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Bias Blind Spot: Structure, Measurement, and Consequences

April 2015
Management Science 61(10):150424060229007

DOI:10.1287/mnsc.2014.2096

Authors:

Irene Scopelliti

City, University of London

Carey K Morewedge

Boston University

Erin N. McCormick

Air Force Research Laboratory WPAFB

Lauren Min

University of Kansas

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People exhibit a bias blind spot: they are less likely to detect bias in themselves than in others. We report the development and validation of an instrument to measure individual differences in the propensity to exhibit the bias blind spot that is unidimensional, internally consistent, has high test-retest reliability, and is discriminated from measures of intelligence, decision-making ability, and personality traits related to self-esteem, self-enhancement, and self-presentation. The scale is predictive of the extent to which people judge their abilities to be better than average for easy tasks and worse than average for difficult tasks, ignore the advice of others, and are responsive to an intervention designed to mitigate a different judgmental bias. These results suggest that the bias blind spot is a distinct metabias resulting from naïve realism rather than other forms of egocentric cognition, and has unique effects on judgment and behavior. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

Fourteen item bias blind spot scale.

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Distribution of Bias Blind Spot Scores in Study 2

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Correlations Between the 14 Selected Scale Items

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Correlations between bias blind spot and intelligence, decision making ability, and personality traits.

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Scale Descriptive Statistics, Reliabilities, and Correlations with Bias Blind Spot (Study 2)

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Bias Blind Spot: Structure, Measurement, and

Consequences

Irene Scopelliti, Carey K. Morewedge, Erin McCormick, H. Lauren Min, Sophie Lebrecht,

Karim S. Kassam

To cite this article:

Irene Scopelliti, Carey K. Morewedge, Erin McCormick, H. Lauren Min, Sophie Lebrecht, Karim S. Kassam (2015) Bias Blind

Spot: Structure, Measurement, and Consequences. Management Science

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MANAGEMENT SCIENCE

Articles in Advance, pp. 1–19

ISSN 0025-1909 (print) ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.2014.2096

Bias Blind Spot: Structure,

Measurement, and Consequences

Irene Scopelliti

Cass Business School, City University London, London EC1Y 8TZ, United Kingdom, irene.scopelliti@city.ac.uk

Carey K. Morewedge

Questrom School of Business, Boston University, Boston, Massachusetts 02215, morewedg@bu.edu

Erin McCormick

Dietrich School of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213,

enmccorm@andrew.cmu.edu

H. Lauren Min

Leeds School of Business, University of Colorado, Boulder, Colorado 80309, lauren.min@colorado.edu

Sophie Lebrecht, Karim S. Kassam

Dietrich School of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

{sophielebrecht@cmu.edu,kskassam@andrew.cmu.edu}

People exhibit a bias blind spot: they are less likely to detect bias in themselves than in others. We report

the development and validation of an instrument to measure individual differences in the propensity to

exhibit the bias blind spot that is unidimensional, internally consistent, has high test-retest reliability, and is

discriminated from measures of intelligence, decision-making ability, and personality traits related to self-esteem,

self-enhancement, and self-presentation. The scale is predictive of the extent to which people judge their abilities

to be better than average for easy tasks and worse than average for difﬁcult tasks, ignore the advice of others,

and are responsive to an intervention designed to mitigate a different judgmental bias. These results suggest

that the bias blind spot is a distinct metabias resulting from naïve realism rather than other forms of egocentric

cognition, and has unique effects on judgment and behavior.

Keywords: bias blind spot; judgment and decision making; metacognition; self-awareness; advice taking;

debiasing

History : Received December 5, 2013; accepted September 13, 2014, by Yuval Rottenstreich, judgment and

decision making. Published online in Articles in Advance.

Introduction

People exhibit numerous systematic biases in judg-

ment (Tversky and Kahneman 1974,Kahneman et al.

1982,Nisbett and Ross 1980), many of which are due

to unconscious processes (Morewedge and Kahneman

2010,Wilson and Brekke 1994). A lack of conscious

access to judgment-forming processes means that

people are often unaware of their own biases (Nisbett

and Wilson 1977) even though they can readily spot

the same biases in the judgments of others. Conse-

quently, most people tend to believe that, on average,

they are less biased in their judgment and behavior

than are their peers. Most people recognize that other

people are likely to be biased when judging an attrac-

tive person, for example, but think that their own

judgment of an attractive person is unaffected by this

type of halo effect. Because the majority of people

cannot be less biased their peers, this phenomenon is

referred to as the bias blind spot (Pronin 2007;Pronin

et al. 2002,2004).

We show that people differ in their propensity

to exhibit the bias blind spot, and develop a reli-

able and valid instrument to measure this individ-

ual difference. Bias blind spot appears to be a unique

construct independent of intelligence and personal-

ity traits related to self-esteem, self-enhancement, and

self-presentation. Bias blind spot appears to be inde-

pendent of general decision-making competence. In

other words, the belief that one is less biased than

one’s peers appears to reﬂect a biased perception of

the self rather than a more general inferior decision-

making ability, or an accurate reﬂection of one’s supe-

rior judgment and decision-making ability. We ﬁnd

that the propensity to believe that one is less biased

than one’s peers has detrimental consequences on

judgments and behaviors that rely on self–other accu-

racy comparisons, including decreasing receptivity to

useful (i.e., debiasing) advice.

Bias Blind Spot

People exhibit a tendency to believe they are less

biased than their peers. College students believe

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

they are less biased than their classmates, airline

passengers believe they are less biased than other pas-

sengers, and Americans believe they are less biased

than their fellow citizens (Pronin et al. 2002). This com-

mon asymmetry in the assessment of bias in the self

and in others has been observed across a variety of

social and cognitive biases. This bias blind spot occurs

whether people explicitly rate the extent of their bias

relative to their peers, or rate the absolute extent of

their bias and separately rate the extent of bias exhib-

ited by their peers (Pronin et al. 2002,2004;Pronin

and Kugler 2007;Epley and Dunning 2000;West et al.

2012,Van Boven et al. 2000;Wilson et al. 1996).

The bias blind spot has been attributed to the inter-

play of two phenomena: the introspection illusion and

naïve realism (Pronin et al. 2004,Pronin and Kugler

2007). The introspection illusion results from differ-

ences in the availability and perceived diagnostic

value of introspection when assessing oneself and

other people. When people evaluate the extent of

bias in their own behavior, they base their evalua-

tions on introspection (e.g., “I ﬁnd Rodger attractive,

but I have no memory of that fact inﬂuencing how

I judge his speaking ability”). Because introspection

is unlikely to reveal biased thought processes, people

conclude that their judgment and behavior are unbi-

ased. Naïve realism, the belief that one’s perception

reﬂects the true state of the world, then generates a

false sense that these charitable self-assessments are

genuine rather than positively biased (Pronin et al.

2004;Ross and Ward 1995,1996).

When people evaluate the extent of bias in others,

their assessments rely on behavior rather than on pri-

vate thoughts, because the private thoughts of others

are not accessible. Consequently, the biased behav-

ior of others is not excused by an ostensibly unbi-

ased thought process (e.g., “Joan might not realize

it, but she probably thinks Rodger speaks eloquently

because she is attracted to him”). Because people use

different types of evidence when assessing bias in the

self and in other people, less bias is perceived in the

self than in others. The variety of social and cogni-

tive biases for which this bias blind spot has been

observed (Pronin et al. 2004,West et al. 2012) sug-

gests that susceptibility to the bias blind spot may be

a higher-order latent factor underlying the belief that

one is less likely to exhibit a variety of speciﬁc biases

than are one’s peers.

Bias Blind Spot and Decision Making

The propensity to exhibit decision-making biases

appears to vary systematically (Levin et al. 2002,

Stanovich 1999,West and Stanovich 1997). Several

studies have shown that performance across decision-

making tasks tends to have high internal consistency

(Blais et al. 2005;Bornstein and Zickafoose 1999;

Klayman et al. 1999;Stankov and Crawford 1996,

1997;Stanovich and West 2000). Bruine de Bruin

et al. (2007) demonstrated that decision-making com-

petence, measured as composite performance on mul-

tiple decision-making tasks, reﬂects the outcome of

reliable individual differences and predicts real-world

decision-making ability.

In addition to clarifying the robustness and unique-

ness of the bias blind spot, a major goal of the present

research was to examine its relationship with gen-

eral decision-making ability. If a single higher-order

construct determines the extent to which people rec-

ognize their own bias, three possible relationships

between the bias blind spot and general decision-

making ability emerge.

First, people who are lowest in decision-making

ability may be least aware of their own bias, and

hence may be most likely to exhibit the bias blind

spot. Indeed, the least skilled are often least able to

assess their level of skill (Kruger and Dunning 1999,

Dunning et al. 2003). If bias blind spot is simply one

component of general decision-making ability, suscep-

tibility to the bias blind spot should be negatively

correlated with decision-making competence.

Second, people may accurately assess their degree

of bias relative to their peers. In other words, peo-

ple who believe they are less biased than their peers

(those high in bias blind spot) may be correct. In this

case, bias blind spot may not really reﬂect suscep-

tibility to a blind spot at all, and there should be

a positive correlation between bias blind spot and

decision-making competence.

Third, susceptibility to the bias blind spot is unre-

lated to general decision-making ability. People who

have not been trained in decision making, and are

therefore unaware of common biases in reasoning

and judgment, show considerable variation in their

decision-making competence (Bruine de Bruin et al.

2007). This suggests that superior decision making

is a function of calibrated intuitions rather than

awareness of appropriate decision-making strategies.

Indeed, research on the limitations of introspection

shows that people have limited insight into the pro-

cesses by which their judgments and decisions are

made (Nisbett and Wilson 1977,Wilson and Dunn

2004). Furthermore, tests comparing bias blind spot

for several speciﬁc biases and the commission of

those speciﬁc biases have revealed little to no rela-

tion between recognition of bias and its commission

(Stanovich and West 2008,West et al. 2012).

Predictive Value of the Bias Blind Spot Scale

We suggest that the bias blind spot is weakly, if at

all, related to general decision-making ability. More

speciﬁcally, that the bias blind spot is a metabias—

a bias in the recognition of other biases—that sys-

tematically biases judgment and behavior in unique

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

and predictable ways. Rather than indicate an overly

positive view of the self, it reﬂects the tendency to

consider different evidence when making self–other

accuracy comparisons. Thus, we predict that it should

(a) serve as an index of bias for self–other com-

parisons that rely on similar cognitive mechanisms,

whether those comparisons are favorable or unfavor-

able to the self. As an index of self–other accuracy

comparisons, we suggest that bias blind spot should

also inﬂuence judgments informed by these (social)

comparisons such as (b) the weight placed on advice

from others and (c) receptivity to debiasing training.

Next, we unpack each of these predictions in turn.

A ﬁrst prediction is that when evaluating their

own abilities, people exhibiting more bias blind spot

should exhibit more naïve realism in their initial

assessments of their self (e.g., “I’m great at using

a computer mouse but terrible at juggling”) and

less often correct those assessment for relevant infor-

mation about others’ abilities (e.g., “000as are most

people”). Rather than simply view the self more pos-

itively, they should be more likely to exhibit biases

reliant on similar cognitive mechanisms. For exam-

ple, they should be more likely to exhibit a better-

than-average effect when evaluating their ability to

perform easy tasks, and a worse-than-average effect

when evaluating their ability to perform difﬁcult

tasks (Kruger 1999).

A second prediction is that commission of bias

blind spot should predict bias in judgments informed

by self–other accuracy comparisons. People who think

they are less vulnerable to bias than their peers, for

example, may attend less to the opinions of others

because they believe that others have a more biased

perception of the world, consistent with the operation

of naïve realism (Ross and Ward 1996,Pronin et al.

2004,Liberman et al. 2012). This should lead them to

place less weight on the advice of others when given

the opportunity to incorporate that advice into their

own judgments.

As another example, people who think they are

less vulnerable to bias than their peers should believe

that their judgment and decision making is in less

need of correction (Wilson and Brekke 1994). Analo-

gous to interventions aiming to curb addiction, where

awareness of the problem is a necessary ﬁrst step

in facilitating corrective action, awareness of bias

may be an important precursor to bias mitigation

(Kruger and Dunning 1999,Wilson and Brekke 1994).

Consequently, greater commission of bias blind spot

should reduce the likelihood of beneﬁtting from bias-

reducing interventions.

Overview of the Studies

We adopted a psychometric approach to the anal-

ysis of the bias blind spot. Our ﬁrst two studies

report the development of our individual-difference

measure to measure the extent to which a person

believes she is less biased than her peers, the evalu-

ation of its reliability, the veriﬁcation of its factorial

structure, and the analysis of its discriminant valid-

ity in relationship with potentially related constructs

such as intelligence, cognitive reﬂection, decision-

making ability, and personality traits related to self-

esteem, self-enhancement, and self-presentation. Our

last three studies report tests of our three judgmental

and behavioral predictions.

Study 1

In Study 1, we generated 27 scale items and then

submitted them to a puriﬁcation process resulting in

a 14-item bias blind spot scale with good reliabil-

ity and stability. We used an item-generation process

aimed at capturing a broad sense of the construct to

develop the scale. In accordance with the underly-

ing theory (Pronin 2008), we reasoned that the bias

blind spot is a metabias that is exhibited across mul-

tiple self-assessments. Therefore, we treated the bias

blind spot as a latent variable that causes an asymme-

try in the assessment of bias in the self and in others

with respect to several biases, both in the social and

cognitive domains. Accordingly, we approached the

conceptualization and the operationalization of sus-

ceptibility to the bias blind spot as a reﬂective mea-

surement model (Bollen and Lennox 1991,Edwards

and Bagozzi 2000). That is, a model in which the

direction of causality is from the construct to the

indicators, and in which changes in the underlying

construct are hypothesized to cause changes in the

indicators. According to the proposed conceptualiza-

tion, changes in individual susceptibility to the bias

blind spot (i.e., a latent variable), will cause changes

in the extent to the bias blind spot is observed for

speciﬁc biases in judgment and behavior.

Method

Participants. Initial Sample0A total of 172 Ama-

zon Mechanical Turk (AMT) workers (86 women;

Mage =3204 years, SD =1005) accessed and completed

a survey on the Internet and received $4 as com-

pensation. In all studies we restricted participation

to residents of the United States. Participation in any

study reported here resulted in ineligibility for par-

ticipation in subsequent studies (i.e., no person parti-

cipated in more than one study). Participants were

79.7% White, 7% Black, 5.2% multiracial, 4.1% Asian,

and 1.2% Native American; 2.9% did not indicate their

ethnicity. AMT workers have been shown to exhibit

susceptibility to biases in judgment and decision mak-

ing similar to traditional college samples with respect

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

to common tasks such as the Asian disease problem,

the conjunction fallacy and outcome bias; to exhibit

similar levels of risk aversion; and to exhibit similar

levels of cooperation in behavioral economics games

such as the prisoner’s dilemma (Berinsky et al. 2012,

Horton et al. 2011,Paolacci et al. 2010).

Follow-Up Sample0A total of 83 participants in the

initial sample completed a follow-up survey consist-

ing of the items in the puriﬁed scale for an additional

$4 in compensation (43 women; Mage =3207 years,

SD =1000) yielding a 52% retention rate. Participants

in this subsample were 75.9% White, 9.6% Black,

7.2% Asian, 4.8% multiracial, and 1.2% Native Amer-

ican; 1.2% did not indicate their ethnicity. There were

no differences between the initial sample and the

follow-up sample in terms of age (F=0005, p > 0080),

gender (2=1000, p > 0060), or ethnicity (2=2041,

p > 0070).

Materials. Item Generation0We began developing

scale items by identifying existing indicators of the

construct in the literature. We drew some questions

from Pronin et al. (2002) verbatim, modiﬁed questions

from this source, and wrote new questions with the

same structure. Each question was structured so that

it described a bias and asked respondents to indi-

cate the incidence of the bias for two different tar-

gets: the self and the average American (for examples,

see Table 1). Each bias was described as a psycho-

logical tendency or effect, avoiding (when possible)

the use of positively connoted words or negatively

connoted words such as bias or error. After read-

ing a description of each bias, participants rated the

extent to which they exhibit that bias and the extent

to which the average American exhibits that bias on

7-point scales with endpoints, not at all (1) and very

much (7).

Item Scoring0Bias blind spot scores were calculated

by subtracting the perceived self-susceptibility to each

bias from the perceived susceptibility of the average

American to that bias, for each bias, and then by aver-

aging those relative differences. For each participant,

the size of the average difference between self and

average American bias ratings was used as a measure

of the magnitude of her bias blind spot.

Procedure. Participants rated their vulnerability

and the vulnerability of the average American to

commit biases on 27 unique items. Item order was

random. After completing all 27 items, participants

reported their age, gender, and ethnicity. To assess

the test-retest reliability of the instrument, all partic-

ipants in the initial sample were invited by means

of the AMT messaging system to complete the puri-

ﬁed version of the scale eight days after the initial

administration.

Results

Puriﬁcation0We ﬁrst reduced the number of items

to improve the psychometric properties of the instru-

ment. We removed items that assessed blind spot by

describing different occurrences of the same underly-

ing judgmental bias. We then computed the correla-

tions between each item and the rest of the scale, and

removed items with item-to-total correlations lower

than 0.40. This puriﬁcation process led to a ﬁnal

instrument containing 14 items (see Table 1for the

full list of items).

Reliability0The 14-item scale shows high reliabil-

ity (=0086), well above the acceptable threshold

of 0.70 (Nunnally 1978). All items appeared to be

worth retention. No question eliminations would yield

a higher value of the Cronbach’s alpha coefﬁcient

(-if-item-deletedi< ). Of the 91 pairwise correla-

tions between the items (M=0030), 85 were positive

and signiﬁcant, 3 were positive and marginally signiﬁ-

cant, and 3 were positive but not signiﬁcant (ps<0042;

see Table 2). Each item correlated well with the scale,

as signaled by an average item-to-total correlation

equal to 0.52. All further analyses, in this and subse-

quent studies, use this 14-item scale. For all 14 biases,

the majority of participants rated the average Amer-

ican as more susceptible to bias than themselves; all

ts≥6026, all ps<00001 (for all means, see Table 3).

Exploratory Factor Analysis0We submitted the

14 bias blind spot items to a principal-components

factor analysis (PCA) followed by a parallel anal-

ysis (Horn 1965). The parallel analysis suggests an

underlying single-factor structure, with only the ﬁrst

eigenvalue observed in the data being higher than

a parallel average random eigenvalue based on the

same sample size and number of variables. In the

single-factor model, all 14 items load onto a single

factor accounting for 35% of the total variance, and

each item has a high correlation with that factor (all

s>0048; see Table 3).

Test-Retest Reliability0An analysis of the second

administration of the scale indicated that the instru-

ment has consistently high reliability (=0088), and

high test-retest reliability (r4815=0080, p < 00001), sig-

naling stability of the bias blind spot scale over time.

Discussion

Study 1 provided evidence for individual differences

in susceptibility to the bias blind spot. High inter-

item correlations and factors loadings suggest that

some people have a higher susceptibility to the bias

blind spot than do others across a variety of judg-

mental biases. The results from this ﬁrst study sug-

gest that our instrument reliably assessed individual

differences in susceptibility to the bias blind spot. In

addition, the instrument captures a single latent vari-

able as proposed by our construct operationalization.

Furthermore, the high test-retest reliability observed

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Table 1 Scale Items and Corresponding Biases

Item Description Bias

1 Some people show a tendency to judge a harmful action as worse than an equally harmful inaction. For example,

this tendency leads to thinking it is worse to falsely testify in court that someone is guilty, than not to testify

that someone is innocent.

Action-inaction bias

2 Psychologists have claimed that some people show a tendency to do or believe a thing only because many other

people believe or do that thing, to feel safer or to avoid conﬂict.

Bandwagon effect

3 Many psychological studies have shown that people react to counterevidence by actually strengthening their

beliefs. For example, when exposed to negative evidence about their favorite political candidate, people tend to

implicitly counterargue against that evidence, therefore strengthening their favorable feelings toward the

candidate.

Conﬁrmation bias

4 Psychologists have claimed that some people show a “disconﬁrmation” tendency in the way they evaluate

research about potentially dangerous habits. That is, they are more critical and skeptical in evaluating evidence

that an activity is dangerous when they engage in that activity than when they do not.

Disconﬁrmation tendency

5 Psychologists have identiﬁed an effect called “diffusion of responsibility,” where people tend not to help in an

emergency situation when other people are present. This happens because as the number of bystanders

increases, a bystander who sees other people standing around is less likely to interpret the incident as a

problem, and also is less likely to feel individually responsible for taking action.

Diffusion of responsibility

6 Research has found that people will make irrational decisions to justify actions they have already taken. For

example, when two people engage in a bidding war for an object, they can end up paying much more than the

object is worth to justify the initial expenses associated with bidding.

Escalation of commitment

7 Psychologists have claimed that some people show a tendency to make “overly dispositional inferences” in the

way they view victims of assault crimes. That is, they are overly inclined to view the victim’s plight as one he or

she brought on by carelessness, foolishness, misbehavior, or naivetë.

Fundamental attribution error

8 Psychologists have claimed that some people show a “halo” effect in the way they form impressions of attractive

people. For instance, when it comes to assessing how nice, interesting, or able someone is, people tend to

judge an attractive person more positively than he or she deserves.

Halo effect

9 Extensive psychological research has shown that people possess an unconscious, automatic tendency to be less

generous to people of a different race than to people of their race. This tendency has been shown to affect the

behavior of everyone from doctors to taxi drivers.

Ingroup favoritism

10 Psychologists have identiﬁed a tendency called the “ostrich effect,” an aversion to learning about potential losses.

For example, people may try to avoid bad news by ignoring it. The name comes from the common (but false)

legend that ostriches bury their heads in the sand to avoid danger.

Ostrich effect

11 Many psychological studies have found that people have the tendency to underestimate the impact or the strength

of another person’s feelings. For example, people who have not been victims of discrimination do not really

understand a victim’s social suffering and the emotional effects of discrimination.

Projection bias

12 Psychologists have claimed that some people show a “self-interest” effect in the way they view political

candidates. That is, people’s assessments of qualiﬁcations, and their judgments about the extent to which

particular candidates would pursue policies good for the American people as a whole, are inﬂuenced by their

feelings about whether the candidates’ policies would serve their own particular interests.

Self-interest bias

13 Psychologists have claimed that some people show a “self-serving” tendency in the way they view their academic

or job performance. That is, they tend to take credit for success but deny responsibility for failure. They see

their successes as the result of personal qualities, like drive or ability, but their failures as the result of external

factors, like unreasonable work requirements or inadequate instructions.

Self-serving bias

14 Psychologists have argued that gender biases lead people to associate men with technology and women with

housework.

Stereotyping

indicates that the individual difference tapped by our

scale is stable over time.

Study 2

In Study 2, we veriﬁed the factorial structure of the

bias blind spot scale that emerged in Study 1 by sub-

mitting the 14 items to a conﬁrmatory factor analysis.

Since the development of a valid and reliable measure-

ment scale introduces the possibility to clarify the rela-

tionship between susceptibility to the bias blind spot

and related constructs that compose its nomological

network (Cronbach and Meehl 1955), we also tested

the discriminant validity of the bias blind spot scale

in relation to a set of established scales and measures

assessing potentially related psychological constructs.

Such comparisons determine whether individual dif-

ferences in bias blind spot reﬂect individual differ-

ences in more basic or established personality traits.

Speciﬁcally, by using ﬁve different samples of re-

spondents, we examined the correlations between the

bias blind spot scale and measures of intelligence,

inclination toward cognitive activities, and decision-

making ability (i.e., SAT scores, Need for cognition,

performance on the Cognitive Reﬂection Test, and

decision-making competence), measures of personal-

ity traits related to self-esteem and self-enhancement

(i.e., self-esteem, superiority, need for uniqueness,

narcissism, and over-claiming tendency), self-presen-

tation (i.e., self-monitoring, self-consciousness, and

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Table 2 Correlations Between the 14 Selected Scale Items

Item 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 Action-inaction bias

2 Bandwagon effect 00287∗∗

3 Conﬁrmation bias 00326∗∗ 00478∗∗

4 Diffusion of responsibility 00440∗∗ 00347∗∗ 00331∗∗

5 Disconﬁrmation tendency 00189∗00236∗∗ 00347∗∗ 00240∗∗

6 Escalation of commitment 00317∗∗ 00413∗∗ 00253∗∗ 00309∗∗ 00267∗∗

7 Fundamental attribution error 00268∗∗ 00222∗∗ 00271∗∗ 00304∗∗ 00061 00216∗∗

8 Halo effect 00356∗∗ 00469∗∗ 00328∗∗ 00273∗∗ 00250∗∗ 00379∗∗ 00331∗∗

9 Ingroup favoritism 00217∗∗ 00321∗∗ 00371∗∗ 00329∗∗ 00245∗∗ 00286∗∗ 00352∗∗ 00398∗∗

10 Ostrich effect 00256∗∗ 00368∗∗ 00313∗∗ 00359∗∗ 00390∗∗ 00329∗∗ 00134 00270∗∗ 00238∗∗

11 Projection bias 00397∗∗ 00244∗∗ 00237∗∗ 00218∗∗ 0013600082 00198∗∗ 00362∗∗ 00281∗∗ 00147

12 Self-interest bias 00336∗∗ 00272∗∗ 00358∗∗ 00323∗∗ 00296∗∗ 00281∗∗ 00249∗∗ 00197∗∗ 00302∗∗ 00261∗∗ 00228∗∗

13 Self-serving bias 00383∗∗ 00403∗∗ 00399∗∗ 00418∗∗ 00357∗∗ 00295∗∗ 00250∗∗ 00447∗∗ 00300∗∗ 00412∗∗ 00172∗00334∗∗

14 Stereotyping 00280∗∗ 00386∗∗ 00233∗∗ 00253∗∗ 00085 00305∗∗ 00340∗∗ 00426∗∗ 00313∗∗ 0015000378∗∗ 00311∗∗ 00256∗∗

Note. Asterisks indicate signiﬁcant correlations: ∗∗p < 0001; ∗p < 0005; p < 0010.

social desirability), and more general personality traits

(i.e., the Big Five Inventory (BFI)).

Method

Participants. Study 2 made use of ﬁve unique sam-

ples that totaled to 661 respondents (344 women;

Mage =3206 years, SD =1102). For each sample, the

content of the questionnaire varied as described next.

All participants were AMT workers residing in the

United States who accessed and completed a survey

on the Internet in exchange for compensation vary-

ing based on the length of the study (Sample 1: $5;

Sample 2: $4; Sample 3: $6; Sample 4: $2; Sample 5:

$4). Participants were 77.0% White, 7.0% Black, 7.0%

Asian, 5.1% multiracial, 1.6% Native American, and

2.4% did not indicate their ethnicity. The ﬁve samples

did not differ in terms of bias blind spot scores F < 1.

Table 3 Bias Blind Spot by Scale Item, Factor Loadings from Exploratory Factor Analysis, and Completely Standardized Parameters from

Conﬁrmatory Factor Analysis

Average American Self Factor Completely

loading standardized

Bias M SD M SD t(171) (EFA) parameter (CFA)

1 Action-inaction bias 5012 1033 3096 1069 9086∗∗∗ 00613 00660

2 Bandwagon effect 5073 1006 3060 1068 15007∗∗∗ 00675 00504

3 Conﬁrmation bias 5055 1027 3098 1060 11090∗∗∗ 00642 00552

4 Diffusion of responsibility 5051 1027 4011 1077 10005∗∗∗ 00613 00564

5 Disconﬁrmation tendency 5031 1017 4048 1052 7031∗∗∗ 00482 00552

6 Escalation of commitment 5036 1013 3057 1067 14028∗∗∗ 00575 00583

7 Fundamental attribution error 4063 1024 3016 1054 12016∗∗∗ 00496 00476

8 Halo effect 5089 1002 4016 1069 12013∗∗∗ 00674 00511

9 Ingroup favoritism 5001 1029 3045 1069 11097∗∗∗ 00598 00599

10 Ostrich effect 5020 1015 4035 1074 6026∗∗∗ 00558 00520

11 Projection bias 5032 1033 4019 1065 7083∗∗∗ 00477 00476

12 Self-interest bias 5075 1024 4075 1062 8010∗∗∗ 00568 00355

13 Self-serving bias 5069 1008 4014 1059 13028∗∗∗ 00681 00677

14 Stereotyping 5046 1015 3062 1081 13032∗∗∗ 00579 00490

Notes. EFA, exploratory factor analysis; CFA, conﬁrmatory factor analysis. Asterisks indicate signiﬁcant results: ∗∗∗p < 00001.

Materials and Procedure. Sample 1. Participants

(n=260) ﬁrst completed all 14 items of the bias

blind spot scale in a random order. Participants then

completed a series of personality scales measuring

extant psychological constructs potentially related to

the bias blind spot: the 10-item Rosenberg self-esteem

scale (Rosenberg 1965), the need for uniqueness

scale (32 items; Snyder and Fromkin 1977), the self-

consciousness scale (22 items; Feingstein et al. 1975),

the need for cognition scale (NFC; 18 items; Cacioppo

et al. 1984), the self-monitoring scale (25 items; Snyder

1974), the superiority scale (10 items; Robbins and

Patton 1985), the NPI-16 short measure of narcissism

(16 items; Ames et al. 2006), and the social desir-

ability scale (33 items; Crowne and Marlowe 1960).

Each scale was administered using its original answer

format and coding scheme. Both the order of the

scales and the order of the items were randomized,

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

with scale items nested within scale. Participants also

reported their scores on the math and verbal sections

of the SAT (if they had taken the SAT). To facilitate

the accuracy of participants who might have taken

the test several years before, participants reported

approximate SAT scores on a 12-point scale increasing

in 50-point increments from 200 to 800. Last, partici-

pants reported their age, gender, ethnicity, and high-

est level of education completed.

Sample 2. Participants (n=101) ﬁrst completed all

14 items of the bias blind spot scale in a random order.

They then completed the original three-item Cogni-

tive Reﬂection Test (CRT; Frederick 2005) in open-

ended format. Given the diffusion of the three original

CRT on AMT and the ease of retrieving the solution to

those three questions, participants were also admin-

istered nine additional and less common CRT ques-

tions (Frederick, personal communication) resulting

in a total of 12 CRT items. Next, participants reported

their scores on the math and verbal sections of the

SAT if they had taken the test (as did Sample 1).

Finally, participants reported their age, gender, eth-

nicity, and highest level of education completed.

Sample 3. Participants (n=100) ﬁrst completed

all 14 items of the bias blind spot scale in a ran-

dom order. Then they completed the Adult Decision-

Making Competence inventory (A-DMC; Bruine de

Bruin et al. 2007), a comprehensive instrument assess-

ing general judgment and decision-making ability.

Participants completed the six behavioral decision-

making batteries of measures composing the A-DMC:

resistance to framing (14 paired items), which measures

whether value assessment is affected by irrelevant

variations in problem descriptions; recognizing social

norms, which measures how well people assess peer

social norms (16 items); under/overconﬁdence, which

measures how well participants recognize the extent

of their own knowledge (34 items); applying decision

rules, which asks participants to indicate, for hypo-

thetical individual consumers using different decision

rules, which products they would buy out of a choice

set (10 items); consistency in risk perception, which mea-

sures the ability to follow probability rules (16 paired

items); and resistance to sunk costs, which measures the

ability to ignore prior investments when making deci-

sions (10 items). These tasks measure different aspects

of the decision-making process and decision-making

skills, such as the ability to assess value and the abil-

ity to integrate information. Both the order of the bat-

teries and the order of the items were randomized,

with scale items nested within battery. Participants

reported their scores on the math and verbal sections

of the SAT if they had taken the test (as did Sam-

ple 1). Finally, participants reported their age, gender,

ethnicity, and highest level of education completed.

Sample 4. Participants (n=102) ﬁrst completed all

14 items of the bias blind spot scale in a random

order. They then completed 11 Over-Claiming Ques-

tionnaire (OCQ) batteries. In these batteries, respon-

dents indicate their familiarity with a list of items,

some of which exist (e.g., plate tectonics) and some

of which do not actually exist (e.g., plates of paral-

lax). These familiarity ratings serve as the basis for

a claiming response bias index (OCQ bias) measur-

ing self-enhancing tendencies: a statistical estimate of

how strong familiarity with an item has to be for a

respondent to express familiarity with it (Macmillan

and Creelman 1991). The 11 batteries covered topics

including rap artists, rock artists, country artists, hor-

ror movies, comedies, dramas, foreign movies, soap

operas, football, world leaders, and fashion designers

(Paulhus et al. 2003). Each battery included seven real

items and three bogus items. Participants were asked

to rate their familiarity with each item on a 7-point

scale with endpoints, not at all familiar (0) and very

familiar (6). Both the order of the batteries and the

order of the items were randomized, with scale items

nested within battery. Finally, participants reported

their age, gender, ethnicity, and highest level of edu-

cation completed.

Sample 5. Participants (n=98) ﬁrst completed all

14 items of the bias blind spot scale in a random order.

Next, participants completed a short-form (44-item)

version of McCrae and Costa’s (1987) BFI (John and

Srivastava 1999). Participants reported their scores on

the math and verbal sections of the SAT if they had

taken the test (as did Sample 1). Finally, participants

reported their age, gender, ethnicity, and highest level

of education completed.

Results

Conﬁrmatory Factor Analysis0To test the factorial

structure that emerged in the exploratory factor anal-

ysis in Study 1, we conducted a conﬁrmatory fac-

tor analysis on the data collected from Sample 1. We

ﬁrst evaluated the assumption of multivariate nor-

mality of the data that is a necessary condition for the

use of maximum likelihood estimation. Toward this

end, we computed Mardia’s (1980) test of multivari-

ate skewness and kurtosis. The test was signiﬁcant

(2=299051, p < 00001), signaling that the assumption

of multivariate normality of the data was violated. As

a consequence we opted for a robust maximum like-

lihood estimation.

The conﬁrmatory factor analysis suggested that the

single-factor model emerged in the exploratory fac-

tor analysis from Study 1 ﬁt the data in Study 2

well, with a goodness-of-ﬁt index of 0.93, a Bentler’s

comparative ﬁt index of 0.98, a non-normed ﬁt index

of 0.97, and a root mean square error of approxima-

tion (RMSEA) of 0.05, suggesting a good ﬁt between

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Figure 1 Distribution of Bias Blind Spot Scores in Study 2

[–6.5, – 7.0]

[–6.0, – 6.5]

[–5.5, – 6.0]

[–5.5, – 5.0)

[–5.0, – 4.5)

[–4.5, – 4.0)

[–4.0, – 3.5)

[–3.5, – 3.0)

[–3.0, – 2.5)

[–2.5, – 2.0)

[–2.0, – 1.5)

[–1.5, – 1.0)

[–1.0, –0.5)

[–0.5, 0)

[0, 0.5)

[0.5, 1.0)

[1.0, 1.5)

[1.5, 2.0)

[2.0, 2.5)

[2.5, 3.0)

[3.0, 3.5)

[3.5, 4.0)

[4.0, 4.5)

[4.5, 5.0)

[5.0, 5.5)

[5.5, 6.0)

[6.0, 6.5]

[6.5, 7.0]

Bias blind spot score

160

140

120

100

Frequency (n)

Notes. Scale ranges from 7 to −7. Scores greater than zero indicate bias blind spot. Median column is indicated by gray bar.

the model and the observed data. The factor-loading

estimates are reported in Table 3and were all signiﬁ-

cant; all ts>549, all ps<0001.

Descriptive StatisticsTo examine the distribution of

susceptibility to the bias blind spot, we computed bias

blind spot scores on the ﬁve samples following the

same procedure as in Study 1 (=084). Across the

ﬁve samples, the distribution of the scores appeared

to be positively skewed with an observed range from

−150 to 5.14 (see Figure 1). On average, participants

exhibited a signiﬁcant bias blind spot (M=148, SD =

095; signiﬁcantly different from 0; t660=4029, p<

0001). Furthermore, for each of the 14 biases that

composed the bias blind spot scale, participants rated

the average American as more susceptible to that bias

than themselves; all Ms > 090, all ts(659) >1685,

all ps<0001. At the individual level, a large major-

ity (85.2%) of participants exhibited a signiﬁcant bias

blind spot across the 14 items, with the averages of

their scores being marginally or signiﬁcantly greater

than 0; all ts(13) >179, all ps<010. Only one par-

ticipant (0.1%) had an average bias blind spot score

that was signiﬁcantly lower than 0 (t13=−246,

p=003).

We then examined whether demographic variables

accounted for some differences in bias blind spot

scores. Gender appeared unrelated to bias blind spot,

as the scores of male (M=145, SD =095) and

female participants (M=151, SD =095) were not

signiﬁcantly different; F<1. Similarly, the correlation

between age and bias blind spot scores was not sig-

niﬁcant, r=−004, p=030. Bias blind spot scores

were signiﬁcantly and negatively affected by level of

education, following approximately a linear pattern

(linear contrast: F1653=1142, p=0001).

Discriminant ValidityWe next examined whether

the bias blind spot scale is distinct from related

constructs: (i) measures of intelligence, inclination

toward cognitive activities, and decision-making abil-

ity (i.e., SAT scores, NFC, CRT, A-DMC); (ii) personal-

ity traits related to self-esteem and self-enhancement

tendencies (i.e., self-esteem, superiority, need for

uniqueness, narcissism, over-claiming tendency);

(iii) personality traits related to self-presentation

(i.e., self-monitoring, self-consciousness, and social

desirability); and (iv) and general personality traits

(i.e., BFI).

For each of the samples we computed bias blind

spot scores (Alphas ranging between 0.82 and 0.85).

For each of the constructs that were measured

using multi-item scales, we computed overall aver-

age scores after reverse scoring appropriate items, and

we examined the correlation of these scores with bias

blind spot scores. Table 4presents these scales and

measures, their Cronbach’s alpha coefﬁcients, means,

standard deviations, and their zero-order correlations

with the bias blind spot scale.

Intelligence, Inclination Toward Cognitive Activi-

ties, and Decision-Making AbilityWe ﬁrst examined

whether bias blind spot is merely a manifestation of

intelligence and cognitive ability in four ways. First,

we examined SAT scores, as both the verbal and math

scores load highly on psychometric gor general intel-

ligence (Brodnick and Ree 1995,Frey and Detterman

2004,Unsworth and Engle 2007). Of the 559 partic-

ipants in Samples 1, 2, 3, and 5, 248 reported their

SAT test scores. The correlation between bias blind

spot scores and the verbal SAT scores was not sig-

niﬁcant (r247=010, p=013), whereas the corre-

lation between bias blind spot scores and the math

SAT scores (r247=−013, p=005), was signiﬁcant,

albeit very small.

Second, we examined the correlation between bias

blind spot and NFC, which refers to the extent to

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Table 4 Scale Descriptive Statistics, Reliabilities, and Correlations with Bias Blind Spot (Study 2)

Construct Sample M SD  N r

Intelligence 1–3, 5

SAT math 605004 111055 — 248 −0013∗

SAT verbal 640032 93022 — 248 0010

Need for cognition 1 63065 13021 0093 260 0016∗

Cognitive Reﬂection Test 2

12 items 3067 3000 0082 101 −0023∗

3 original items 1030 1025 0079 101 −0019

9 new items 2038 2002 0071 101 −0022∗

Decision-making competence 3

Resistance to framing 1045 0054 0058 100 0014

Recognizing social norms 0051 0026 0089 100 0007

Under/overconﬁdence 0091 0007 0089 100 0006

Applying decision rules 0077 0016 0050 100 0002

Consistency in risk perception 0075 0015 0071 100 0009

Resistance to sunk costs 4040 0062 0040 100 0008

Big Five personality traits 5

Neuroticism 24030 5065 0087 98 0006

Extraversion 22084 7042 0088 98 0003

Openness 36062 6081 0085 98 0033∗∗

Agreeableness 31076 5030 0083 98 0010

Conscientiousness 33002 6042 0083 98 0007

Self-esteem 1 19096 6026 0092 260 0015∗

Superiority 1 35013 7039 0075 260 −0002

Need for uniqueness 1 101023 17034 0089 260 0016∗∗

Narcissism 1 20040 3065 0082 260 0004

Over-claiming 4 0050 0026 0095 102 −0025∗

Self-consciousness 1

Public 18062 4058 0082 260 −0005

Private 25045 4057 0073 260 0007

Social anxiety 16010 5001 0087 260 −0010

Self-monitoring 1 11018 4043 0074 260 0005

Social desirability 1 3030 6080 0097 260 −0005

p < 0010; ∗p < 0005; ∗∗ p < 0001.

which people “engage in and enjoy effortful cogni-

tive activities” (Cacioppo and Petty 1982, p. 1). People

who are high in NFC expend more effort processing

information, and perform better on arithmetic prob-

lems, anagrams, trivia tests, and college coursework

(Cacioppo et al. 1996). In line with the results of

West et al. (2012), we observed a small but signiﬁ-

cant and positive correlation between bias blind spot

scores and need for cognition (r42585=0016, p=0001).

This signiﬁcant correlation suggests that people with

a high NFC tend to believe that they are less suscep-

tible to biases than their peers.

Third, we examined whether participants with

higher bias blind spot scores make more use of intu-

ition than of deliberate reasoning by examining the

correlation between bias blind spot scores and the

CRT (Frederick 2005), a set of questions that each have

an incorrect intuitive response and a correct response

that can be reached through deliberate reasoning. We

computed a composite measure of performance on

the CRT by assigning a score of one to each cor-

rect answer, a score of zero to each incorrect answer,

and summing all items (M=3067, SD =3000). Bias

blind spot scores were signiﬁcantly and negatively

correlated with CRT scores (r4995= −0022, p=0002).

This pattern of correlations is consistent irrespective

of which CRT items are considered: all 12 items: r=

−0023, p=0002; the three “original” items: r= −0019,

p=0006; or the nine “new” items: r= −0022, p=0003.

Participants who believed themselves to be less vul-

nerable to bias than others were more likely to rely on

their intuition and less likely to engage in cognitive

reﬂection and deliberation (i.e., were less accurate).

Finally, we computed scores for each A-DMC bat-

tery as recommended by Bruine de Bruin et al.1

(2007). Bias blind spot were uncorrelated with perfor-

mance on all of the A-DMC batteries; all rs<0014, all

ps>0018, average r=0008.

Self-Esteem and Self-Enhancement0We next examined

the possibility that the bias blind spot is the manifes-

tation of more basic perceptions of being superior to

others or being different from others (e.g., Taylor and

Brown 1988,Snyder and Fromkin 1980) by examining

1The levels of reliability for each battery reported in Table 4

are generally consistent with those reported by Bruine de Bruin

et al. (2007).

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

the correlations between bias blind spot and superior-

ity, self-esteem, need for uniqueness, narcissism, and

over-claiming. If the domain of these constructs sig-

niﬁcantly overlaps with that of the bias blind spot, we

should observe high correlations between those traits

and bias blind spot scores.

The superiority scale (Robbins and Patton 1985)

measures whether people believe that they are supe-

rior to others. People who believe they are superior

to others may believe that they are less susceptible to

cognitive and social biases than inferior others. How-

ever, perceived self-superiority did not correlate with

bias blind spot scores (r42585= −0002, p=0072).

We then examined the correlation between bias

blind spot and self-esteem (Rosenberg 1965), since

people holding high levels of self-esteem may be

more likely to see themselves as immune from bias

and therefore show higher susceptibility to the bias

blind spot. We found evidence for a signiﬁcant and

positive correlation between bias blind spot scores

and self-esteem (r42585=0015, p=0002), suggesting

that people who believe they are less vulnerable to

bias than their peers tend to have higher levels of

self-esteem. The small size of the correlation, how-

ever, suggests that the two constructs are theoretically

distinct.

We also examined the possibility that the belief that

one is less vulnerable to bias than one’s peers is a

manifestation of a need for uniqueness (Snyder and

Fromkin 1977), the desire to be dissimilar from oth-

ers. The correlation between bias blind spot scores

and need for uniqueness was positive and signiﬁcant

(r42585=0016, p < 0001). This small signiﬁcant correla-

tion suggests that people high in bias blind spot have

a higher desire to distinguish themselves from others,

but that the two constructs are theoretically distinct.

Finally, we examined the relationship between the

bias blind spot and two different operationalizations

of self-enhancement, a narcissism scale (Ames et al.

2006), and over-claiming (Paulhus 1998). Narcissism,

a personality trait that involves a pervasive pattern

of grandiosity, self-focus, and self-importance accom-

panied by preoccupation with success and demands

for admiration (Morf and Rhodewalt 2001), has been

used as a measure of trait self-enhancement (Paulhus

1998). The narcissism scale did not correlate with

bias blind spot scores (r42585=0004, p=0049). Over-

claiming, a measure that can discriminate accuracy

from self-enhancement in response patterns, was

assessed by using the OCQ (Paulhus et al. 2003).

We computed OCQ bias indexes, corresponding to

the mean of the hit rate and the false-alarm rate for

real and false OCQ items (Paulhus et al. 2003), for

each battery of items, and averaged across batter-

ies. Higher values of OCQ bias indicate lower self-

enhancing tendencies, as they indicate that a higher

sense of familiarity needs to be experienced in order

for a respondent to claim familiarity with an item. The

correlation between bias blind spot scores and OCQ

bias was negative and signiﬁcant, r41015= −0025, p=

0001, suggesting that higher bias blind spot scores

are associated with higher self-enhancing tendencies

as measured by over-claiming, but that the two con-

structs are sufﬁciently discriminated.

Self-Presentation0We examined whether the bias

blind spot is the manifestation of other personality

traits related to self-presentation. Self-consciousness

is an acute sense of self-awareness, articulated in

three dimensions: private self-awareness (i.e., a ten-

dency to introspect and examine one’s inner self and

feelings), public self-consciousness (i.e., a tendency

to think about how the self is viewed by others),

and social anxiety (i.e., a sense of apprehensiveness

over being evaluated by others in a social context;

Feingstein et al. 1975). We did not observe a signif-

icant correlation between bias blind spot scores and

private self-awareness (r42585=0007, p=0024), public

self-consciousness (r42585= −0005, p=0046), or social

anxiety (r42585= −0010, p=0011). Self-monitoring mea-

sures the extent to which one consciously employs

impression management strategies in social interac-

tions (Snyder 1974). We did not observe a signiﬁcant

correlation between bias blind spot scores and self-

monitoring (r42585=0005, p=0040). Lastly, we sought

to test whether there was a negative correlation

between bias blind spot and the tendency to answer

in a way that would be viewed favorably by others

(Crowne and Marlowe 1960). The correlation was neg-

ative but not signiﬁcant (r42585= −0005, p=0041).

Big Five Personality Traits0Finally, we examined

the correlations between bias blind spot and the

Big Five personality traits (McCrae and Costa 1987).

Bias blind spot scores appeared to be uncorrelated

with agreeableness (r4965=0010, p=0032), extraver-

sion (r4965=0003, p=0080), neuroticism (r4965=0006,

p=0057), and conscientiousness (r4965=0007, p=0049),

but moderately correlated with openness to experi-

ence (r4965=0033, p < 0001). Participants who reported

signiﬁcantly higher openness to experience had higher

bias blind spot scores, although the moderate size of

the coefﬁcient suggests that the two constructs are suf-

ﬁciently discriminated. This result was not predicted.

Because open-minded thinking has been shown to cor-

relate with superior cognitive ability in a number of

domains (Stanovich and West 2007,West et al. 2008),

it is possible that if people high in openness to expe-

rience have both superior cognitive ability and some

awareness of that ability, they might accurately report

that they are less susceptible to bias than their peers.

Discussion

Bias blind spot appears to be a latent unidimensional

construct that induces people to see themselves as less

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

biased than other people across multiple judgmental

biases (i.e., a meta bias). Bias blind spot scores did

not appear to be correlated with demographic vari-

ables such as gender and age, although bias blind

spot decreased signiﬁcantly as level of education

increased. The results of Study 2 support the dis-

criminant validity of the bias blind spot by showing

that the construct is not a derivative of several poten-

tially related constructs such as measures of intelli-

gence and cognitive ability, decision-making ability,

self-esteem and self-enhancement, self-presentation,

and general personality traits. Many of the correla-

tions between these traits and bias blind spot were

not signiﬁcant. Other correlations were in the low to

moderate range (0.17 to 0.33; Cohen 1988), suggesting

that the bias blind spot scale is assessing a distinct

construct.

Most importantly, the perception that one is less

vulnerable to bias than one’s peers truly appears to

constitute a blind spot in self-awareness. The lack of

correlation with general decision-making competence

suggests that susceptibility to bias blind spot (a) does

not reﬂect generally poorer decision-making ability,

and (b) does not reﬂect an accurate self-assessment

of decision-making ability. The negative correlation

observed between susceptibility to bias blind spot and

CRT scores could be interpreted as supporting a posi-

tive relation to more general cognitive ability, but also

lends support our suggestion that the bias blind spot

is a distinct meta-bias reﬂected in the different con-

sideration of evidence when making self and other

assessments. In other words, people who are more

likely to rely on their intuition may more biased, but

also be less likely to correct their initial introspec-

tive self-assessments by considering additional evi-

dence, such as their behavior. A more direct test of

this assumption was performed in Study 3.

Study 3: Bias Blind Spot and

Self-Assessment

In Study 3, we further elucidated the cognitive mech-

anisms underlying the bias blind spot by examin-

ing the relationship between susceptibility to the bias

blind spot and the asymmetric use of evidence in

another set of self–other comparisons: relative perfor-

mance assessments. When evaluating their own skills

and abilities relative to their peers, people tend to

focus on their own capabilities and neglect the capa-

bilities of others. Because they neglect the fact that

easy activities are easy for most people and difﬁcult

activities are difﬁcult for most people, they rate them-

selves as better than the average person when assess-

ing their ability at easy activities, but as worse than

the average person when assessing their ability at dif-

ﬁcult activities (Kruger 1999).

If the bias blind spot is simply due to overly pos-

itive self-perceptions, then people high in bias blind

spot should be more susceptible to the better-than-

average effect, but not to the worse-than-average

effect. That is, they should rate themselves as bet-

ter than average for both easy and difﬁcult activi-

ties. If susceptibility to the bias blind spot is due to

greater naïve realism and conﬁdence in initial self-

assessments (e.g., “I’m great at using a computer

mouse but terrible at juggling”), coupled with a fail-

ure to correct those assessment to account for other’s

abilities (e.g., “000as are most people”), people high in

susceptibility to the bias blind spot should be more

susceptible to both the better-than-average effect and

the worse-than-average effect.

Method

Pretest. In a pretest, 100 AMT workers (47 women;

Mage =2806 years, SD =1004) were presented with a

list of 34 activities in an online survey and com-

pensated $1 for participating. Included in that list

were the activities used by Kruger (1999, Study 2).

Participants rated the difﬁculty of each activity on

a 9-point scale (1=not difﬁcult at all; 9=very difﬁ-

cult). We selected the two activities rated least difﬁcult

and the two activities rated most difﬁcult: operating a

computer mouse (Mdifﬁculty =1034, SD =0084), copying and

pasting text (Mdifﬁculty =1043, SD =1013), programming

a computer (Mdifﬁculty =7058, SD =2058), and juggling

(Mdifﬁculty =7068, SD =2028). A one-way repeated mea-

sures ANOVA accompanied by planned comparisons

revealed that the two difﬁcult activities were perceived

as signiﬁcantly more difﬁcult than the two easy activ-

ities (F 411985=11105052, p < 00001), that the two easy

activities were perceived as equally easy, and that the

two difﬁcult activities were perceived as equally difﬁ-

cult (F s <1).

Participants. A new sample of 156 AMT workers

(90 women; Mage =3200 years, SD =1200) received

$2.50 for participating in Study 3. Participants were

79.5% White, 8.3% Black, 4.5% Asian, 3.2% multira-

cial, and 0.6% Native American; 3.8% did not indicate

their ethnicity.

Materials and Procedure. Participants completed

the 14-item bias blind spot scale in a random order.

Then they rated their comparative ability on the two

easy (operating a computer mouse and copying and past-

ing text) and on the two difﬁcult activities (program-

ming a computer and juggling) on 100-point scales

(0 =I’m at the very bottom; 100 =I’m at the very top).

Activity rating order was random. Last, participants

reported their age, gender, and ethnicity.

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Results and Discussion

Bias blind spot scores were computed following the

procedure described in Study 1 (=0083). A mixed

model analysis was used to estimate the effect of sus-

ceptibility to the bias blind spot, type of activity (easy

versus difﬁcult), and their interaction on compara-

tive ability ratings. Since each participant assessed her

ability on both the easy and the difﬁcult activities, we

let the intercept vary randomly to take into account

the lack of independence between the observations.

We estimated the following model:

CAij =0+1BBSi+2TAj+3BBSi×TAj+Ui+ij

where CA refers to comparative ability, index irefers

to participants and index jrefers to the type of

activity (easy versus difﬁcult). The dependent vari-

able was thus the average comparative ability rat-

ing expressed by each participant on easy or difﬁcult

activities. The explanatory variables were each partic-

ipant’s bias blind spot score (BBSi5, the type of activ-

ity (TAj, dummy coded, with 0 =easy, 1 =difﬁcult)

and the interaction between these two variables, and

Uiindicated each participant’s random effect. Both

bias blind spot scores and comparative ability ratings

were standardized. Results are based on a total of

624 observations, where each observation is a compar-

ative ability rating on an activity provided by a par-

ticipant. The results revealed a signiﬁcant interaction

between susceptibility to the bias blind spot and type

of activity (easy vs. difﬁcult) on comparative ability

assessments (b=0027, SE =0006, t= −4084, p < 00001).

Table 5reports the results of this estimation and the

comparison of this factorial model with an alternative

model that includes only the main effects.

The interaction between bias blind spot and the

type of activity was further explored by conducting a

simple slope analysis. Bias blind spot was associated

with higher comparative ability ratings on easy activ-

ities (=0013, t=3019, p=00002), and lower compar-

ative ability ratings on difﬁcult activities (= −0014,

Table 5 Regression Results for the Effects of Bias Blind Spot and Type of Activity (Easy vs. Difﬁcult) on (Standardized) Comparative Ability

Ratings (Study 3)

Main effect model Factorial model

Variable baSE t p baSE t p

Intercept 0072 0004 19067 <00001 0072 0004 19053 <00001

Type Activity b−1044 0006 −24021 <00001 −1044 0006 −25035 <00001

BBSc0004 0003 1022 0022 0013 0004 3055 <00001

Type Activity b×BBS c−0027 0006 −4084 <00001

−2 Log-likelihood 1,286.19 1,263.85

Degrees of freedom 6 7

Akaike information criterion 1,298.19 1,277.85

Bayesian information criterion 1,324.81 1,308.90

Likelihood ratio test 22.34 (1), p < 00001

aUnstandardized coefﬁcients.

bDummy coded.

cStandardized.

t= −3051, p < 00001). These results suggest that people

high in susceptibility to the bias blind spot are more

susceptible to both the better-than-average effect and

the worse-than-average effect. These results provide

further evidence that the bias blind spot is a form of

egocentric cognition driven by an asymmetric consid-

eration of evidence when evaluating the self and oth-

ers, rather than by a general self-enhancement motive

that induces a person to see herself as superior to

her peers.

Study 4: Bias Blind Spot and

Advice Taking

In Study 4, we began to examine our last set of

predictions, that bias blind spot affects judgments

informed by self–other accuracy comparisons. Specif-

ically, we examined the effect of the bias blind spot on

the weight given to advice from other people (Yaniv

and Kleinberger 2000). If bias blind spot does inﬂu-

ence bias in judgments reliant on self–other accu-

racy comparisons, people who are more susceptible

to the bias blind spot should view their judgments

to be more accurate than the judgments of other

people, based on the belief that they are based on

more objective perceptions of the world (Pronin et al.

2004). Consequently, people high in bias blind spot

should give less weight to advice received from oth-

ers. They should be less likely to correct their initial

(self-informed) judgments when subsequently given

the opportunity to incorporate others’ judgments into

their own.

Method

Participants. A total of 178 AMT workers (109

women; Mage =3308 years, SD =1202) received $8 for

completing Study 4. Participants were 75.3% White,

6.7% Black, 6.7% multiracial, 6.2% Asian, and

1.7% Native American; 3.4% did not indicate their

ethnicity.

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Materials and Procedure. Participants completed

the 14-item bias blind spot scale in a random order.

Next, they estimated the weight of 30 household

objects (e.g., a trash can, a statue, a doll house), one at

a time. For each object they were shown a color pic-

ture and were provided with the object’s dimensions

(length, height, and width). After making each initial

weight estimate, participants were given advice in the

form of the guess of another “randomly selected par-

ticipant” in the study (i.e., the average estimate of two

pilot participants), and were then given the opportu-

nity to estimate the weight of the object again. Thus,

participants had the option to revise their original

estimate based on the advice that they received from

the other participant. The order in which objects were

judged was random. Finally, participants reported

their age, gender, and ethnicity.

Results and Discussion

We computed bias blind spot scores following the pro-

cedure described in Study 1 (=0082). We computed

the weight on advice (WOA) relative to each object,

by computing the difference between the original esti-

mate and the estimate made after receiving the advice

of the other participant (as in Gino and Moore 2007,

Gino 2008,Harvey and Fischer 1997,Yaniv 2004).

WOA reﬂects how much a participant uses the advice

she receives (Yaniv 2004) and is deﬁned as

WOA =ﬁnal estimate −initial estimate

advice −initial estimate 0

WOA is equal to 0 when participants entirely dis-

count the advice. In such a case, ﬁnal estimates

are equal to initial estimates, meaning that partici-

pants did not revise their estimates after receiving

the advice. WOA is equal to 1 when participants’

ﬁnal estimate is equal to the advice received. In

this case participants give maximum weight to the

advice received. Finally, WOA equals a value between

0 and 1, when participants weigh both their initial

estimate and the received advice positively. Follow-

ing the procedure used in prior research (Gino and

Moore 2007,Gino 2008,Yaniv 2004), cases in which

the advice equaled the initial estimate were excluded

from the analysis, since WOA in those cases equaled a

number divided by 0, making it impossible to quan-

tify how much a participant did or did not use the

advice. For cases in which the ﬁnal estimate did not

fall between the initial estimate and the advice (e.g.,

participants provide revised estimates that are fur-

ther away from the advice than initial estimates),

and WOA was thus greater than 1, values above 1

were adjusted to 1.2One participant whose aggregate

2We estimated the same model on WOA scores that were not sub-

ject to this recommended adjustment, and the effect of susceptibil-

ity to the bias blind spot is even stronger: b1= −0005, SE =0001,

t=3065, p < 00001.

WOA score value was more than ﬁve standard devia-

tions above the mean WOA score was excluded from

all subsequent analyses. No other participants were

excluded. Results are based on a total of 5,049 obser-

vations, each observation being the weight of an

object estimated by a participant.

A mixed model analysis was used to estimate the

effect of susceptibility to the bias blind spot on WOA.

Because each participant evaluated the weight of mul-

tiple objects, we let the intercept vary randomly to

take into account the lack of independence between

the observations. We estimated the following model,

WOAij =0+b1BBSi+Ui+ij1

where index ireferred to participants and index j

refers to objects. The dependent variable was thus

the value for WOA for each participant and for each

object. The explanatory variable was each partici-

pant’s bias blind spot score (BBSi5, and Uiindicated

each participant’s random effect. The results revealed

a signiﬁcant and negative effect of susceptibility to

the bias blind spot on WOA (b1= −0003, SE =0001, t=

−3023, p=00001). On average, participants with bias

blind spot scores one standard deviation below the

mean placed six percent more weight on the advice

of others than participants with bias blind spot scores

one standard deviation above the mean (WOA =00223

vs. WOA =00164).

This analysis was complemented by the estimation

of a logit mixed model that examined the relationship

between bias blind spot scores and the rate at which

participants completely ignored advice (WOA =0).

We estimated the following model:

NO_WOAij =0+b1BBSi+Ui+ij1

where index ireferred to participants and index j

refers to objects. The dependent variable was a

dummy variable that indicates the complete igno-

rance of advice (i.e., D=1 when WOA =0; D=0 oth-

erwise) for each participant and for each object. The

explanatory variable was each participant’s bias blind

spot score (BBSi), and Uiindicated each participant’s

random effect. Consistent with the previous model,

the results revealed a signiﬁcant and positive effect of

susceptibility to the bias blind spot on the likelihood

of completely ignoring advice (b1=00104, SE =00044,

z=2034, p=0002). Susceptibility to the bias blind spot

increased participants’ likelihood of conﬁrming their

initial estimate and completely neglecting advice.

In short, participants who exhibited high levels of

bias blind spot were more likely to ignore the advice

provided, whereas participants who exhibited low

levels of bias blind spot weighed the advice more

heavily. These results are consistent with the results

of Liberman et al. (2012), who showed that naïve

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

realism, by making people believe that their own

perceptions of the world are objective, leads them

to underweight the input of their peers. Our results

extend those by Liberman et al. (2012) by showing

how individual differences in susceptibility to the bias

blind spot signiﬁcantly affect the extent to which peo-

ple incorporate others’ advice in their judgments. Fur-

thermore, our results provide additional evidence in

support of the role of naïve realism as the process

underlying the bias blind spot (Pronin et al. 2004).

Study 5: Bias Blind Spot and Bias

Reduction

In Study 5, we expanded our analysis of bias blind

spot to see whether people characterized by higher

susceptibility to the bias blind spot are more resis-

tant to debiasing procedures. There is little evidence

that people who are more aware of their own biases

are better able to overcome them. West et al. (2012)

examined the effect of the bias blind spot on the inci-

dence of six cognitive biases, and observed no evi-

dence that people characterized by lower bias blind

spot scores were less likely to commit bias. A rela-

tionship between bias blind spot and decision-making

ability might emerge, however, after an intervention

aimed at reducing bias.

We predicted that the perception that one is less

vulnerable to bias than others may constitute a bar-

rier to the activation of corrective strategies aimed at

avoiding bias (Wilson and Brekke 1994). This barrier

may prevent people who believe that others are more

susceptible to bias from actively responding to train-

ing procedures designed to correct biased judgments.

In order to test this prediction, we had participants

read an article designed to increase awareness of the

occurrence of a speciﬁc bias, the fundamental attribu-

tion error (FAE) that also suggested how to mitigate

that bias. We predicted that susceptibility to the bias

blind spot would moderate the effect of this debiasing

training on subsequent commission of the FAE.

Method

Participants. A total of 297 AMT workers (170

women; Mage =3107 years, SD =1007) completed an

online survey and received $2 for their participa-

tion. Participants were 76.4% White, 7.4% multiracial,

6.4% Black, 5.7% Asian, and 1.0% Native American;

3.0% did not indicate their ethnicity.

Procedure. Participants completed the 14-item bias

blind spot scale with items in random order. Next,

participants were randomly assigned to one of two

conditions: debiasing training or control. Participants

assigned to the debiasing training condition read an

explanatory article about the FAE, a tendency to over-

estimate the impact of individual dispositions on

behaviors whereas underestimating the impact of sit-

uational variables (Jones and Harris 1967). The article

described the existence of the FAE, provided exam-

ples of its occurrence, and explained how one might

correct the bias (see the section “debiasing training”

in the appendix). Participants assigned to the con-

trol condition read an article of equal length report-

ing the results of a research study on trust (see the

section “control training” in the appendix). All par-

ticipants then answered nine questions designed to

test the occurrence of the FAE in judgment inspired

by existing paradigms, in a random order (Jones and

Harris 1967,Snyder and Frankel 1976,Scopelliti et al.

2015). Finally, participants reported their age, gender,

and ethnicity.

Results and Discussion

Bias blind spot scores were computed following the

procedure described in Study 1 (=0080). Answers

to the nine FAE questions were highly correlated with

one another (=0080). We summed the number of

questions in which participants exhibited the FAE that

served as our primary dependent variable (M=4017,

SD =2073). The FAE scores were neither normally

distributed, nor could be normalized by log transfor-

mation. Therefore we estimated a Poisson regression,

appropriate to model count dependent variables that

have only non-negative integer values.

We predicted that the debiasing training would

reduce the incidence of bias more for participants

who were less susceptible to the bias blind spot. This

prediction was tested using the procedures outlined

by Aiken and West (1991) to decompose the pre-

dicted interaction using regression analysis. First, bias

blind spot scores were mean-centered by subtract-

ing the mean bias blind spot score from all obser-

vations. Second, we created the interaction term of

(dummy-coded) training factor (debiasing vs. control)

by (mean-centered) bias blind spot score. Next, the

FAE scores were regressed on the training factor (debi-

asing vs. control), bias blind spot scores, and the inter-

action between these two variables.

The analysis revealed a signiﬁcant interaction

between training and susceptibility to the bias blind

spot (2415=4073, p=0003). To shed light on the

nature of this interaction, we conducted a ﬂoodlight

analysis (Spiller et al. 2013) estimating the marginal

effect of training on commission of the FAE at all levels

of susceptibility to the bias blind spot. Table 6reports

the results of this estimation and the comparison of

this factorial model with an alternative model that

includes only the main effects. We used the Johnson-

Neyman technique to identify the range of bias blind

spot scores for which the effect of the debiasing train-

ing was signiﬁcant. This analysis revealed that there

was a signiﬁcant positive effect of debiasing training

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

Table 6 Poisson Regression Results for the Effects of Bias Blind Spot and Type of Training on Fundamental Attribution Error Commission (Study 5)

Main effect model Factorial model

Variable baSE Wald 2p baSE Wald 2p

Intercept 1071 0006 819041 <00001 1081 0007 605062 <00001

Type of Training b−0036 0006 39027 <00001 −0057 0011 26020 <00001

BBSc−0008 0003 5087 0002 −0015 0005 10048 <00001

Type Trainingb×BBS c0015 0007 4073 0003

−2 Log-likelihood 1,471.535 1,466.813

Degrees of freedom 4.000 3

Akaike information criterion 1,477.535 1,474.813

Bayesian information criterion 1,488.617 1,489.588

Likelihood ratio test 4.72 (1), p=0003

aUnstandardized coefﬁcients.

bDummy coded.

cMean centered.

on reducing FAE commission for bias blind spot scores

lower than 2.55 (original scale) (2415=3087, p=0005),

but not for bias blind spot scores higher than 2.55.

The effect of a training procedure designed to

reduce commission of the FAE was stronger for par-

ticipants with a lower susceptibility to the bias blind

spot than for participants with a higher susceptibility

to the bias blind spot. The results suggest that high

susceptibility to the bias blind spot may constitute a

barrier to bias reduction.

General Discussion

The foregoing studies provide an in-depth portrait of

the bias blind spot. We found that the tendency for

people to be better able to identify bias in the judg-

ments and behaviors of others than in their own judg-

ment and behavior is captured by a single latent fac-

tor, and that there is substantial individual variation in

susceptibility to this meta-bias. Using a psychometric

approach, we developed and validated an instrument

to measure individual differences in susceptibility to

the bias blind spot. We found that the bias blind spot

is discriminated from intelligence, cognitive ability,

cognitive reﬂection, and personality traits related to

self-esteem, self-enhancement, and self-presentation.

Moreover, the bias blind spot does not appear to be a

facet of general decision-making ability or an accurate

perception of the extent to which one is biased. Rather,

susceptibility to the bias blind spot appears to be a

true blind spot in self-awareness, because of asymmet-

ric consideration of evidence when assessing the self

and other people.

Susceptibility to bias blind spot appears to predict

at least two kinds of bias in social judgment. First,

bias blind spot predicts the extent to which people

exhibit biases reliant on similar cognitive mechanisms,

namely the consideration of different evidence in self–

other comparisons. Participants higher in susceptibil-

ity to bias blind spot were more likely to ignore the

abilities of others when judging their own abilities,

resulting in a greater propensity to believe that they

were better than average on easy tasks and worse than

average on difﬁcult tasks than their peers (Study 3).

Second, bias blind spot predicts bias in judgments

reliant on self–other accuracy comparisons. It pre-

dicted the extent to which people ignored the advice

of others, as participants high in bias blind spot placed

less weight on advice than did participants low in bias

blind spot (Study 4). Susceptibility to the bias blind

spot was also associated with reduced responsiveness

to training designed to reduce the incidence of a dif-

ferent judgmental bias (Study 5).

Consequences and Implications of the Bias

Blind Spot

The identiﬁcation of individual differences in suscepti-

bility to the bias blind spot has important implications

for the incidence of bias and the improvement of judg-

ment and decision making. As we have demonstrated,

awareness of one’s vulnerability to bias is an impor-

tant antecedent of openness to advice that in turn

affects decision quality. Research maintains that inte-

grating advice from external sources improves deci-

sion making (Larrick and Soll 2006,Johnson et al. 2001,

Budescu and Rantilla 2000,Yaniv 2004; see Bonaccio

and Dalal 2006 for a review). People high in bias blind

spot, for example, may be particularly likely to ignore

the advice of peers or experts when engaging in ﬁnan-

cial or medical decision making and require alterna-

tive forms of guidance to improve the quality of their

decisions.

Second, awareness of one’s susceptibility to bias

blind spot appears to be an important indicator of

receptivity to efforts to improve one’s decision mak-

ing. Indeed, Wilson and Brekke (1994) suggest that a

critical impediment to the correction of the contami-

nating inﬂuence of biases on judgment is people’s lack

of humility about their vulnerability to bias. When

people are unaware of their bias, they are unlikely

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

to adopt corrective strategies to avoid the sources of

bias that inﬂuence their judgment. Consequently, peo-

ple who are more susceptible to bias blind spot are

less prone to improve their decision making by engag-

ing in bias reduction strategies, responding to training,

and taking advice. Consequently they may need more

persuasion or evidence that their decision making is

subject to error to acknowledge their potential for bias.

Our results corroborate this view by showing that

people who believe that they are relatively immune to

bias are less likely to enact corrective strategies, even

when correcting strategies are explained and explic-

itly suggested. In the work place, the results suggest

that employees high in bias blind spot may be less

receptive to training designed to improve their deci-

sion making, and may need more or different kinds of

training. At home, the results suggest that people high

in bias blind spot may need more or different forms

of guidance to improve their ability to make conse-

quential decisions affecting their personal life. It has

been proposed that decision making may be a teach-

able skill (Baron and Brown 1991,Bruine de Bruin et al.

2007,Fischhoff 1982,Larrick 2004), and correlational

evidence has suggested that people who have received

formal training in decision making may obtain bet-

ter life outcomes (Larrick et al. 1993). If so, then the

bias blind spot represents an obstacle to improving

the quality of both work and life because it bolsters

resistance to debiasing training aimed at improving

decision-making ability. This inﬂuence is not irrevo-

cable. We have found that propensity to exhibit bias

blind spot can be reduced by as much as 39% in a

related research program consisting of scenarios in

which participants could exhibit bias blind spot and

were then provided with critical feedback and training

(Symborski et al. 2014).

Third, some have argued that the bias blind spot is

a critical determinant of misunderstanding, mistrust,

and pessimism when attempting to reach agreements

in interpersonal, political and professional relation-

ships as it obstructs the resolution of conﬂicts (Pronin

et al. 2004), and have highlighted the importance of

mitigating bias blind spot to promote dialogue, diplo-

macy, and peace processes (Pronin et al. 2004). Pronin

et al. (2006), for example, elucidate the consequences

of perceiving others as more biased than oneself in the

context of terrorism. When terrorists were depicted as

biased and irrational rather than objective and ratio-

nal, people indicated a greater preference for a mili-

tary over a diplomatic resolution to a conﬂict, which is

likely to result in a spiral of conﬂict escalation. Assess-

ing individual differences in bias blind spot may help

predict the likelihood of interpersonal conﬂict, misun-

derstanding, and the need for dispute resolution. For

example, high susceptibility to the bias blind spot may

exacerbate the perception of the gap between antago-

nists in a controversy (i.e., false polarization, Robinson

et al. 1995,Pronin et al. 2002). People high in bias blind

spot, being more likely to believe that their view is

correct, may be less likely to take the necessary steps

to arrive at an agreement. More generally, susceptibil-

ity to the bias blind spot is likely to predict problem-

atic interpersonal interactions when those interactions

require one to acknowledge the potential for bias or

error in one’s own thinking and reasoning.

Limitations and Directions for Future Research

Of course, the present research is not without its own

limitations. We relied upon samples drawn from AMT

that are not representative samples. Fortunately, AMT

samples have been shown to exhibit vulnerability to

biases in judgment and decision making similar to tra-

ditional college samples (Berinsky et al. 2012,Horton

et al. 2011,Paolacci et al. 2010). We thus expect that

the results should generalize to other samples.

In the discriminant validity testing conducted in

Study 2, we collected measures to test multiple rela-

tionships within the same sample (Sample 1). Most of

the correlation coefﬁcients computed on this sample

were not signiﬁcant, but we did observe three small

signiﬁcant correlations. Considering the fact that these

signiﬁcant correlations were obtained within a larger

set of multiple comparisons, it is possible that their

signiﬁcance level is inﬂated. We have refrained from

drawing strong inferences from those results.

Although our discriminant validity test shows how

the bias blind spot is distinct from and independent

of several other constructs, in Studies 3–5 we did not

pit our scale against other potential predictors of the

judgments and behaviors investigated. Our choice not

to include measures for which we would expect to

ﬁnd null results was driven by the tradeoff between

ruling out a potential alternative and using a length-

ier study likely to induce participant fatigue and at an

additional cost. Future research may compare the pre-

dictive power of the bias blind spot scale with that of

other traits and biases.

Conclusion

We ﬁnd that bias blind spot is a latent factor in

self-assessments of relative vulnerability to bias. This

meta-bias affected the majority of participants in our

samples, but exhibited considerable variance across

participants. We present a concise, reliable, and valid

measure of individual differences in bias blind spot

that has the ability to predict related biases in self-

assessment, advice taking, and responsiveness to bias

reduction training. Given the inﬂuence of bias blind

spot on consequential judgments and decisions, as

well as receptivity to training, this measure may prove

Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and Consequences

useful across a broad range of domains such as per-

sonnel assessment, information analysis, negotiation,

consumer decision making, and education.

Acknowledgments

The authors gratefully recognize the research support of a

contract from the Intelligence Advanced Research Projects

Activity (IARPA) via the Air Force Research Laboratory

(AFRL) to Carey K. Morewedge and Karim S. Kassam. The

U.S. Government is authorized to reproduce and distribute

reprints for Governmental purposes notwithstanding any

contained herein are those of the authors and should not be

interpreted as necessarily representing the ofﬁcial policies

or endorsements, either expressed or implied, of IARPA,

AFRL, or the U.S. Government. The authors thank Nino

Miceli for advice on the data analysis.

Appendix. Fundamental Attribution Error

Training

Debiasing Training

Please read the following:

A mistake in judgment that people often make is under-

estimating how much a situation can determine someone’s

behavior. This error occurs when we think another person’s

action tell us something meaningful about their personal-

ity when instead anyone placed in their shoes would have

acted the same way. For example, one might see people

in anxiety-inducing situations (e.g., bungee jumping for the

ﬁrst time), and infer that they are anxious people in general,

rather than infer that they are average people in a nerve-

racking situation. Similarly, one may think that an author

paid to write in favor of a political leader has actually a

favorable opinion of that political leader.

To combat this error, we have to acknowledge the impor-

tance of situations in determining behavior.

Control Training

Please read the following:

Our decisions to trust people with our money are based

more on how they look then how they behave, according

to a new study. Researchers found people are more likely

to invest money in someone whose face is generally per-

ceived as trustworthy, even when they are given negative

information about this person’s reputation.

The researchers found that 13 out of 15 participants

invested more, on average, in the trustworthy identities.

In a second experiment, the researchers gave the volun-

teers information about whether the trustees had good or

bad histories. Even with this inside information, the average

amount invested in those who looked “trustworthy” was

6% higher.

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