate

“description invariance”“description invariance” Based largely on (risky choice) Based largely on (risky choice)

framing effects, Tversky and framing effects, Tversky and Kahneman (1986) conclude that Kahneman (1986) conclude that “. . .[N]o theory of choice can be “. . .[N]o theory of choice can be both normatively adequate and both normatively adequate and descriptively accurate”descriptively accurate”

EquivalenceEquivalence But what have people meant by But what have people meant by

“equivalence”?“equivalence”?– Objective equivalenceObjective equivalence– Formal equivalenceFormal equivalence– Logical equivalenceLogical equivalence

Information equivalenceInformation equivalence is what is is what is requiredrequired– To make “irrational” claim, different frames To make “irrational” claim, different frames

must not communicate choice-relevant must not communicate choice-relevant information information (Sher & McKenzie, 2006)(Sher & McKenzie, 2006)

Information leakageInformation leakage(Sher & McKenzie, 2006; McKenzie & Nelson, 2003; (Sher & McKenzie, 2006; McKenzie & Nelson, 2003;

McKenzie, 2004; McKenzie, Liersch, & Finkelstein, 2006)McKenzie, 2004; McKenzie, Liersch, & Finkelstein, 2006) Logical equivalence does not guarantee Logical equivalence does not guarantee

information equivalenceinformation equivalence– E.g., passive and active sentence formsE.g., passive and active sentence forms

A speaker’s A speaker’s choice of framechoice of frame can be informative can be informative– E.g., “1/2 full” vs. “1/2 empty”E.g., “1/2 full” vs. “1/2 empty”

Assume exactly 2 frames, F1 and F2, and Assume exactly 2 frames, F1 and F2, and background condition B: background condition B:

p(“F1”|B) > p(“F1”|~B) p(“F1”|B) > p(“F1”|~B) ↔↔ p(B|“F1”) > p(B|“F1”) > p(B|“F2”)p(B|“F2”)

If knowledge of B relevant to choice, then If knowledge of B relevant to choice, then responding differently to F1 and F2 is rationalresponding differently to F1 and F2 is rational

Frames information equivalent only if no Frames information equivalent only if no choice-relevant inferences can be drawn from choice-relevant inferences can be drawn from speaker’s choice of frame. Else, “information speaker’s choice of frame. Else, “information leakage” is said to occur.leakage” is said to occur.

Why do attribute Why do attribute framing effects occur?framing effects occur?

Traditional explanation: Traditional explanation: Positive frame (e.g., “lean”) Positive frame (e.g., “lean”) evokes positive associations, evokes positive associations, negative frame (“fat”) negative frame (“fat”) evokes negative evokes negative associations, which influence associations, which influence judgmentsjudgments (Levin, 1987; Levin et al., (Levin, 1987; Levin et al., 1998)1998)

Our explanation: Speakers Our explanation: Speakers more likely to use label (e.g., more likely to use label (e.g., “fat”) that has increased “fat”) that has increased relative to reference point, relative to reference point, thereby leaking information thereby leaking information about relative abundanceabout relative abundance

Information leakageInformation leakage(McKenzie & Sher, in preparation)(McKenzie & Sher, in preparation)

Imagine that all ground beef is about 40% fat, or 60% lean. Imagine that all ground beef is about 40% fat, or 60% lean. Recently, you heard that a new ground beef is going to be Recently, you heard that a new ground beef is going to be sold on the market that is 25% fat, or 75% lean. You sold on the market that is 25% fat, or 75% lean. You happen to be talking to a friend about the new beef. happen to be talking to a friend about the new beef. Given that most ground beef is 40% fat, or 60% lean, Given that most ground beef is 40% fat, or 60% lean, what is the most natural way to describe the new ground what is the most natural way to describe the new ground beef to your friend? Place a mark next to one description:beef to your friend? Place a mark next to one description:

_____ The new beef is 25% fat_____ The new beef is 25% fat_____ The new beef is 75% lean_____ The new beef is 75% lean

when other beef 40% fat/60% lean, when other beef 40% fat/60% lean, 53%53% describe describe new beef as “75% lean”new beef as “75% lean”

when other beef 10% fat/90% lean, when other beef 10% fat/90% lean, 23%23% describe describe new beef as “75% lean” new beef as “75% lean”

Speaker’s choice of frame leaks info about relative Speaker’s choice of frame leaks info about relative fat contentfat content

Information absorption Information absorption and source of frame and source of frame (McKenzie & Sher, in preparation)(McKenzie & Sher, in preparation)

ContextConversation Advertisement

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……using medical treatment outcomes (% using medical treatment outcomes (% die vs. % survive) die vs. % survive) (McKenzie & Nelson, 2003)(McKenzie & Nelson, 2003)– illustrate normative issueillustrate normative issue

……looking at spontaneous, real behavior looking at spontaneous, real behavior (Sher & McKenzie, 2006)(Sher & McKenzie, 2006)

……describing outcome of flips of coin and describing outcome of flips of coin and rolls of die rolls of die (Sher & McKenzie, 2006)(Sher & McKenzie, 2006)– Findings not explained in terms of associative Findings not explained in terms of associative

accountaccount ……examining default effects examining default effects (McKenzie, Liersch, (McKenzie, Liersch,

and Finkelstein, 2006)and Finkelstein, 2006)

Similar results…

Framing effects Framing effects conclusionsconclusions Traditional normative view incorrectTraditional normative view incorrect

– Frames must be information equivalent, Frames must be information equivalent, not logically equivalent, for framing effects not logically equivalent, for framing effects to be irrationalto be irrational

Information leakage has psychological, Information leakage has psychological, as well as rational, implicationsas well as rational, implications

Unclear extent to which information Unclear extent to which information leakage can explain all framing effectsleakage can explain all framing effects

Cell ACell A Cell BCell B

Cell CCell C Cell DCell D

Present

AbsentVariable X

Present AbsentVariable Y

Covariation assessment

Robust finding: Cell A has largest Robust finding: Cell A has largest impact and Cell D smallest impact; impact and Cell D smallest impact; Cells B and C fall in betweenCells B and C fall in between

This bias seen as nonnormative This bias seen as nonnormative because 4 cells equally important in because 4 cells equally important in traditional normative modelstraditional normative models P = A/(A+B) – C/(C+D)P = A/(A+B) – C/(C+D) = (AD-BC)/[(A+B)(C+D)(A+C)(B+D)]= (AD-BC)/[(A+B)(C+D)(A+C)(B+D)]1/21/2

Cell A “bias”Cell A “bias”

Who cares?Who cares? Covariation assessment underlies Covariation assessment underlies

such fundamental behaviors as such fundamental behaviors as learning, categorization, and learning, categorization, and judging causationjudging causation

People's ability to accurately assess People's ability to accurately assess covariation allows them to explain covariation allows them to explain the past, control the present, and the past, control the present, and predict the future predict the future (Crocker, 1981)(Crocker, 1981)

Cell A “bias” makes normative (Bayesian) Cell A “bias” makes normative (Bayesian) sense if presence of variables tends to be sense if presence of variables tends to be rarer than their absencerarer than their absence (Anderson, 1990; (Anderson, 1990; McKenzie & Mikkelsen, 2000, 2007)McKenzie & Mikkelsen, 2000, 2007)

Bayesian perspective assumes subjects Bayesian perspective assumes subjects approach covariation task as one of approach covariation task as one of inferenceinference rather than statistical summary rather than statistical summary (see also Griffiths & Tenenbaum, 2005)(see also Griffiths & Tenenbaum, 2005)– Trying to discriminate between 2 hypotheses Trying to discriminate between 2 hypotheses

about population – relationship (H1) vs. no about population – relationship (H1) vs. no relationship (H2)relationship (H2)

– Likelihood ratios, e.g., p(Cell A|H1)/p(Cell A|Likelihood ratios, e.g., p(Cell A|H1)/p(Cell A|H2)H2)

Bayesian accountBayesian account

Absolute log-likelihood ratio of cells as function of p(X) and p(Y).|LLR| = Abs(log[p(j|H1)/p(j|H2)]), j = A, B, C, D; H1: rho=0.1; H2: rho=0

When presence of X and Y is rare, Cell A most informative and Cell D least informative (B & C fall in between)

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……is it reasonable to assume that the is it reasonable to assume that the presence of variables is rare? presence of variables is rare?

Well, most people do not have a fever, Well, most people do not have a fever, most things are not red, most people most things are not red, most people are not accountants, and so onare not accountants, and so on– Of categories “X” and “not-X” (e.g., red Of categories “X” and “not-X” (e.g., red

things and non-red things), which would be things and non-red things), which would be larger?larger?

Cell A “bias” reversed when subjects Cell A “bias” reversed when subjects know that know that absenceabsence of variables rare of variables rare (McKenzie & Mikkelsen, 2007)(McKenzie & Mikkelsen, 2007)

Yeah, but…Yeah, but…

Rarity affects cell impact as predicted by Bayesian Rarity affects cell impact as predicted by Bayesian accountaccount– Cell A vs. D Cell A vs. D andand Cell B vs. C Cell B vs. C

Second robust phenomenon: Subjects’ prior beliefs Second robust phenomenon: Subjects’ prior beliefs about relationship between variables influence about relationship between variables influence judgments – which is hallmark of Bayesian approachjudgments – which is hallmark of Bayesian approach

Normative principles, combined with consideration Normative principles, combined with consideration of environment, provide parsimonious account of the of environment, provide parsimonious account of the two most robust phenomena in covariation literaturetwo most robust phenomena in covariation literature

Different from framing effects, though: Not case that Different from framing effects, though: Not case that traditional normative model wrong, but a traditional normative model wrong, but a different different normative modelnormative model applies applies

Covariation Covariation assessment assessment conclusionsconclusions

Bayesian account of Bayesian account of some classic learning some classic learning phenomenaphenomena

Previous evidence for Bayesian Previous evidence for Bayesian approach comes from summary approach comes from summary descriptions of data and presentation descriptions of data and presentation of single cellsof single cells

What about trial-by-trial updating – What about trial-by-trial updating – traditionally the domain of Rescorla-traditionally the domain of Rescorla-Wagner model?Wagner model?

Will limit ourselves to the 2-variable Will limit ourselves to the 2-variable case: 1 predictor and 1 outcomecase: 1 predictor and 1 outcome

Goal is to show, via computer Goal is to show, via computer simulation, that Bayes can account for simulation, that Bayes can account for previous updating findingsprevious updating findings

The Bayesian ModelThe Bayesian Model(adapted from J. R. Anderson, 1990)(adapted from J. R. Anderson, 1990)

Parameters:Parameters: H1, H2 H1, H2

– H1: rho = 0.5, H2: rho = 0H1: rho = 0.5, H2: rho = 0 p(H1) = 1-p(H2)p(H1) = 1-p(H2) alphaX, betaX alphaX, betaX

– alphaX/(alphaX+betaX) = alphaX/(alphaX+betaX) = p(X)p(X)

– rarity rarity alphaX < betaX alphaX < betaX alphaY, betaY alphaY, betaY

– alphaY/(alphaY+betaY) = alphaY/(alphaY+betaY) = p(Y)p(Y)

– rarity rarity alphaY < betaY alphaY < betaY

AA BB

CC DDPr

Ab

Pr AbY

XalphaXbetaX

alphaY

betaY

Density BiasDensity Bias Initial rise in conditioning or Initial rise in conditioning or

judgments of contingency when judgments of contingency when presented with uncorrelated data presented with uncorrelated data (phi = 0), especially when (phi = 0), especially when outcome is commonoutcome is common

Density BiasDensity BiasH1: rho = 0.5, H2: rho = 0P(H1) = P(H2) = 0.5P(X) = 0.5alphaX = alphaY = 1beta X = betaY = 19Observed data: Phi = 0

Trial

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P(Y) = 0.3P(Y) = 0.5P(Y) = 0.7

Density Bias and Density Bias and RarityRarity

H1: rho = 0.5, H2: rho = 0P(H1) = P(H2) = 0.5P(X) = 0.5, P(Y) = 0.7Observed data: phi = 0

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Alpha = 1, Beta = 19Alpha = Beta = 10

Rescorla-Wagner Rescorla-Wagner ModelModel ΔVΔVXX = αβ(λ- = αβ(λ-ΣΣV)V) “…“…perhaps for an increment in perhaps for an increment in

associative connections to occur, it is associative connections to occur, it is necessary that the US instigate some necessary that the US instigate some mental work on the part of the mental work on the part of the animal. This mental work will occur animal. This mental work will occur only if the US is unpredictable – if it only if the US is unpredictable – if it in some sense ‘surprises’ the animal” in some sense ‘surprises’ the animal” (Kamin, 1969)(Kamin, 1969)

R-W and Density BiasR-W and Density BiasP(X) = 0.5alphaX = 0.9betaX = alphaY = betaY = 0.2Observed data: phi = 0

Trial

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P(Y) = 0.3P(Y) = 0.5P(Y) = 0.7

Density Bias, R-W, and Density Bias, R-W, and alpha/betaalpha/beta

P(X) = 0.5, P(Y) = 0.7betaY = 0.2Observed data: phi = 0

Trial

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alphaX = 0.9alphaX = 0.2alpha~X = 0.9alpha~X, beta~Y = 0.9

Partial Reinforcement Partial Reinforcement EffectEffect Initial learning of weak correlation Initial learning of weak correlation

takes longer to extinguish than takes longer to extinguish than initial learning of strong initial learning of strong correlationcorrelation

Partial Reinforcement Partial Reinforcement EffectEffect

H2: rho = 0P(H1) = 0.9P(X) = P(Y) = 0.5Alpha = Beta = 10Observed data: phi = 0

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H1: rho = 0.4H1: rho = 0.7H1: rho = 1.0

Also…Also… Learned irrelevance/helplessnessLearned irrelevance/helplessness

– Initial learning of independence between Initial learning of independence between variables retards subsequent learning of variables retards subsequent learning of real relationshipreal relationship

Latent inhibitionLatent inhibition– Initial presentations of X (CS) alone retard Initial presentations of X (CS) alone retard

subsequent learning of CS-UCS relationshipsubsequent learning of CS-UCS relationship UCS pre-exposure effectUCS pre-exposure effect

– Initial presentations of Y (UCS) alone retard Initial presentations of Y (UCS) alone retard subsequent learning of CS-UCS relationshipsubsequent learning of CS-UCS relationship

Some advantages of Some advantages of Bayes in this contextBayes in this context

Can explain both trial-by-trial updating Can explain both trial-by-trial updating and responses to summaries of dataand responses to summaries of data

ParsimonyParsimony– Local: Bayes reduces to countingLocal: Bayes reduces to counting– Global: Bayes used to explain behavior Global: Bayes used to explain behavior

ranging from vision to reasoningranging from vision to reasoning Speculation: R-W mimics Bayesian Speculation: R-W mimics Bayesian

responseresponse– Marr’s levels of analysis?Marr’s levels of analysis?

What did he say?What did he say? Some important “biases” can be seen as rational Some important “biases” can be seen as rational

– which provides more satisfying account– which provides more satisfying account Important interplay between normative models Important interplay between normative models

and behaviorand behavior Normative principles – Normative principles – combined with combined with

considerations of the structure of the considerations of the structure of the environmentenvironment – can help explain why people – can help explain why people behave as they dobehave as they do

Many “biases” indicate behavior that is not only Many “biases” indicate behavior that is not only more rational, but also psychologically richer, more rational, but also psychologically richer, than previously thoughtthan previously thought

Thank you!Thank you!Context

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H1: rho = 0.5, H2: rho = 0P(H1) = P(H2) = 0.5P(X) = 0.5, P(Y) = 0.7Observed data: phi = 0

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H1: rho = 0.5, H2: rho = 0P(H1) = P(H2) = 0.5P(X) = 0.5alphaX = alphaY = 1beta X = betaY = 19Observed data: Phi = 0

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P(Y) = 0.3P(Y) = 0.5P(Y) = 0.7

P(X) = 0.5, P(Y) = 0.7betaY = 0.2Observed data: phi = 0

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alphaX = 0.9alphaX = 0.2alpha~X = 0.9alpha~X, beta~Y = 0.9

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Risky Choice: Asian Risky Choice: Asian Disease ProblemDisease Problem(Tversky & Kahneman, 1981)(Tversky & Kahneman, 1981)

Imagine that U.S. is preparing for outbreak of an unusual Imagine that U.S. is preparing for outbreak of an unusual Asian disease, which is expected to kill 600 people. Two Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of proposed. Assume that the exact scientific estimate of the consequences of the programs are as follows:the consequences of the programs are as follows:

If Program A adopted, 200 people will be saved.If Program A adopted, 200 people will be saved. If Program B adopted, 1/3 probability that 600 people If Program B adopted, 1/3 probability that 600 people

will be saved, and 2/3 probability that no people will be will be saved, and 2/3 probability that no people will be saved.saved.

If Program C adopted, 400 people will die.If Program C adopted, 400 people will die. If Program D adopted, 1/3 probability that nobody will If Program D adopted, 1/3 probability that nobody will

die, and 2/3 probability that 600 people will die.die, and 2/3 probability that 600 people will die.

Risky Choice Frame Risky Choice Frame SelectionSelectionSubjects first chose preferred program from completely described Subjects first chose preferred program from completely described

programs.programs.

Imagine that your job is to describe the situation, and the programs Imagine that your job is to describe the situation, and the programs which have been proposed, to a committee who will then decide which have been proposed, to a committee who will then decide which program, A or B, to use. Please complete the sentences which program, A or B, to use. Please complete the sentences below as if you were describing the programs to the committee.below as if you were describing the programs to the committee. be savedbe saved

If Program A is adopted, ________ people willIf Program A is adopted, ________ people will . . (write #) (write #) diedie (circle one)(circle one)

If Program B is adopted, If Program B is adopted, be savedbe saved

there is ________ probability that ________ people will ,there is ________ probability that ________ people will , (write #)(write #) (write #) (write #) die die (circle one)(circle one) be savedbe saved

and ________ probability that _______ people willand ________ probability that _______ people will . . (write #)(write #) (write #) (write #) die die

(circle one)(circle one)

Implicit Implicit Recommendation Recommendation Results Results (unpublished data)(unpublished data)

If prefer sure thing (Program A):If prefer sure thing (Program A):– 81% (83/103) word sure thing in terms of “saved”81% (83/103) word sure thing in terms of “saved”

If prefer gamble (Program B):If prefer gamble (Program B):– 48% (45/93) word sure thing in terms of “saved”48% (45/93) word sure thing in terms of “saved”

Word gamble same regardless of preference Word gamble same regardless of preference (“1/3 prob that 600 saved and 2/3 prob that (“1/3 prob that 600 saved and 2/3 prob that 600 die”)600 die”)

Speakers’ preferences affect phrasing of Speakers’ preferences affect phrasing of risky choice option(s) -- which listeners risky choice option(s) -- which listeners might use to infer speaker’s preferencemight use to infer speaker’s preference

Strength of Preference Strength of Preference and Choice of Frame and Choice of Frame (unpublished data)(unpublished data)

Strength of Preference

Weak Moderate Very Strong% W

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Save

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100Prefer Sure Thing (Prog. A)Prefer Gamble (Prog. B)

Cell A “bias” Cell A “bias” Cell D Cell D “bias”“bias”

Condition 3 (Concrete)Condition 3 (Concrete) Sample 1 Sample 2 (Cell) Sample 1 Sample 2 (Cell)Emotionally disturbed: Yes / Drop out: Yes 6 1 (A)Emotionally disturbed: Yes / Drop out: Yes 6 1 (A)Emotionally disturbed: Yes / Drop out: No 1 1 (B)Emotionally disturbed: Yes / Drop out: No 1 1 (B)Emotionally disturbed: No / Drop out: Yes 1 1 (C)Emotionally disturbed: No / Drop out: Yes 1 1 (C)Emotionally disturbed: No / Drop out: No 1 6 (D)Emotionally disturbed: No / Drop out: No 1 6 (D)

““Which sample stronger evidence of relation?” 73% 27%Which sample stronger evidence of relation?” 73% 27%------------------------------------------------------------------------------------------------------------------------------------------------------------------Condition 4 (Concrete)Condition 4 (Concrete) Sample 1 Sample 2 (Cell) Sample 1 Sample 2 (Cell)Emotionally healthy: No / Graduate: No 6 1 (D)Emotionally healthy: No / Graduate: No 6 1 (D)Emotionally healthy: No / Graduate: Yes 1 1 (C)Emotionally healthy: No / Graduate: Yes 1 1 (C)Emotionally healthy: Yes / Graduate: No 1 1 (B)Emotionally healthy: Yes / Graduate: No 1 1 (B)Emotionally healthy: Yes / Graduate: Yes 1 6 (A)Emotionally healthy: Yes / Graduate: Yes 1 6 (A)

““Which sample stronger evidence of relation?” 67% 33%Which sample stronger evidence of relation?” 67% 33%

Cognition in Context Understanding “Biases” in Reasoning, Learning, and Decision Making

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