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Citation: Scopelliti, I., Morewedge, C. K., McCormick, E., Min, L., LeBrecht, S. and Kassam, K. (2015). Bias Blind Spot: Structure, Measurement, and Consequences. Management Science, 61(10), pp. 2468-2486. doi: 10.1287/mnsc.2014.2096

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Bias Blind Spot: Structure, Measurement, andConsequencesIrene 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 BlindSpot: Structure, Measurement, and Consequences. Management Science 61(10):2468-2486. http://dx.doi.org/10.1287/mnsc.2014.2096

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MANAGEMENT SCIENCEVol. 61, No. 10, October 2015, pp. 2468–2486ISSN 0025-1909 (print) � ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.2014.2096

© 2015 INFORMS

Bias Blind Spot: Structure,Measurement, and Consequences

Irene ScopellitiCass Business School, City University London, London EC1Y 8TZ, United Kingdom, [email protected]

Carey K. MorewedgeQuestrom School of Business, Boston University, Boston, Massachusetts 02215, [email protected]

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

[email protected]

H. Lauren MinLeeds School of Business, University of Colorado, Boulder, Colorado 80309, [email protected]

Sophie Lebrecht, Karim S. KassamDietrich School of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

{[email protected], [email protected]}

People exhibit a bias blind spot: they are less likely to detect bias in themselves than in others. We reportthe 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 isdiscriminated 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 abilitiesto 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 suggestthat the bias blind spot is a distinct metabias resulting from naïve realism rather than other forms of egocentriccognition, 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 anddecision making. Published online in Articles in Advance April 24, 2015.

IntroductionPeople exhibit numerous systematic biases in judg-ment (Tversky and Kahneman 1974, Kahneman et al.1982, Nisbett and Ross 1980), many of which are dueto unconscious processes (Morewedge and Kahneman2010, Wilson and Brekke 1994). A lack of consciousaccess to judgment-forming processes means thatpeople are often unaware of their own biases (Nisbettand Wilson 1977) even though they can readily spotthe 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 behaviorthan are their peers. Most people recognize that otherpeople are likely to be biased when judging an attrac-tive person, for example, but think that their ownjudgment of an attractive person is unaffected by thistype of halo effect. Because the majority of peoplecannot be less biased their peers, this phenomenon isreferred to as the bias blind spot (Pronin 2007; Proninet al. 2002, 2004).

We show that people differ in their propensityto 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 uniqueconstruct independent of intelligence and personal-ity traits related to self-esteem, self-enhancement, andself-presentation. Bias blind spot appears to be inde-pendent of general decision-making competence. Inother words, the belief that one is less biased thanone’s peers appears to reflect a biased perception ofthe 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 findthat the propensity to believe that one is less biasedthan one’s peers has detrimental consequences onjudgments and behaviors that rely on self–other accu-racy comparisons, including decreasing receptivity touseful (i.e., debiasing) advice.

Bias Blind SpotPeople exhibit a tendency to believe they are lessbiased than their peers. College students believe

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Scopelliti et al.: Bias Blind Spot: Structure, Measurement, and ConsequencesManagement Science 61(10), pp. 2468–2486, © 2015 INFORMS 2469

they are less biased than their classmates, airlinepassengers believe they are less biased than other pas-sengers, and Americans believe they are less biasedthan their fellow citizens (Pronin et al. 2002). This com-mon asymmetry in the assessment of bias in the selfand in others has been observed across a variety ofsocial and cognitive biases. This bias blind spot occurswhether people explicitly rate the extent of their biasrelative to their peers, or rate the absolute extent oftheir bias and separately rate the extent of bias exhib-ited by their peers (Pronin et al. 2002, 2004; Proninand 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 andnaïve realism (Pronin et al. 2004, Pronin and Kugler2007). The introspection illusion results from differ-ences in the availability and perceived diagnosticvalue of introspection when assessing oneself andother people. When people evaluate the extent ofbias 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 howI judge his speaking ability”). Because introspectionis unlikely to reveal biased thought processes, peopleconclude that their judgment and behavior are unbi-ased. Naïve realism, the belief that one’s perceptionreflects the true state of the world, then generates afalse sense that these charitable self-assessments aregenuine 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 othersare not accessible. Consequently, the biased behav-ior of others is not excused by an ostensibly unbi-ased thought process (e.g., “Joan might not realizeit, but she probably thinks Rodger speaks eloquentlybecause she is attracted to him”). Because people usedifferent types of evidence when assessing bias in theself and in other people, less bias is perceived in theself than in others. The variety of social and cogni-tive biases for which this bias blind spot has beenobserved (Pronin et al. 2004, West et al. 2012) sug-gests that susceptibility to the bias blind spot may bea higher-order latent factor underlying the belief thatone is less likely to exhibit a variety of specific biasesthan are one’s peers.

Bias Blind Spot and Decision MakingThe propensity to exhibit decision-making biasesappears to vary systematically (Levin et al. 2002,Stanovich 1999, West and Stanovich 1997). Severalstudies 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 Bruinet al. (2007) demonstrated that decision-making com-petence, measured as composite performance on mul-tiple decision-making tasks, reflects the outcome ofreliable individual differences and predicts real-worlddecision-making ability.

In addition to clarifying the robustness and unique-ness of the bias blind spot, a major goal of the presentresearch was to examine its relationship with gen-eral decision-making ability. If a single higher-orderconstruct determines the extent to which people rec-ognize their own bias, three possible relationshipsbetween the bias blind spot and general decision-making ability emerge.

First, people who are lowest in decision-makingability may be least aware of their own bias, andhence may be most likely to exhibit the bias blindspot. Indeed, the least skilled are often least able toassess their level of skill (Kruger and Dunning 1999,Dunning et al. 2003). If bias blind spot is simply onecomponent of general decision-making ability, suscep-tibility to the bias blind spot should be negativelycorrelated with decision-making competence.

Second, people may accurately assess their degreeof 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 thiscase, bias blind spot may not really reflect suscep-tibility to a blind spot at all, and there should bea positive correlation between bias blind spot anddecision-making competence.

Third, susceptibility to the bias blind spot may beunrelated to general decision-making ability. Peoplewho have not been trained in decision making, andare therefore unaware of common biases in reason-ing and judgment, show considerable variation intheir decision-making competence (Bruine de Bruinet al. 2007). This suggests that superior decision mak-ing is a function of calibrated intuitions rather thanawareness of appropriate decision-making strategies.Indeed, research on the limitations of introspectionshows that people have limited insight into the pro-cesses by which their judgments and decisions aremade (Nisbett and Wilson 1977, Wilson and Dunn2004). Furthermore, tests comparing bias blind spotfor several specific biases and the commission ofthose 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 ScaleWe suggest that the bias blind spot is weakly, if atall, related to general decision-making ability. Morespecifically, 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

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and predictable ways. Rather than indicate an overlypositive view of the self, it reflects the tendency toconsider different evidence when making self–otheraccuracy 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 accuracycomparisons, we suggest that bias blind spot shouldalso influence judgments informed by these (social)comparisons such as (b) the weight placed on advicefrom others and (c) receptivity to debiasing training.Next, we unpack each of these predictions in turn.

A first prediction is that when evaluating theirown abilities, people exhibiting more bias blind spotshould exhibit more naïve realism in their initialassessments of their self (e.g., “I’m great at usinga computer mouse but terrible at juggling”) andless often correct those assessment for relevant infor-mation about others’ abilities (e.g., “0 0 0as are mostpeople”). Rather than simply view the self more pos-itively, they should be more likely to exhibit biasesreliant on similar cognitive mechanisms. For exam-ple, they should be more likely to exhibit a better-than-average effect when evaluating their ability toperform easy tasks, and a worse-than-average effectwhen evaluating their ability to perform difficulttasks (Kruger 1999).

A second prediction is that commission of biasblind spot should predict bias in judgments informedby self–other accuracy comparisons. People who thinkthey are less vulnerable to bias than their peers, forexample, may attend less to the opinions of othersbecause they believe that others have a more biasedperception of the world, consistent with the operationof naïve realism (Ross and Ward 1996, Pronin et al.2004, Liberman et al. 2012). This should lead them toplace less weight on the advice of others when giventhe opportunity to incorporate that advice into theirown judgments.

As another example, people who think they areless vulnerable to bias than their peers should believethat their judgment and decision making is in lessneed of correction (Wilson and Brekke 1994). Analo-gous to interventions aiming to curb addiction, whereawareness of the problem is a necessary first stepin facilitating corrective action, awareness of biasmay be an important precursor to bias mitigation(Kruger and Dunning 1999, Wilson and Brekke 1994).Consequently, greater commission of bias blind spotshould reduce the likelihood of benefitting from bias-reducing interventions.

Overview of the StudiesWe adopted a psychometric approach to the anal-ysis of the bias blind spot. Our first two studies

report the development of our individual-differencemeasure to assess the extent to which a personbelieves she is less biased than her peers, the evalu-ation of its reliability, the verification of its factorialstructure, and the analysis of its discriminant valid-ity in relationship with potentially related constructssuch as intelligence, cognitive reflection, decision-making ability, and personality traits related to self-esteem, self-enhancement, and self-presentation. Ourlast three studies report tests of our three judgmentaland behavioral predictions.

Study 1In Study 1, we generated 27 scale items and thensubmitted them to a purification process resulting ina 14-item bias blind spot scale with good reliabil-ity and stability. We used an item-generation processaimed at capturing a broad sense of the construct todevelop the scale. In accordance with the underly-ing theory (Pronin 2008), we reasoned that the biasblind spot is a metabias that is exhibited across mul-tiple self-assessments. Therefore, we treated the biasblind spot as a latent variable that causes an asymme-try in the assessment of bias in the self and in otherswith respect to several biases, both in the social andcognitive domains. Accordingly, we approached theconceptualization and the operationalization of sus-ceptibility to the bias blind spot as a reflective mea-surement model (Bollen and Lennox 1991, Edwardsand Bagozzi 2000). That is, a model in which thedirection of causality is from the construct to theindicators, and in which changes in the underlyingconstruct are hypothesized to cause changes in theindicators. According to the proposed conceptualiza-tion, changes in individual susceptibility to the biasblind spot (i.e., a latent variable), will cause changesin the extent to the bias blind spot is observed forspecific biases in judgment and behavior.

Method

Participants. Initial Sample0 A total of 172 Ama-zon Mechanical Turk (AMT) workers (86 women;Mage = 3204 years, SD = 1005) accessed and completeda survey on the Internet and received $4 as com-pensation. In all studies we restricted participationto residents of the United States. Participation in anystudy reported here resulted in ineligibility for par-ticipation in subsequent studies (i.e., no person parti-cipated in more than one study). Participants were79.7% White, 7% Black, 5.2% multiracial, 4.1% Asian,and 1.2% Native American; 2.9% did not indicate theirethnicity. AMT workers have been shown to exhibitsusceptibility to biases in judgment and decision mak-ing similar to traditional college samples with respect

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to common tasks such as the Asian disease problem,the conjunction fallacy and outcome bias; to exhibitsimilar levels of risk aversion; and to exhibit similarlevels of cooperation in behavioral economics gamessuch as the prisoner’s dilemma (Berinsky et al. 2012,Horton et al. 2011, Paolacci et al. 2010).

Follow-Up Sample0 A total of 83 participants in theinitial 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. Participantsin 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 wereno differences between the initial sample and thefollow-up sample in terms of age (F = 0005, p > 0080),gender (�2 = 1000, p > 0060), or ethnicity (�2 = 2041,p > 0070).

Materials. Item Generation0 We began developingscale items by identifying existing indicators of theconstruct in the literature. We drew some questionsfrom Pronin et al. (2002) verbatim, modified questionsfrom this source, and wrote new questions with thesame structure. Each question was structured so thatit 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 negativelyconnoted words such as bias or error. After read-ing a description of each bias, participants rated theextent to which they exhibit that bias and the extentto which the average American exhibits that bias on7-point scales with endpoints, not at all (1) and verymuch (7).

Item Scoring0 Bias blind spot scores were calculatedby subtracting the perceived self-susceptibility to eachbias from the perceived susceptibility of the averageAmerican 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 andaverage American bias ratings was used as a measureof the magnitude of her bias blind spot.

Procedure. Participants rated their vulnerabilityand the vulnerability of the average American tocommit biases on 27 unique items. Item order wasrandom. After completing all 27 items, participantsreported their age, gender, and ethnicity. To assessthe test-retest reliability of the instrument, all partic-ipants in the initial sample were invited by meansof the AMT messaging system to complete the puri-fied version of the scale eight days after the initialadministration.

ResultsPurification0 We first reduced the number of items

to improve the psychometric properties of the instru-ment. We removed items that assessed bias blindspot by describing different occurrences of the sameunderlying judgmental bias. We then computed thecorrelations between each item and the rest of thescale, and removed items with item-to-total correla-tions lower than 0.40. This purification process led toa final instrument containing 14 items (see Table 1 forthe full list of items).

Reliability0 The 14-item scale shows high reliabil-ity (� = 0086), well above the acceptable thresholdof 0.70 (Nunnally 1978). All items appeared to beworth retention. No question eliminations would yielda higher value of the Cronbach’s alpha coefficient(�-if-item-deletedi <�). Of the 91 pairwise correla-tions between the items (M = 0030), 85 were positiveand 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 correlationequal 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; allts ≥ 6026, all ps < 00001 (for all means, see Table 3).

Exploratory Factor Analysis0 We submitted the14 bias blind spot items to a principal-componentsfactor analysis (PCA) followed by a parallel anal-ysis (Horn 1965). The parallel analysis suggests anunderlying single-factor structure, with only the firsteigenvalue observed in the data being higher thana parallel average random eigenvalue based on thesame sample size and number of variables. In thesingle-factor model, all 14 items load onto a singlefactor accounting for 35% of the total variance, andeach item has a high correlation with that factor (all�s > 0048; see Table 3).

Test-Retest Reliability0 An analysis of the second ad-ministration of the scale indicated that the instrumenthas consistently high reliability (� = 0088), and hightest-retest reliability (r4815= 0080, p < 00001), signalingstability of the bias blind spot scale over time.

DiscussionStudy 1 provided evidence for individual differencesin susceptibility to the bias blind spot. High inter-item correlations and factors loadings suggest thatsome people have a higher susceptibility to the biasblind spot than do others across a variety of judg-mental biases. The results from this first study sug-gest that our instrument reliably assessed individualdifferences in susceptibility to the bias blind spot. Inaddition, the instrument captures a single latent vari-able as proposed by our construct operationalization.Furthermore, the high test-retest reliability observed

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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 testifythat 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 otherpeople 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 theirbeliefs. For example, when exposed to negative evidence about their favorite political candidate, people tend toimplicitly counterargue against that evidence, therefore strengthening their favorable feelings toward thecandidate.

Confirmation bias

4 Psychologists have claimed that some people show a “disconfirmation” tendency in the way they evaluateresearch about potentially dangerous habits. That is, they are more critical and skeptical in evaluating evidencethat an activity is dangerous when they engage in that activity than when they do not.

Disconfirmation bias

5 Psychologists have identified an effect called “diffusion of responsibility,” where people tend not to help in anemergency situation when other people are present. This happens because as the number of bystandersincreases, a bystander who sees other people standing around is less likely to interpret the incident as aproblem, 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. Forexample, when two people engage in a bidding war for an object, they can end up paying much more than theobject 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 theway they view victims of assault crimes. That is, they are overly inclined to view the victim’s plight as one he orshe 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 attractivepeople. For instance, when it comes to assessing how nice, interesting, or able someone is, people tend tojudge 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 lessgenerous to people of a different race than to people of their race. This tendency has been shown to affect thebehavior 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 strengthof another person’s feelings. For example, people who have not been victims of discrimination do not reallyunderstand 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 politicalcandidates. That is, people’s assessments of qualifications, and their judgments about the extent to whichparticular candidates would pursue policies good for the American people as a whole, are influenced by theirfeelings 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 academicor job performance. That is, they tend to take credit for success but deny responsibility for failure. They seetheir successes as the result of personal qualities, like drive or ability, but their failures as the result of externalfactors, 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 withhousework.

Stereotyping

indicates that the individual difference tapped by ourscale is stable over time.

Study 2In Study 2, we verified the factorial structure of thebias 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 spotand related constructs that compose its nomologicalnetwork (Cronbach and Meehl 1955), we also testedthe discriminant validity of the bias blind spot scalein 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 thebias 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, anddecision-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

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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 bias2 Bandwagon effect 00287∗∗

3 Confirmation bias 00326∗∗ 00478∗∗

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

5 Disconfirmation bias 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, thecontent of the questionnaire varied as described next.All participants were AMT workers residing in theUnited States who accessed and completed a surveyon 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, and2.4% did not indicate their ethnicity. The five samplesdid 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 fromConfirmatory Factor Analysis

Average American Self Factor Completelyloading standardized

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

1 Action-inaction bias 5012 1033 3096 1069 9086∗∗∗ 00613 006602 Bandwagon effect 5073 1006 3060 1068 15007∗∗∗ 00675 005043 Confirmation bias 5055 1027 3098 1060 11090∗∗∗ 00642 005524 Diffusion of responsibility 5051 1027 4011 1077 10005∗∗∗ 00613 005645 Disconfirmation bias 5031 1017 4048 1052 7031∗∗∗ 00482 005526 Escalation of commitment 5036 1013 3057 1067 14028∗∗∗ 00575 005837 Fundamental attribution error 4063 1024 3016 1054 12016∗∗∗ 00496 004768 Halo effect 5089 1002 4016 1069 12013∗∗∗ 00674 005119 Ingroup favoritism 5001 1029 3045 1069 11097∗∗∗ 00598 00599

10 Ostrich effect 5020 1015 4035 1074 6026∗∗∗ 00558 0052011 Projection bias 5032 1033 4019 1065 7083∗∗∗ 00477 0047612 Self-interest bias 5075 1024 4075 1062 8010∗∗∗ 00568 0035513 Self-serving bias 5069 1008 4014 1059 13028∗∗∗ 00681 0067714 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 biasblind spot scale in a random order. Participants thencompleted a series of personality scales measuringextant psychological constructs potentially related tothe bias blind spot: the 10-item Rosenberg self-esteemscale (Rosenberg 1965), the need for uniquenessscale (32 items; Snyder and Fromkin 1977), the self-consciousness scale (22 items; Feingstein et al. 1975),the need for cognition scale (NFC; 18 items; Cacioppoet al. 1984), the self-monitoring scale (25 items; Snyder1974), the superiority scale (10 items; Robbins andPatton 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 answerformat and coding scheme. Both the order of thescales and the order of the items were randomized,

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with scale items nested within scale. Participants alsoreported their scores on the math and verbal sectionsof the SAT (if they had taken the SAT). To facilitatethe accuracy of participants who might have takenthe test several years before, participants reportedapproximate SAT scores on a 12-point scale increasingin 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 all14 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 originalCRT on AMT and the ease of retrieving the solution tothose three questions, participants were also admin-istered nine additional and less common CRT ques-tions (Frederick, personal communication) resultingin a total of 12 CRT items. Next, participants reportedtheir scores on the math and verbal sections of theSAT 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 completedall 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 deBruin 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 measureswhether value assessment is affected by irrelevantvariations in problem descriptions; recognizing socialnorms, which measures how well people assess peersocial norms (16 items); under/overconfidence, whichmeasures how well participants recognize the extentof their own knowledge (34 items); applying decisionrules, which asks participants to indicate, for hypo-thetical individual consumers using different decisionrules, which products they would buy out of a choiceset (10 items); consistency in risk perception, which mea-sures the ability to follow probability rules (16 paireditems); and resistance to sunk costs, which measures theability to ignore prior investments when making deci-sions (10 items). These tasks measure different aspectsof the decision-making process and decision-makingskills, 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. Participantsreported their scores on the math and verbal sectionsof 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 all14 items of the bias blind spot scale in a randomorder. 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 someof which do not actually exist (e.g., plates of paral-lax). These familiarity ratings serve as the basis fora claiming response bias index (OCQ bias) measur-ing self-enhancing tendencies: a statistical estimate ofhow strong familiarity with an item has to be for arespondent to express familiarity with it (Macmillanand Creelman 1991). The 11 batteries covered topicsincluding rap artists, rock artists, country artists, hor-ror movies, comedies, dramas, foreign movies, soapoperas, football, world leaders, and fashion design-ers (Paulhus et al. 2003). Each battery included sevenreal items and three bogus items. Participants wereasked to rate their familiarity with each item on a 7-point scale with endpoints, not at all familiar (0) andvery familiar (6). Both the order of the batteries andthe order of the items were randomized, with itemsnested within battery. Finally, participants reportedtheir age, gender, ethnicity, and highest level of edu-cation completed.

Sample 5. Participants (n = 98) first completed all14 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 andSrivastava 1999). Participants reported their scores onthe math and verbal sections of the SAT if they hadtaken the test (as did Sample 1). Finally, participantsreported their age, gender, ethnicity, and highest levelof education completed.

ResultsConfirmatory Factor Analysis0 To 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. Wefirst evaluated the assumption of multivariate nor-mality of the data that is a necessary condition for theuse of maximum likelihood estimation. Toward thisend, we computed Mardia’s (1980) test of multivari-ate skewness and kurtosis. The test was significant(�2 = 299051, p < 00001), signaling that the assumptionof multivariate normality of the data was violated. Asa consequence we opted for a robust maximum like-lihood estimation.

The confirmatory factor analysis suggested that thesingle-factor model emerged in the exploratory fac-tor analysis from Study 1 fit the data in Study 2well, with a goodness-of-fit index of 0.93, a Bentler’scomparative fit index of 0.98, a non-normed fit indexof 0.97, and a root mean square error of approxima-tion (RMSEA) of 0.05, suggesting a good fit between

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Figure 1 Distribution of Bias Blind Spot Scores in Study 2

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-loadingestimates are reported in Table 3 and were all signifi-cant; all ts > 5049, all ps < 00001.

Descriptive Statistics0 To examine the distribution ofsusceptibility to the bias blind spot, we computed biasblind spot scores on the five samples following thesame procedure as in Study 1 (� = 0084). Across thefive samples, the distribution of the scores appearedto be positively skewed with an observed range from−1050 to 5.14 (see Figure 1). On average, participantsexhibited a significant bias blind spot (M = 1048, SD =

0095; significantly different from 0; t46605= 40029, p <00001). Furthermore, for each of the 14 biases thatcomposed the bias blind spot scale, participants ratedthe average American as more susceptible to that biasthan themselves; all Ms > 0090, all ts(659) > 16085,all ps < 00001. At the individual level, a large major-ity (85.2%) of participants exhibited a significant biasblind spot across the 14 items, with the averages oftheir scores being marginally or significantly greaterthan 0; all ts(13) > 1079, all ps < 0010. Only one par-ticipant (0.1%) had an average bias blind spot scorethat was significantly lower than 0 (t4135 = −2046,p = 0003).

We then examined whether demographic variablesaccounted for some differences in bias blind spotscores. Gender appeared unrelated to bias blind spot,as the scores of male (M = 1045, SD = 0095) andfemale participants (M = 1051, SD = 0095) were notsignificantly different; F < 1. Similarly, the correlationbetween age and bias blind spot scores was not sig-nificant, r = −0004, p = 0030. Bias blind spot scoreswere significantly and negatively affected by level ofeducation, following approximately a linear pattern(linear contrast: F 4116535= 11042, p = 00001).

Discriminant Validity0 We next examined whetherthe bias blind spot scale is distinct from related

constructs: (i) measures of intelligence, inclinationtoward 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-enhancementtendencies (i.e., self-esteem, superiority, need foruniqueness, narcissism, over-claiming tendency);(iii) personality traits related to self-presentation(i.e., self-monitoring, self-consciousness, and socialdesirability); and (iv) and general personality traits(i.e., BFI).

For each of the samples we computed bias blindspot scores (Alphas ranging between 0.82 and 0.85).For each of the constructs that were measuredusing multi-item scales, we computed overall aver-age scores after reverse scoring appropriate items, andwe examined the correlation of these scores with biasblind spot scores. Table 4 presents these scales andmeasures, their Cronbach’s alpha coefficients, means,standard deviations, and their zero-order correlationswith the bias blind spot scale.

Intelligence, Inclination Toward Cognitive Activi-ties, and Decision-Making Ability0 We first examinedwhether bias blind spot is merely a manifestation ofintelligence and cognitive ability in four ways. First,we examined SAT scores, as both the verbal and mathscores load highly on psychometric g or general intel-ligence (Brodnick and Ree 1995, Frey and Detterman2004, Unsworth and Engle 2007). Of the 559 partic-ipants in Samples 1, 2, 3, and 5, 248 reported theirSAT test scores. The correlation between bias blindspot scores and the verbal SAT scores was not sig-nificant (r42475 = 0010, p = 0013), whereas the corre-lation between bias blind spot scores and the mathSAT scores (r42475= −0013, p = 0005), was significant,albeit very small.

Second, we examined the correlation between biasblind spot and NFC, which refers to the extent to

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

Construct Sample M SD � N r

Intelligence 1–3, 5SAT math 605004 111055 — 248 −0013∗

SAT verbal 640032 93022 — 248 0010Need for cognition 1 63065 13021 0093 260 0016∗

Cognitive Reflection Test 212 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 3Resistance to framing 1045 0054 0058 100 0014Recognizing social norms 0051 0026 0089 100 0007Under/overconfidence 0091 0007 0089 100 0006Applying decision rules 0077 0016 0050 100 0002Consistency in risk perception 0075 0015 0071 100 0009Resistance to sunk costs 4040 0062 0040 100 0008

Big Five personality traits 5Neuroticism 24030 5065 0087 98 0006Extraversion 22084 7042 0088 98 0003Openness 36062 6081 0085 98 0033∗∗

Agreeableness 31076 5030 0083 98 0010Conscientiousness 33002 6042 0083 98 0007

Self-esteem 1 19096 6026 0092 260 0015∗

Superiority 1 35013 7039 0075 260 −0002Need for uniqueness 1 101023 17034 0089 260 0016∗∗

Narcissism 1 20040 3065 0082 260 0004Over-claiming 4 0050 0026 0095 102 −0025∗

Self-consciousness 1Public 18062 4058 0082 260 −0005Private 25045 4057 0073 260 0007Social anxiety 16010 5001 0087 260 −0010

Self-monitoring 1 11018 4043 0074 260 0005Social 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). Peoplewho are high in NFC expend more effort processinginformation, and perform better on arithmetic prob-lems, anagrams, trivia tests, and college coursework(Cacioppo et al. 1996). In line with the results ofWest et al. (2012), we observed a small but signifi-cant and positive correlation between bias blind spotscores and need for cognition (r42585= 0016, p = 0001).This significant correlation suggests that people witha high NFC tend to believe that they are less suscep-tible to biases than their peers.

Third, we examined whether participants withhigher bias blind spot scores make more use of intu-ition than of deliberate reasoning by examining thecorrelation between bias blind spot scores and theCRT (Frederick 2005), a set of questions that each havean incorrect intuitive response and a correct responsethat can be reached through deliberate reasoning. Wecomputed a composite measure of performance onthe CRT by assigning a score of one to each correctanswer, a score of zero to each incorrect answer, andsumming all items (M = 3067, SD = 3000). Bias blind

spot scores were significantly and negatively corre-lated with CRT scores (r4995 = −0022, p = 0002). Thispattern of correlations was consistent irrespective ofwhich CRT items were 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 ontheir intuition and less likely to engage in cognitivereflection 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 scores were uncorrelated withperformance on all of the A-DMC batteries; all rs <0014, all ps > 0018, average r = 0008.

Self-Esteem and Self-Enhancement0 We next examinedthe possibility that the bias blind spot is the manifes-tation of more basic perceptions of being superior toothers or being different from others (e.g., Taylor andBrown 1988, Snyder and Fromkin 1980) by examining

1 The levels of reliability for each battery reported in Table 4are generally consistent with those reported by Bruine de Bruinet al. (2007).

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the correlations between bias blind spot and superior-ity, self-esteem, need for uniqueness, narcissism, andover-claiming. If the domain of these constructs sig-nificantly overlaps with that of the bias blind spot, weshould observe high correlations between those traitsand 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 superiorto others may believe that they are less susceptible tocognitive and social biases than inferior others. How-ever, perceived self-superiority did not correlate withbias blind spot scores (r42585= −0002, p = 0072).

We then examined the correlation between biasblind spot and self-esteem (Rosenberg 1965), sincepeople holding high levels of self-esteem may bemore likely to see themselves as immune from biasand therefore show higher susceptibility to the biasblind spot. We found evidence for a significant andpositive correlation between bias blind spot scoresand self-esteem (r42585 = 0015, p = 0002), suggestingthat people who believe they are less vulnerable tobias than their peers tend to have higher levels ofself-esteem. The small size of the correlation, how-ever, suggests that the two constructs are theoreticallydistinct.

We also examined the possibility that the belief thatone is less vulnerable to bias than one’s peers is amanifestation of a need for uniqueness (Snyder andFromkin 1977), the desire to be dissimilar from oth-ers. The correlation between bias blind spot scoresand need for uniqueness was positive and significant(r42585= 0016, p < 0001). This small significant correla-tion suggests that people high in bias blind spot havea higher desire to distinguish themselves from others,but that the two constructs are theoretically distinct.

Finally, we examined the relationship between thebias blind spot and two different operationalizationsof self-enhancement, a narcissism scale (Ames et al.2006), and over-claiming (Paulhus 1998). Narcissism,a personality trait that involves a pervasive patternof grandiosity, self-focus, and self-importance accom-panied by preoccupation with success and demandsfor admiration (Morf and Rhodewalt 2001), has beenused as a measure of trait self-enhancement (Paulhus1998). The narcissism scale did not correlate withbias blind spot scores (r42585 = 0004, p = 0049). Over-claiming, a measure that can discriminate accuracyfrom self-enhancement in response patterns, wasassessed by using the OCQ (Paulhus et al. 2003).We computed OCQ bias indexes, corresponding tothe mean of the hit rate and the false-alarm rate forreal and false OCQ items (Paulhus et al. 2003), foreach 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 orderfor a respondent to claim familiarity with an item. Thecorrelation between bias blind spot scores and OCQbias was negative and significant, r41015= −0025, p =

0001, suggesting that higher bias blind spot scoresare associated with higher self-enhancing tendenciesas measured by over-claiming, but that the two con-structs are sufficiently discriminated.

Self-Presentation0 We examined whether the biasblind spot is the manifestation of other personalitytraits related to self-presentation. Self-consciousnessis an acute sense of self-awareness, articulated inthree dimensions: private self-awareness (i.e., a ten-dency to introspect and examine one’s inner self andfeelings), public self-consciousness (i.e., a tendencyto think about how the self is viewed by others),and social anxiety (i.e., a sense of apprehensivenessover 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 andprivate self-awareness (r42585=0007, p=0024), publicself-consciousness (r42585=−0005, p=0046), or socialanxiety (r42585=−0010, p=0011). Self-monitoring mea-sures the extent to which one consciously employsimpression management strategies in social interac-tions (Snyder 1974). We did not observe a significantcorrelation between bias blind spot scores and self-monitoring (r42585=0005, p=0040). Lastly, we soughtto test whether there was a negative correlationbetween bias blind spot and the tendency to answerin 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 Traits0 Finally, we examinedthe correlations between bias blind spot and theBig Five personality traits (McCrae and Costa 1987).Bias blind spot scores appeared to be uncorrelatedwith 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 reportedsignificantly higher openness to experience had higherbias blind spot scores, although the moderate size ofthe 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 ofdomains (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 someawareness of that ability, they might accurately reportthat they are less susceptible to bias than their peers.

DiscussionBias blind spot appears to be a latent unidimensionalconstruct that induces people to see themselves as less

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biased than other people across multiple judgmentalbiases (i.e., a meta bias). Bias blind spot scores didnot appear to be correlated with demographic vari-ables such as gender and age, although bias blindspot decreased significantly as level of educationincreased. The results of Study 2 support the dis-criminant validity of the bias blind spot by showingthat 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 werenot significant. Other correlations were in the low tomoderate range (0.17 to 0.33; Cohen 1988), suggestingthat the bias blind spot scale is assessing a distinctconstruct.

Most importantly, the perception that one is lessvulnerable to bias than one’s peers truly appears toconstitute a blind spot in self-awareness. The lack ofcorrelation with general decision-making competencesuggests that susceptibility to bias blind spot (a) doesnot reflect generally poorer decision-making ability,and (b) does not reflect an accurate self-assessmentof decision-making ability. The negative correlationobserved between susceptibility to bias blind spot andCRT scores could be interpreted as supporting a posi-tive relation to more general cognitive ability, but alsolends support our suggestion that the bias blind spotis a distinct meta-bias reflected in the different con-sideration of evidence when making self and otherassessments. In other words, people who are morelikely to rely on their intuition may more biased, butalso be less likely to correct their initial introspec-tive self-assessments by considering additional evi-dence, such as their behavior. A more direct test ofthis assumption was performed in Study 3.

Study 3: Bias Blind Spot andSelf-AssessmentIn Study 3, we further elucidated the cognitive mech-anisms underlying the bias blind spot by examin-ing the relationship between susceptibility to the biasblind spot and the asymmetric use of evidence inanother set of self–other comparisons: relative perfor-mance assessments. When evaluating their own skillsand abilities relative to their peers, people tend tofocus on their own capabilities and neglect the capa-bilities of others. Because they neglect the fact thateasy activities are easy for most people and difficultactivities 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 thanthe 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 blindspot should be more susceptible to the better-than-average effect, but not to the worse-than-averageeffect. 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 togreater naïve realism and confidence in initial self-assessments (e.g., “I’m great at using a computermouse but terrible at juggling”), coupled with a fail-ure to correct those assessment to account for other’sabilities (e.g., “0 0 0as are most people”), people high insusceptibility to the bias blind spot should be moresusceptible to both the better-than-average effect andthe worse-than-average effect.

Method

Pretest. In a pretest, 100 AMT workers (47 women;Mage =2806 years, SD=1004) were presented with alist of 34 activities in an online survey and com-pensated $1 for participating. Included in that listwere the activities used by Kruger (1999, Study 2).Participants rated the difficulty of each activity ona 9-point scale (1=not difficult at all; 9=very diffi-cult). We selected the two activities rated least difficultand the two activities rated most difficult: operating acomputer mouse (Mdifficulty =1034, SD=0084), copying andpasting text (Mdifficulty =1043, SD=1013), programminga computer (Mdifficulty =7058, SD=2058), and juggling(Mdifficulty =7068, SD=2028). A one-way repeated mea-sures ANOVA accompanied by planned comparisonsrevealed that the two difficult activities were perceivedas significantly more difficult than the two easy activ-ities (F 411995=11105052, p<00001), that the two easyactivities were perceived as equally easy, and that thetwo difficult activities were perceived as equally diffi-cult (Fs<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 were79.5% White, 8.3% Black, 4.5% Asian, 3.2% multira-cial, and 0.6% Native American; 3.8% did not indicatetheir ethnicity.

Materials and Procedure. Participants completedthe 14-item bias blind spot scale in a random order.Then they rated their comparative ability on the twoeasy (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, participantsreported their age, gender, and ethnicity.

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Results and DiscussionBias blind spot scores were computed following theprocedure described in Study 1 (� = 0083). A mixedmodel analysis was used to estimate the effect of sus-ceptibility to the bias blind spot, type of activity (easyversus difficult), and their interaction on compara-tive ability ratings. Since each participant assessed herability on both the easy and the difficult activities, welet the intercept vary randomly to take into accountthe lack of independence between the observations.We estimated the following model:

CAij = �0 +�1 BBSi +�2 TAj +�3 BBSi × TAj +Ui + �ij

where CA refers to comparative ability, index i refersto participants and index j refers to the type ofactivity (easy versus difficult). The dependent vari-able was thus the average comparative ability rat-ing expressed by each participant on easy or difficultactivities. 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, andUi indicated each participant’s random effect. Bothbias blind spot scores and comparative ability ratingswere standardized. Results are based on a total of624 observations, where each observation is a compar-ative ability rating on an activity provided by a par-ticipant. The results revealed a significant interactionbetween susceptibility to the bias blind spot and typeof activity (easy vs. difficult) on comparative abilityassessments (b = 0027, SE = 0006, t = −4084, p < 00001).Table 5 reports the results of this estimation and thecomparison of this factorial model with an alternativemodel that includes only the main effects.

The interaction between bias blind spot and thetype of activity was further explored by conducting asimple slope analysis. Bias blind spot was associatedwith 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 AbilityRatings (Study 3)

Main effect model Factorial model

Variable b a SE t p b a SE t p

Intercept 0072 0004 19067 <00001 0072 0004 19053 <00001Type Activity b −1044 0006 −24021 <00001 −1044 0006 −25035 <00001BBS c 0004 0003 1022 0022 0013 0004 3055 <00001Type Activity b ×BBS c −0027 0006 −4084 <00001

−2 Log-likelihood 1,286.19 1,263.85Degrees of freedom 6 7Akaike information criterion 1,298.19 1,277.85Bayesian information criterion 1,324.81 1,308.90Likelihood ratio test 22.34 (1), p < 00001

aUnstandardized coefficients.bDummy coded.cStandardized.

t = −3051, p < 00001). These results suggest that peoplehigh in susceptibility to the bias blind spot are moresusceptible to both the better-than-average effect andthe worse-than-average effect. These results providefurther evidence that the bias blind spot is a form ofegocentric cognition driven by an asymmetric consid-eration of evidence when evaluating the self and oth-ers, rather than by a general self-enhancement motivethat induces a person to see herself as superior toher peers.

Study 4: Bias Blind Spot andAdvice TakingIn Study 4, we began to examine our last set of predic-tions, that bias blind spot affects judgments informedby self–other accuracy comparisons. Specifically, weexamined the effect of the bias blind spot on theweight given to advice from other people (Yaniv andKleinberger 2000). If bias blind spot does influencebias in judgments reliant on self–other accuracy com-parisons, people who are more susceptible to thebias blind spot should view their judgments to bemore accurate than the judgments of other people,because they believe their judgments are based onmore objective perceptions of the world (Pronin et al.2004). Consequently, people high in bias blind spotshould give less weight to advice received from oth-ers. They should be less likely to correct their initial(self-informed) judgments when subsequently giventhe opportunity to incorporate others’ judgments intotheir own.

Method

Participants. A total of 178 AMT workers (109women; Mage = 3308 years, SD = 1202) received $8 forcompleting Study 4. Participants were 75.3% White,6.7% Black, 6.7% multiracial, 6.2% Asian, and1.7% Native American; 3.4% did not indicate theirethnicity.

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Materials and Procedure. Participants completedthe 14-item bias blind spot scale in a random order.Next, they estimated the weight of 30 householdobjects (e.g., a trash can, a statue, a doll house), one ata 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 initialweight estimate, participants were given advice in theform of the guess of another “randomly selected par-ticipant” in the study (i.e., the average estimate of twopilot participants), and were then given the opportu-nity to estimate the weight of the object again. Thus,participants had the option to revise their originalestimate based on the advice that they received fromthe other participant. The order in which objects werejudged was random. Finally, participants reportedtheir age, gender, and ethnicity.

Results and DiscussionWe computed bias blind spot scores following the pro-cedure described in Study 1 (� = 0082). We computedthe weight on advice (WOA) relative to each object,by computing the difference between the original esti-mate and the estimate made after receiving the adviceof 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 adviceshe receives (Yaniv 2004) and is defined as

WOA =final estimate − initial estimate

advice − initial estimate0

WOA is equal to 0 when participants entirely dis-count the advice. In such a case, final estimatesare equal to initial estimates, meaning that partici-pants did not revise their estimates after receivingthe advice. WOA is equal to 1 when participants’final estimate is equal to the advice received. Inthis case participants give maximum weight to theadvice received. Finally, WOA equals a value between0 and 1, when participants weigh both their initialestimate and the received advice positively. Follow-ing the procedure used in prior research (Gino andMoore 2007, Gino 2008, Yaniv 2004), cases in whichthe advice equaled the initial estimate were excludedfrom the analysis, since WOA in those cases equaled anumber divided by 0, making it impossible to quan-tify how much a participant did or did not use theadvice. For cases in which the final estimate did notfall 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 1were adjusted to 1.2 One participant whose aggregate

2 We 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 fromall subsequent analyses. No other participants wereexcluded. Results are based on a total of 5,049 obser-vations, each observation being the weight of anobject estimated by a participant.

A mixed model analysis was used to estimate theeffect 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 totake into account the lack of independence betweenthe observations. We estimated the following model,

WOAij = �0 + b1BBSi +Ui + �ij1

where index i referred to participants and index jrefers to objects. The dependent variable was thusthe value for WOA for each participant and for eachobject. The explanatory variable was each partici-pant’s bias blind spot score (BBSi5, and Ui indicatedeach participant’s random effect. The results revealeda significant and negative effect of susceptibility tothe bias blind spot on WOA (b1 = −0003, SE = 0001, t =

−3023, p = 00001). On average, participants with biasblind spot scores one standard deviation below themean placed six percent more weight on the adviceof others than participants with bias blind spot scoresone standard deviation above the mean (WOA = 00223vs. WOA = 00164).

This analysis was complemented by the estimationof a logit mixed model that examined the relationshipbetween bias blind spot scores and the rate at whichparticipants completely ignored advice (WOA = 0).We estimated the following model:

NO_WOAij = �0 + b1 BBSi +Ui + �ij1

where index i referred to participants and index jrefers to objects. The dependent variable was adummy 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. Theexplanatory variable was each participant’s bias blindspot score (BBSi), and Ui indicated each participant’srandom effect. Consistent with the previous model,the results revealed a significant and positive effect ofsusceptibility to the bias blind spot on the likelihoodof completely ignoring advice (b1 = 00104, SE = 00044,z= 2034, p = 0002). Susceptibility to the bias blind spotincreased participants’ likelihood of confirming theirinitial estimate and completely neglecting advice.

In short, participants who exhibited high levels ofbias blind spot were more likely to ignore the adviceprovided, whereas participants who exhibited lowlevels of bias blind spot weighed the advice moreheavily. These results are consistent with the resultsof Liberman et al. (2012), who showed that naïve

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realism, by making people believe that their ownperceptions of the world are objective, leads themto underweight the input of their peers. Our resultsextend those by Liberman et al. (2012) by showinghow individual differences in susceptibility to the biasblind spot significantly affect the extent to which peo-ple incorporate others’ advice in their judgments. Fur-thermore, our results provide additional evidence insupport of the role of naïve realism as the processunderlying the bias blind spot (Pronin et al. 2004).

Study 5: Bias Blind Spot and BiasReductionIn Study 5, we expanded our analysis of bias blindspot to see whether people characterized by highersusceptibility to the bias blind spot are more resis-tant to debiasing procedures. There is little evidencethat people who are more aware of their own biasesare 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 blindspot scores were less likely to commit bias. A rela-tionship between bias blind spot and decision-makingability might emerge, however, after an interventionaimed at reducing bias.

We predicted that the perception that one is lessvulnerable to bias than others may constitute a bar-rier to the activation of corrective strategies aimed atavoiding bias (Wilson and Brekke 1994). This barriermay prevent people who believe that others are moresusceptible to bias from actively responding to train-ing procedures designed to correct biased judgments.In order to test this prediction, we had participantsread an article designed to increase awareness of theoccurrence of a specific bias, the fundamental attribu-tion error (FAE) that also suggested how to mitigatethat bias. We predicted that susceptibility to the biasblind spot would moderate the effect of this debiasingtraining on subsequent commission of the FAE.

Method

Participants. A total of 297 AMT workers (170women; Mage = 3107 years, SD = 1007) completed anonline 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 biasblind spot scale with items in random order. Next,participants were randomly assigned to one of twoconditions: debiasing training or control. Participantsassigned to the debiasing training condition read anexplanatory 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 articledescribed the existence of the FAE, provided exam-ples of its occurrence, and explained how one mightcorrect 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 thesection “control training” in the appendix). All par-ticipants then answered nine questions designed totest the occurrence of the FAE in judgments inspiredby existing paradigms, in a random order (Jones andHarris 1967, Snyder and Frankel 1976, Scopelliti et al.2015). Finally, participants reported their age, gender,and ethnicity.

Results and DiscussionBias blind spot scores were computed following theprocedure described in Study 1 (� = 0080). Answersto the nine FAE questions were highly correlated withone another (� = 0080). We summed the number ofquestions in which participants exhibited the FAE thatserved as our primary dependent variable (M = 4017,SD = 2073). The FAE scores were neither normallydistributed, nor could be normalized by log transfor-mation. Therefore we estimated a Poisson regression,appropriate to model count dependent variables thathave only non-negative integer values.

We predicted that the debiasing training wouldreduce the incidence of bias more for participantswho were less susceptible to the bias blind spot. Thisprediction was tested using the procedures outlinedby Aiken and West (1991) to decompose the pre-dicted interaction using regression analysis. First, biasblind 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, theFAE 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 interactionbetween training and susceptibility to the bias blindspot (�2415 = 4073, p = 0003). To shed light on thenature of this interaction, we conducted a floodlightanalysis (Spiller et al. 2013) estimating the marginaleffect of training on commission of the FAE at all levelsof susceptibility to the bias blind spot. Table 6 reportsthe results of this estimation and the comparison ofthis factorial model with an alternative model thatincludes only the main effects. We used the Johnson-Neyman technique to identify the range of bias blindspot scores for which the effect of the debiasing train-ing was significant. This analysis revealed that therewas a significant positive effect of debiasing training

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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 b a SE Wald �2 p b a SE Wald �2 p

Intercept 1071 0006 819041 <00001 1081 0007 605062 <00001Type of Training b −0036 0006 39027 <00001 −0057 0011 26020 <00001BBS c −0008 0003 5087 0002 −0015 0005 10048 <00001Type Training b ×BBS c 0015 0007 4073 0003

−2 Log-likelihood 1,471.535 1,466.813Degrees of freedom 4.000 3Akaike information criterion 1,477.535 1,474.813Bayesian information criterion 1,488.617 1,489.588Likelihood ratio test 4.72 (1), p = 0003

aUnstandardized coefficients.bDummy coded.cMean centered.

on reducing FAE commission for bias blind spot scoreslower 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 toreduce commission of the FAE was stronger for par-ticipants with a lower susceptibility to the bias blindspot than for participants with a higher susceptibilityto the bias blind spot. The results suggest that highsusceptibility to the bias blind spot may constitute abarrier to bias reduction.

General DiscussionThe foregoing studies provide an in-depth portrait ofthe bias blind spot. We found that the tendency forpeople 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 insusceptibility to this meta-bias. Using a psychometricapproach, we developed and validated an instrumentto measure individual differences in susceptibility tothe bias blind spot. We found that the bias blind spotis discriminated from intelligence, cognitive ability,cognitive reflection, and personality traits related toself-esteem, self-enhancement, and self-presentation.Moreover, the bias blind spot does not appear to be afacet of general decision-making ability or an accurateperception of the extent to which one is biased. Rather,susceptibility to the bias blind spot appears to be atrue blind spot in self-awareness, because of asymmet-ric consideration of evidence when assessing the selfand other people.

Susceptibility to bias blind spot appears to predictat least two kinds of bias in social judgment. First,bias blind spot predicts the extent to which peopleexhibit 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 theywere better than average on easy tasks and worse thanaverage on difficult tasks than their peers (Study 3).Second, bias blind spot predicts bias in judgmentsreliant on self–other accuracy comparisons. It pre-dicted the extent to which people ignored the adviceof others, as participants high in bias blind spot placedless weight on advice than did participants low in biasblind spot (Study 4). Susceptibility to the bias blindspot was also associated with reduced responsivenessto training designed to reduce the incidence of a dif-ferent judgmental bias (Study 5).

Consequences and Implications of the BiasBlind SpotThe identification of individual differences in suscepti-bility to the bias blind spot has important implicationsfor 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 turnaffects 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 Bonaccioand Dalal 2006 for a review). People high in bias blindspot, for example, may be particularly likely to ignorethe 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 theirdecisions.

Second, awareness of one’s susceptibility to biasblind spot appears to be an important indicator ofreceptivity to efforts to improve one’s decision mak-ing. Indeed, Wilson and Brekke (1994) suggest that acritical impediment to the correction of the contami-nating influence of biases on judgment is people’s lackof humility about their vulnerability to bias. Whenpeople are unaware of their bias, they are unlikely

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to adopt corrective strategies to avoid the sources ofbias that influence their judgment. Consequently, peo-ple who are more susceptible to bias blind spot areless prone to improve their decision making by engag-ing in bias reduction strategies, responding to training,and taking advice. Consequently they may need morepersuasion or evidence that their decision making issubject to error to acknowledge their potential for bias.

Our results corroborate this view by showing thatpeople who believe that they are relatively immune tobias are less likely to enact corrective strategies, evenwhen correcting strategies are explained and explic-itly suggested. In the work place, the results suggestthat employees high in bias blind spot may be lessreceptive to training designed to improve their deci-sion making, and may need more or different kinds oftraining. At home, the results suggest that people highin bias blind spot may need more or different formsof guidance to improve their ability to make conse-quential decisions affecting their personal life. It hasbeen 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 correlationalevidence has suggested that people who have receivedformal training in decision making may obtain bet-ter life outcomes (Larrick et al. 1993). If so, then thebias blind spot represents an obstacle to improvingthe quality of both work and life because it bolstersresistance to debiasing training aimed at improvingdecision-making ability. This influence is not irrevo-cable. We have found that propensity to exhibit biasblind spot can be reduced by as much as 39% in arelated research program consisting of scenarios inwhich participants could exhibit bias blind spot andwere then provided with critical feedback and training(Symborski et al. 2014).

Third, some have argued that the bias blind spot isa critical determinant of misunderstanding, mistrust,and pessimism when attempting to reach agreementsin interpersonal, political and professional relation-ships as it obstructs the resolution of conflicts (Proninet al. 2004), and have highlighted the importance ofmitigating bias blind spot to promote dialogue, diplo-macy, and peace processes (Pronin et al. 2004). Proninet al. (2006), for example, elucidated the consequencesof perceiving others as more biased than oneself in thecontext of terrorism. When terrorists were depicted asbiased 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 islikely to result in a spiral of conflict escalation. Assess-ing individual differences in bias blind spot may helppredict the likelihood of interpersonal conflict, misun-derstanding, and the need for dispute resolution. Forexample, high susceptibility to the bias blind spot may

exacerbate the perception of the gap between antago-nists in a controversy (i.e., false polarization, Robinsonet al. 1995, Pronin et al. 2002). People high in bias blindspot, being more likely to believe that their view iscorrect, may be less likely to take the necessary stepsto arrive at an agreement. More generally, susceptibil-ity to the bias blind spot is likely to predict problem-atic interpersonal interactions when those interactionsrequire one to acknowledge the potential for bias orerror in one’s own thinking and reasoning.

Limitations and Directions for Future ResearchOf course, the present research is not without its ownlimitations. We relied upon samples drawn from AMTthat are not representative samples. Fortunately, AMTsamples have been shown to exhibit vulnerability tobiases in judgment and decision making similar to tra-ditional college samples (Berinsky et al. 2012, Hortonet al. 2011, Paolacci et al. 2010). We thus expect thatthe results should generalize to other samples.

In the discriminant validity testing conducted inStudy 2, we collected measures to test multiple rela-tionships within the same sample (Sample 1). Most ofthe correlation coefficients computed on this samplewere not significant, but we did observe three smallsignificant correlations. Considering the fact that thesesignificant correlations were obtained within a largerset of multiple comparisons, it is possible that theirsignificance level is inflated. We have refrained fromdrawing strong inferences from those results.

Although our discriminant validity test shows howthe bias blind spot is distinct from and independentof several other constructs, in Studies 3–5 we did notpit our scale against other potential predictors of thejudgments and behaviors investigated. Our choice notto include measures for which we would expect tofind null results was driven by the tradeoff betweenruling out a potential alternative and using a length-ier study likely to induce participant fatigue and at anadditional cost. Future research may compare the pre-dictive power of the bias blind spot scale with that ofother traits and biases.

ConclusionWe find that bias blind spot is a latent factor inself-assessments of relative vulnerability to bias. Thismeta-bias affected the majority of participants in oursamples, but exhibited considerable variance acrossparticipants. We present a concise, reliable, and validmeasure of individual differences in bias blind spotthat has the ability to predict related biases in self-assessment, advice taking, and responsiveness to biasreduction training. Given the influence of bias blindspot on consequential judgments and decisions, aswell as receptivity to training, this measure may prove

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useful across a broad range of domains such as per-sonnel assessment, information analysis, negotiation,consumer decision making, and education.

AcknowledgmentsThe authors gratefully recognize the research support of acontract [FA8650-11-C-7175] from the Intelligence AdvancedResearch Projects Activity (IARPA) via the Air ForceResearch Laboratory (AFRL) to Carey K. Morewedge andKarim S. Kassam. The U.S. Government is authorized toreproduce and distribute reprints for Governmental pur-poses notwithstanding any copyright annotation thereon.The views and conclusions contained herein are those ofthe authors and should not be interpreted as necessarilyrepresenting the official policies or endorsements, eitherexpressed or implied, of IARPA, AFRL, or the U.S. Govern-ment. The authors thank Nino Miceli for advice on the dataanalysis.

Appendix. Fundamental Attribution ErrorTraining

Debiasing TrainingPlease read the following:

A mistake in judgment that people often make is under-estimating how much a situation can determine someone’sbehavior. This error occurs when we think another person’saction tell us something meaningful about their personal-ity when instead anyone placed in their shoes would haveacted the same way. For example, one might see peoplein anxiety-inducing situations (e.g., bungee jumping for thefirst 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 authorpaid to write in favor of a political leader has actually afavorable opinion of that political leader.

To combat this error, we have to acknowledge the impor-tance of situations in determining behavior.

Control TrainingPlease read the following:

Our decisions to trust people with our money are basedmore on how they look then how they behave, accordingto a new study. Researchers found people are more likelyto invest money in someone whose face is generally per-ceived as trustworthy, even when they are given negativeinformation about this person’s reputation.

The researchers found that 13 out of 15 participantsinvested more, on average, in the trustworthy identities.In a second experiment, the researchers gave the volun-teers information about whether the trustees had good orbad histories. Even with this inside information, the averageamount invested in those who looked “trustworthy” was6% higher.

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