attitudes and social cognition a …...penalty) and was followed by criticisms of the study itself,...

ATTITUDES AND SOCIAL COGNITION

A Disconfirmation Bias in the Evaluation of Arguments

Kari EdwardsBrown University

Edward E. SmithUniversity of Michigan (Ann Arbor)

Two experiments provided evidence for a disconfirmation bias in argument evaluation such thatarguments incompatible with prior beliefs are scrutinized longer, subjected to more extensive refu-tational analyses, and consequently are judged to be weaker than arguments compatible with priorbeliefs. The idea that people are unable to evaluate evidence independently of prior beliefs has beendocumented elsewhere, including in the classic study by C. G. Lord, L. Ross, and M. R. Lepper(1979). The present findings contribute to this literature by specifying the processes by which priorbeliefs affect the evaluation of evidence. The authors compare the disconflrmation model to severalother models of how prior beliefs influence current judgments and present data that provide supportfor the disconfirmation model. Results indicate that whether a person's prior belief is accompaniedby emotional conviction affects the magnitude and form of the disconfirmation bias.

When evaluating an argument, can one assess its strength in-dependently of one's prior belief in the conclusion? A good dealof evidence indicates the answer is an emphatic no (e.g., Batson,1975; Chapman & Chapman, 1959; Darley & Gross, 1983;Geller & Pitz, 1968; Nisbett & Ross, 1980; Sherif & Hovland,1961). This phenomenon, which we refer to as the prior beliefeffect, has important implications. Given two people, or groups,with opposing beliefs about a social, political, or scientific issue,the degree to which they will view relevant evidence as strongwill differ. This difference, in turn, may result in a failure of theopposing parties to converge on any kind of meaningfulagreement, and, under some circumstances, they may becomemore extreme in their beliefs.

Perhaps the most renowned study documenting the prior be-lief effect is one conducted by Lord, Ross, and Lepper (1979);this study served as the starting point for our work. Lord et al.were concerned with people's evaluations of arguments about

Kari Edwards, Department of Psychology, Brown University; EdwardE. Smith, Department of Psychology, University of Michigan (AnnArbor).

The research reported in this article was supported by funds providedby Brown University and by US. Air Force Contract AFOSR-93-0265.We are grateful to Patrick Maher, whose ideas were instrumental to ourthinking about the normative status of the prior belief effect. We wouldalso like to thank Thane Pittman and Liz Tighe for their helpful com-ments on an earlier version of this article, Jon Baron for additional in-sights about the normative questions, J. B. Smeltzer for programmingassistance, and Hilary Ammazzalorso, Ray Ashare, Craig Malina, andSilvio Menzano for assistance in running the experiments and codingthe protocols.

Correspondence concerning this article should be addressed to KariEdwards, Department of Psychology, Brown University, Providence,Rhode Island 02912. Electronic mail may be sent via the Internet tokari [email protected].

whether the death penalty is an effective deterrent against mur-der. They selected two groups of participants, one known to be-lieve that the death penalty is an effective deterrent and oneknown to believe that it is not an effective deterrent. Bothgroups were presented with two arguments, one that pointed tothe deterrent efficacy of the death penalty and one that pointedto its inefficacy as a deterrent. Each argument consisted of abrief description of the design and findings of a study support-ing or opposing the death penalty (e.g., a study showing that astate's murder rate declined after institution of the deathpenalty) and was followed by criticisms of the study itself, aswell as rebuttals of these criticisms. The best-known finding as-sociated with this study is that the pro-death-penalty and anti-death-penalty participants became more polarized in their be-liefs—and hence more different from one another—as a resultof reading the two arguments. Note, however, that this result isa logical consequence of another more basic finding obtainedby Lord et al.: When participants were asked to rate how con-vincing each study seemed as evidence (i.e., assessments in-volved participants' judgment of the argument's strength ratherthan their final belief in the conclusion), proponents of thedeath penalty judged the pro-death-penalty arguments to bemore convincing or stronger than the anti-death-penalty argu-ments, whereas the opponents of the death penalty judged theanti-death-penalty arguments to be more convincing. This isthe prior belief effect, and it has as one of its consequences thepolarization of belief.

Given the importance of the prior belief effect, it is importantto identify the mechanisms that underlie it. Lord et al. suggestedthat the effect arises because people tend to accept at face valuethose arguments that are compatible with their prior beliefs buttend to scrutinize those arguments that are incompatible withtheir prior beliefs. This idea has been proposed by other inves-tigators as well (e.g., Ditto & Lopez, 1992; Koehler, 1993;Kunda, 1990; Ross & Lepper, 1980). Our objective in the pres-

Journal of Personality and Social Psychology. 1 » 6 , Vol. 71, No. 1.5-24Copyright 1996 by the American Psychological Association. Inc. 0022-3514/96/53.00

EDWARDS AND SMITH

ent article is to move beyond the straightforward and nowwidely accepted notion that prior beliefs affect the extent towhich relevant information is scrutinized and to specify theprocesses by which they do so. We sketch an explicit model ofhow such differential scrutiny comes about, generate predic-tions from this model, and present two experiments on argu-ment evaluation that support the predictions.1

Disconfirmation Model

Our central thesis is the same as Lord et al.'s (1979): Whenfaced with evidence contrary to their beliefs, people try to un-dermine the evidence. That is, there is a bias to disconfirm ar-guments incompatible with one's position. This idea can be de-veloped into a disconfirmation model by making the followingassumptions.

1. When one is presented an argument to evaluate, there willbe some automatic activation in memory of material relevantto the argument. Some of the accessed material will includeone's prior beliefs about the issue.

2. If the argument presented is incompatible with prior be-liefs, one will engage in a deliberative search of memory for ma-terial that will undermine the argument simply. Hence, "scruti-nizing an argument" is implemented as a deliberate memorysearch, and such a search requires extensive processing.2

3. Possible targets of the memory search include stored beliefsand arguments that offer direct evidence against the premisesand conclusion of the presented argument.

4. The outputs of the memory search are integrated withother (perhaps unbiased) considerations about the current ar-gument, and the resulting evaluation serves as the basis for judg-ments of the current argument's strength.

These four assumptions are embodied in the simple boxmodel diagrammed in Figure 1. The model readily explains theprior belief effect. Specifically, the evaluation of arguments thatare incompatible with one's prior beliefs is biased by counter-evidence retrieved during the memory search, whereas the eval-uation of arguments compatible with prior beliefs is not biasedby such counterevidence (see Figure 1).

In addition to explaining the prior belief effect, the discon-firmation model leads to the following three predictions.

Read Argument

Automatic Memory Search

Argument Compatible withPrior Beliefs?

1. Because memory searches are time consuming, partici-pants should take longer to evaluate arguments that are incom-patible with their prior beliefs than arguments that are compat-ible with these beliefs.

2. If participants are asked to report what they are thinkingwhile evaluating an argument, they should report more mate-rial when the argument is incompatible with their beliefs thanwhen it is compatible. This is because there will be substantiallymore output from the memory search in the case of incompati-ble arguments.

3. If participants are asked to report what they are thinkingwhile evaluating an argument, most of the reported materialwill be compatible with their prior beliefs. This is because theinitial activation of memory presumably retrieves mainly priorbeliefs, whereas the deliberate memory search retrieves mate-rial refuting arguments that are themselves incompatible withthe participants* prior beliefs (i.e., the deliberative search re-trieves material supportive of one's beliefs).

This set of predictions does not follow from some familiaralternative models of how prior beliefs influence current judg-ments. One such alternative, sometimes alluded to but rarelydeveloped, hinges on the notion that people with different atti-tudes about an issue have stored different beliefs (e.g., see Nis-bett & Ross, 1980). When applied to the Lord et al. (1979)study, the differential storage account would proceed as follows.When presented an argument, all participants search theirmemory for relevant material to bring to bear on the evaluationof this argument. Because pro-death-penalty participants havemainly pro-death-penalty material stored and anti-death-pen-alty participants have mainly anti-death-penalty materialstored, most of the retrieved material will be consistent withparticipants' prior beliefs. In turn, because this material willenter into the evaluation of the current argument, a prior beliefeffect should arise. This account further predicts that if partici-

yes

Final Evaluationof Argument

Deliberative Memory Searchto Undermine Argument

Figure L Disconfirmation model.

' The prior belief effect investigated in this article arises in the contextof inductive reasoning and should be distinguished from a seeminglyrelated effect obtained in studies of deductive reasoning. In the lattercase (e.g., Oakhill & Johnson-Laird, 1985), participants are presentedcomplex arguments (often syllogisms) and asked to determine whetheror not the conclusion is deductively valid; participants are more likely toerroneously judge a non valid conclusion to be valid when it is compati-ble with their prior beliefs than when it is incompatible. Most currentmodels of deductive reasoning ascribe this effect to a shallow heuristicthat is used to supply an answer when a problem is too difficult to behandled by the normal reasoning processes; the heuristic is essentially asuperficial strategy that participants use when real deductive reasoninghas broken down (for recent discussion, see Polk & Newell, 1995; Rips,1994). All of this is quite unlike the prior belief effect of interest in thepresent article. The latter arises even when participants are given sim-ple, inductive arguments to evaluate for strength (rather than for deduc-tive validity). Furthermore, no theoretical account treats this effect asthe outcome of a shallow heuristic, because there is reason to believethat the effect reflects the very heart of the judgmental process (Lord et.al., 1979).

2 It is possible that the decision to engage in an effortful search ofmemory is triggered by a motive to undermine the argument. But asit currently stands, the model we present has no explicit motivationalcomponent. The extent to which motivational factors are operative inthe disconfirmation model is an open question that might be worthy ofinvestigation in future studies.

DISCONFIRMATION BIAS

pants are asked to report what they are thinking about, theyshould retrieve mainly material consistent with their prior be-liefs. This prediction is the same as one made by the disconfir-mation model. But here the similarity ends. Unlike the discon-firmation model, the differential storage account predicts thatthe same amount of material should be retrieved regardless ofwhether the current argument is compatible or incompatiblewith prior beliefs. This is because in both cases, when memoryis accessed, most of the information retrieved will be consistentwith prior beliefs. It is not clear what predictions the differentialstorage account would make about the relative times needed toevaluate compatible and incompatible arguments.

Another alternative interpretation of the prior belief effect isthat advanced by Kunda (1990) in her review of how prior be-liefs and personal involvement affect reasoning. Again, theeffects of interest are mediated by memory search, but in thisaccount there is a variation in the kind of probe used to accessmemory. When people are presented an argument compatiblewith their prior beliefs, Kunda proposed that they use the con-clusion of that argument to access memory and suggested thatthe ensuing process is akin to one in which people seek confir-mation for a particular hypothesis. But if presented an incom-patible argument, people enter memory with a conclusion thatrefutes the position advocated and attempt to find confirmationfor this altered hypothesis. To illustrate in the context of theLord et al. (1979) study, pro-death-penalty participants will usea hypothesis that points to the deterrent efficacy of the deathpenalty to enter memory, regardless of whether the presentedargument itself is pro-death penalty or anti-death penalty. Be-cause the subsequent memory search will produce materialsimilar to this hypothesis or probe, most of the material re-trieved will be consistent with participants' prior beliefs, whichin turn will give rise to a bias in the evaluation of the presentedargument: hence, the prior belief effect. This account yields pre-dictions similar to those offered by the differential storage ac-count. It predicts that when participants report their thoughtswhile evaluating an argument, they should report the samenumber for compatible and incompatible arguments becausethe probe used to enter memory is identical in the two cases. Forthis same reason, participants should take no longer to evaluateincompatible than compatible arguments. Both of these predic-tions are in opposition to the disconfirmation model.

Overview of Experiment 1

Experiment 1 took place approximately 4-6 weeks after pre-testing. The experiment consisted of two stages. In Stage 1, par-ticipants read a set of 14 arguments and rated the strength ofeach one. The arguments, which were composed of a singlepremise and a conclusion, each pertained to an issue aboutwhich participants had a strong prior belief. We sought to ex-amine seven issues rather than a single one, as is frequently thepractice (e.g., Lord et al., 1979), to ensure that our resultswould not be due to some idiosyncratic aspect of the issue. InStage 2, which took place in the same session, participants com-pleted a thought-listing task. They were asked to generate allthoughts, feelings, or arguments that occurred to them as theyconsidered the conclusions of each of the arguments that theyhad just evaluated. This experiment took the form of a 7 X 2

X 2 mixed-model design; issue and version (pro vs. anti) werewithin-subject variables, and compatibility with prior belief(compatible vs. incompatible) was a between-subjects variable.Thus, for each of the seven issues, approximately half of theparticipants were in favor of the position advocated and approx-imately half were opposed to the position advocated. Further-more, for each issue, two arguments were presented, one advo-cating each side of the issue. Thus, for each of the seven issues,participants received one compatible and one incompatible ar-gument (compatibility reflecting the relationship between aperson's prior belief and the position advocated in a givenargument).

The major predictions of the disconfirmation model are asfollows.

Hypothesis 1: Arguments that are compatible with a person'sprior belief will be judged to be stronger than those that areincompatible with a person's prior belief.

Hypothesis 2: People will take longer to evaluate an argumentthat is incompatible with their beliefs than an argument that iscompatible with their beliefs.

Hypothesis 3: People will generate more thoughts and argu-ments when an argument is incompatible with their beliefs thanwhen it is compatible.

Hypothesis 4: Among the thoughts and arguments generated,more will be refutational (rather than supportive) in naturewhen the presented argument is incompatible with prior beliefsthan when it is compatible.

Pretest

One hundred twelve University of Michigan undergraduatescompleted a questionnaire that was presented as a survey ofstudents' opinions. The questionnaire included 45 belief state-ments concerning political, ethical, social, and academic issues.Participants indicated their agreement with each of the state-ments on a 7-point scale ranging from disagree completely (1)to agree completely (7). They also rated the strength of theirfeelings toward each issue on a 4-point scale ranging from nofeelings about the issue (1) to extremely strong feelings aboutthe issue (4). Finally, they rated how much knowledge or infor-mation they had about each issue on a 4-point scale rangingfrom I have no knowledge/information about this topic (1) to /have a great deal of knowledge/'information about this topic (4).They were reminded that people often can have strong opinionsand feelings toward an issue that they do not know much about.

Participants were encouraged to make their ratings carefullyand honestly. They were assured that there were no right orwrong answers and that their responses would remain confi-dential. On completion of the questionnaire, participants wereasked to provide their phone number if they were interested inparticipating in additional studies for remuneration. All partic-ipants received $2 for completing the questionnaire.

Seven of the original 45 items in the pretest questionnairewere selected for inclusion in the actual study on the basis of thefollowing criteria: First for each issue, there was an approxi-mately equal number of advocates and proponents. Second, foreach issue, the polarity of prior beliefs was not systematicallyrelated to the amount of prior knowledge about the issue (i.e.,on any given issue, proponents and opponents were equally

EDWARDS AND SMITH

knowledgeable). The Appendix contains the seven issues (beliefstatements) selected for inclusion in Experiment 1. Positive andnegative forms of these statements served as the conclusions ofthe arguments that participants evaluated in the experimentproper, resulting in a total of 14 arguments.

The means for each measure (prior belief, degree of emo-tional conviction, and amount of knowledge) are depicted inTable 1 for each issue selected. The correlations among thesemeasures, calculated separately for the seven issues, appear inTable 2. The correlation matrix indicates that, for six of theseven issues, the amount of knowledge participants reportedwas not correlated with the direction of their prior beliefs. Theonly exception to this pattern occurred for the issue of whetherit should be possible to execute minors who have been convictedof murder. For three of the seven issues, prior beliefs were nega-tively correlated with degree of emotional conviction.3 Finally,on all seven issues, there was a strong and significant correlationbetween degree of emotional conviction and self-reportedknowledge. That is, the more knowledge participants indicatedthey had about an issue, the stronger their emotional convic-tion. The relevance of these relationships is discussed in a sub-sequent section.

Experiment I

Method

Participants

Among the undergraduates who completed the pretest questionnaire,77 indicated an interest in participating in future studies. Of these, 68were reached by telephone and scheduled to participate in the experi-ment approximately 4-6 weeks later. Fifty-four participants completed

Table 2Correlations Among Prior Beliefs, Degree of EmotionalConviction, and Amount of Knowledge: Experiment 1

Issue Emotion

Death penaltyPrior beliefEmotion

Strike childPrior beliefEmotion

Hire minoritiesPrior beliefEmotion

Parental consent/abortionPrior beliefEmotion

Gay-lesbian adoptionsPrior beliefEmotion

Death sentence for minorsPrior beliefEmotion

Blood alcohol level checksPrior beliefEmotion

.00

-.32*

-.11

-.04

-.35*

-.48**

-.06

Knowledge

.09

.42**

- .08.53**

-.04.68**

.12-.54**

-.01.55**

- . 4 4 * *

.15

.63*

*/><.05. **p<.0\.

the experiment proper.11 Participants were tested in groups of 2-3 andwere seated in cubicles separated by a noise-reducing partition to max-imize privacy. They were asked to wear headphones during the entireexperiment to reduce the possibility of distraction due to ambient noise.Participants received $5 for completing the study, which lasted approx-imately 40 min.

Table 1Means and Standard Deviations for Prior Beliefs, Degreeof Emotional Conviction, and Knowledge for Eachof the Seven Issues: Experiment 1

Issue

Death penaltyStrike childHire minoritiesParental consent/

abortionGay-lesbian

adoptionsDeath sentence for

minorsBlood alcohol level

checks

Prior belief

M

4.503.874.31

4.42

4.52

4.63

3.85

SD

1.842.142.07

2.24

2.10

1.85

1.94

Emotion

M

2.923.403.25

3.65

3.21

3.02

2.92

SD

1.131.271.41

1.30

1.46

1.31

1.19

Knowledge

M

2.753.042.98

3.15

2.50

2.48

2.48

SD

0.650.790.92

0.75

1.04

0.80

1.00

Nate. N = 112. Participants indicated their agreement with each ofthe statements on a 7-point scale ranging from disagree completely {1)to agree completely (1). Strength of feelings toward each issue was indi-cated on a 4-point scale ranging from no feelings about the issue (1) toextremely strong feelings about the issue (4). Amount of knowledgeabout each issue was indicated on a 4-point scale ranging from / haveno knowledge/information about this topic (1) to / have a great deal ofknowledge/information about this topic (4).

Materials and Apparatus

The entire experiment was completed on IBM personal computers.Instructions and arguments were presented to participants on succes-sive screens of the computer such that participants could control thepace at which the screens advanced. The time participants required tomake their responses and the amount of time they spent reading each ofthe arguments were recorded in milliseconds.

Each argument consisted of a single premise and a conclusion. Therewere two arguments for each of the seven issues, one representing thepro side and the other representing the ami side of the issue. Argumentswere constructed so as to be moderately strong and to be refutable bymeans other than simply disagreeing with the implications of the con-clusion. To illustrate, consider the two presented arguments regardingthe abolition of the death penalty:

Pro side: Implementing the death penalty means there is a chancethat innocent people will be sentenced to i&a(/i. Therefore, the deathpenalty should be abolished.

3 Given that these correlations were obtained for only three of theseven issues, and given that the nature of the three correlations is notreadily intcrpretable, we suspect that they are idiosyncratic to these is-sues and do not pursue them further.

4 This number does not include participants whose first language wasnot English (« = 8), participants who changed their minds about par-ticipating in Stage 2 (« = 2), or participants who failed to show up forthe experimental session (n = 9).

DISCONFIRMATION BIAS

Ami side: Sentencing a person to death ensures that he/she willnever commit another crime. Therefore, the death penalty shouldnot be abolished.

Procedure

The experimenter began each session by explaining that the purposeof the study was to learn more about how people evaluate the strengthof arguments. The experimenter explained that there would be threepractice trials intended to familiarize participants with the procedureas well as the way in which the computer keyboard should be used tomake ratings. Participants were encouraged to ask the experimenter forclarification of any aspect of the procedure at the end of the practicesession if they needed it, and they were told that after this time theywould not be able to interrupt the experiment. They were told to workat their own pace and to expect that other participants in their sessionwould finish at different times because everyone was evaluating differentarguments (although this was not actually the case, this assurance wasconveyed to participants to minimize feelings of being rushed or self-conscious about their pace).

Participants received the following instructions in the first severalpanels of the computer:

In this experiment, you will be presented with arguments about 7different issues. For each issue, you will read two arguments, onerepresenting each side of the issue. Each argument will have apremise and a conclusion; the conclusion advocates a particularposition, while the premise supplies a reason for the position.

An example of a PREMISE is: Working parents should be able tosee their children during the day. An example of a CONCLUSIONis: Therefore, all companies with over 200 employees should pro-vide day care.

For each issue, you will be asked to evaluate the strength of eachargument. By 'strength' we mean the extent to which the conclu-sion follows from the premise. Thus, your job is to judge the extentto which the conclusion follows from the premise—NOT whetheryou think the conclusion is true or false.

REMEMBER: whether you agree or disagree with the conclusionof an argument is not the same thing as the degree to which youthink the argument is weak or strong.

Note that the instructions emphasized the importance of the distinc-tion between believing a conclusion to be true and believing an argu-ment to be strong.

Stage 1: Judgments of argument strength. After the practice session,participants proceeded to the first stage of the experiment. On each ofthe 14 trials, an argument was presented and then followed by the scaleon which participants rated the strength of arguments. Arguments werepresented in random order. Each argument was presented on two suc-cessive screens; the premise was presented first, followed by the conclu-sion. Participants controlled the amount of time each premise and con-clusion remained on the screen and were allowed as much time as theywanted to complete their ratings. Strength ratings were made on a 7-point scale ranging from very weak (1 )to very,strong (7). The completearguments, along with the rating scale, remained on the screen untilparticipants had made their ratings. Participants were not permitted toreturn to an argument after ratings had been made.

Stage 2: Thought-listing and argument generation. On completionof Stage I, participants were instructed to retrieve a packet from anenvelope at their workstation. This packet consisted of seven pages; onthe top of each was a conclusion to one of the arguments participantshad just evaluated. Whether participants received the pro or the anti

argument for a given issue was varied between participants. Participantswere given 3 min (signaled by a tone heard through the headphones) tolist the different thoughts that came to mind as they considered eachconclusion. The pages participants used to list their thoughts containeda series of lines, numbered 1-15, so as to encourage participants torespond in a list or short sentence format and to provide different argu-ments on each line. Soon after completing the thought-listing task, par-ticipants were thoroughly debriefed, thanked for their participation,and dismissed.

Results

Preliminary Analyses

It was important to verify, for each of the seven issues, thatpro and anti arguments were not differentially strong. Collaps-ing across all seven issues, pro versions of arguments (e.g., thedeath penalty should be abolished and it is appropriate to hit achild) received a mean strength rating of 3.53 {SD = 0.65), andanti versions of arguments (e.g., the death penalty should notbe abolished and it is not appropriate to hit a child) received amean rating of 3.57 (SD = 0.77). These mean ratings seemedalmost identical, an impression confirmed by a repeated mea-sures analysis of variance (ANOVA) in which issue and version(pro vs. anti) were within-subject variables. There was no maineffect of version, F(\, 51) = 0.07, ns, nor did version interactwith issue, 7^6, 306) = 1.67, ns (see Table 3). However, therewas a significant main effect of issue, F(6, 306) = 3.78, p =.001. A follow-up Tukey test indicated that the arguments con-cerning the death penalty were judged to be stronger than thoseconcerning blood alcohol levels, F{\, 306) = 15.86,/><.O1 (seeTable 3).

It was also important for present purposes to ensure that proand anti arguments were not associated with differential readingtimes. Preliminary analyses suggested that the average timeparticipants spent reading entire arguments (premise andconclusion) did not differ as a function of the version (for proarguments, M = 12.35, SD = 4.35; for anti arguments, M =11.83, SD = 4.49). An ANOVA was conducted on the variablerepresenting the average reading time per argument. In thisanalysis, issue, version (pro vs. anti), and component of argu-ment (premise vs. conclusion) were included as within-subjectvariables. There was no main effect of version, F(i,45) = 1.46,ns, and this variable did not interact with either issue, F( 1,45)= 1.26, ns, or component, F( 1, 45) = 1.02, ns. Table 3 con-tains the mean reading times included in this analysis. On thebasis of these results, all subsequent analyses were collapsedacross version and component.

Tests of the Four Predictions

Central to the disconfirmation model is the prediction thatindividuals will judge an argument to be strong to the degreethat its conclusion is compatible with their prior beliefs(Hypothesis 1). To test this prediction, we created a variable torepresent the relation between a participant's prior belief aboutan issue and the position advocated in an argument relevant tothis belief. This categorical variable, which was computed foreach of the 14 arguments, had two possible classifications: com-patible and incompatible. Only when participants could be said

10 EDWARDS AND SMITH

to have a moderately strong belief about any given issue (i.e.,those whose prior beliefs were on the extremes of the prior beliefscale [1-2 or 6-7]) did an argument receive a compatibilityscore. Thus, if a participant's prior belief rating on the issue ofabolishing the death penalty was 6 (i.e., he or she was in favor ofabolition), the pro-death-penalty argument would be classifiedas compatible, whereas the anti-death-penalty argument wouldbe incompatible. Thus, in all analyses of Stage 1 data, compati-bility was treated as a within-subject variable. Analyses wereconducted to ensure that the two groups of participants classi-fied as having opposing positions did, indeed, differ in their be-

Table 3Mean Strength Ratings (Top Panel) and Reading Times(in Milliseconds; Bottom Panels) for Each of the Issuesas a Function of Position Advocated in Argument

Pro position Anti position

Issue M SD M SD

Judged argument strength

Death penaltyHire minoritiesStrike childParental consent/abortionGay-lesbian adoptionsDeath sentence for minorsBlood alcohol level checks

Overall

4.123.503.733.853.652.922.943.53

.67

.76

.74

.88

.75

.64

.530.65

Reading time: Premises


6.185.876.057.767.648.157.03

3.782.512.934.107.708.585.03

Reading time: Conclusions


5.265.775.835.203.887.746.12

4.314.383.392.592.435.924.19

Reading time: Entire arguments


Overall

11.0911.4611.8512.8911.4615.3512.5912.35

6.365.435.025.318.90

10.876.554.35

3.583.253.563.583.643.943.463.57

8.135.686.266.996.207.115.37

5.124.916.464.894.107.726.44

12.7810.3912.4411.5510.1914.3211.4211.83

1.641.961.731.831.661.781.830.77

5.362.272.552.983.825.803.12

3.073.144.553.314.066.475.04

6.904.71S.815.167.089.676.434.49

Note. Strength ratings were made on 7-point scales (1 = very weak. 7= very strong). Reading time was calculated from the time the premise(or conclusion) was presented on the screen until the time the partici-pantadvanced the screen. Thus, these measures do not include the timeparticipants spent making their ratings.

c0)

5 • •

3 • •

2 - •

1 - -

• CompatibleH Incompatible

c01Q.

i&

u

co

o-O

<

03

Figure 2, Judged strength of compatible and incompatible arguments:Experiment 1. pen. = penalty; B.A.L. = blood alcohol level.

liefs. Results confirmed that these classifications correspondedto significantly different positions on each of the issues.5

Figure 2 depicts the mean strength judgments for the 14 ar-guments as a function of compatibility. As can be seen in Figure2, the judged strength of an incompatible argument was approx-imately half that of a compatible argument for five of the sevenissues. Needless to say, these are dramatic differences. SeparateANOYAs were conducted for each issue, with compatibility asa within-subject variable.6 As predicted (Hypothesis 1), incom-patible arguments were judged to be significantly weaker thancompatible arguments for five of the seven issues: death penalty,/•"( 1,26) = 42.97. p < .001; hitting child, F{ 1,26) = 78.l3,p <.001; hiring minorities, F( 1, 28) = 24.44, p < .001; consent forabortion, F{ 1,36) = 74.14,p < .001; and gay-lesbian adoption,F{ 1, 30) = 43.27, p < .001. The only exceptions to the patternwere the arguments concerning issues that were previouslyfound to elicit low strength ratings: death sentence for minorsand blood alcohol level checks, F( 1, 28) = 0.03, ns, and F( 1,26) = 1.37, ns, respectively. These results, then, are generally

5 Thus, the means for the issue concerning the death penalty were1.50 and 6.37 (on a 7-point scale), /(44) = -14.52, p < .001. Themeans for the issue concerning hitting a child were 1.23 and 6.79, ;(25)= —33.39, p < .001. The means for hiring minorities were 1.25 and6.53, *(27) = -28.57, p < .001. The means for consent for abortionwere 1.85 and 6.17, i(48) = -18.11, p < 001. The means for gay-lesbian adoption were 1.27 and 6.60, r(29) = -28.92, p < .001. Themeans for death sentence for minors were 1.71 and 6.45, /(27) =-21.63, p < .001. Finally, the means for blood alcohol level checks were1.53 and 6.51, ((25) = -24.71, p< .001.

6 Because the designation, compatible versus incompatible, was madeon an issue-by-issue basis, separate comparisons were conducted foreach issue rather than issue being treated as a within-subject variable ina mixed-model analysis. This holds for the present as well as all subse-quent analyses of Stage 1 data.

DISCONFIRMATION BIAS 11

consistent with the idea that people judge the strength of an ar-gument in accordance with their prior beliefs.

The second prediction of the disconfirmation model is thatindividuals should spend longer evaluating an argument that isincompatible with their prior beliefs than one that is compatible(Hypothesis 2). This is because only incompatible argumentswill lead to a time-consuming, effortful search of memory. Fig-ure 3 depicts the time participants spent reading each of the 14arguments as a function of compatibility. As can be seen in thefigure, the overall pattern is quite striking: For all seven cases,more time was spent reading incompatible than compatible ar-guments. A series of ANOVAs performed on the reading timemeasure (with compatibility as a between-subjects variable)confirmed a pattern that is consistent with that characterizingthe relationship between prior beliefs and judged strength of ar-guments. Namely, for five of the seven issues, participants tooksignificantly longer reading arguments that were incompatible,as opposed to compatible, with their prior beliefs: death penalty,F{ 1, 26) = 13.72, p = .001; hitting a child, F{1, 26) = 23.73,/>< .001; hiring minorities, F( 1,26) = 4.36, p = .047; consent forabortion, F( 1,36) = 14.64, p < .001; and gay-lesbian adoption,F{ 1, 30) = 12.88, p = .001. Note that the two issues for whichthis relationship did not hold were the same two for which therelationship between prior beliefs and judged strength of argu-ments was not significant: death sentence for minors, F( 1, 27)= 1.32, ns, and blood alcohol level checks, F( 1,26) = 0.46, ns.

The next set of analyses was concerned with the thoughts thatparticipants generated in the thought-listing task completed inStage 2. Recall that in this task, participants had been presentedwith one of two conclusions (previously presented in the argu-ment evaluation stage) for each of the issues. Their task wasto generate thoughts or arguments that came to mind as theyconsidered the presented conclusion. Thus, for all analyses ofdata obtained in this stage of the experiment, compatibility wasa between-subjects variable rather than a within-subjectvariable.

The mean number of generated thoughts across all seven is-sues was 3.87 (SD - 0.61). For each issue, an ANOVA was per-

20

S3 1 8

8 16a 14

t 12| 10

• CompatibleB Incompatible

2o

Figure S. Mean time spent reading compatible and incompatible ar-guments: Experiment 1. pen. = penalty; B.A.L. = blood alcohol level.

Figure 4. Mean number of arguments generated in response to com-patible and incompatible arguments: Experiment 1. pen. = penalty;B.A.L. = blood alcohol level.

formed on the number of generated arguments, with compati-bility as a between-subjects variable. In support of Hypothesis3, there was a main effect of compatibility such that participantsgenerated more arguments when presented with an incompati-ble conclusion than when presented with a compatible conclu-sion (see Figure 4). This difference was significant for three ofthe issues: death penalty, F(l , 26) = 6.31, p = .02: hitting achild, F{ 1, 25) = 5.49, p = .03; and consent for abortion, F{ 1,33) = 36.95, p<. 001. The difference was marginally significantfor the issue concerning hiring minorities, F( 1,26) = 3.25, p =.08. The means were also in the predicted direction for the issueconcerning gay-lesbian adoption, F( 1,27) = 2.31, p ~ .14, butwere insignificantly in the wrong direction for death sentencefor minors, F{ 1,26) = 1.96, n s, and blood alcohol level checks,F{ 1,25) = 0.34, ns. What remains an open question is whetherthere are qualitative differences in the types of arguments gen-erated by participants responding to a compatible versus an in-compatible conclusion. This question was the subject of the fol-lowing series of analyses.

The finding that people generate comparatively more argu-ments when faced with an incompatible argument, taken alone,does not establish that the processing goal of such individualswas to undermine the argument. More conclusive evidence forthis idea would exist if the difference in the number of argu-ments generated were accompanied by a corresponding differ-ence in the number of refutational (as opposed to supportive)arguments generated. That is, individuals responding to incom-patible positions (relative to those responding to compatiblepositions) should generate more arguments overall, as well as agreater number of refutational arguments (Hypothesis 4). Toexamine this hypothesis, we transcribed all generated argu-ments from the questionnaire packets to a single document con-taining neither participants' identification numbers nor any in-formation pertaining to their prior beliefs. Two independentjudges then scored each of the generated thoughts as to whether


9

2

Death Pen.Hit Child

Abortion Adoption

Minorities

Type ofargumentgenerated

• RefutationalB Supportive

Under 16B.A.L.

Figure 5. Mean number of supportive and refutational arguments generated in response to compatible(C) and incompatible (I) arguments: Experiment I. Pen. = penalty: B.A.L. = blood alcohol level.

it was supportive, refutational, or ambiguous in relation to theposition advocated in the corresponding conclusion presented.Generated arguments that were scored as ambiguous (4.7%)were omitted from these analyses. Interrater agreement on thistask was high (K = .97).

Figure 5 illustrates the number of supportive and refutationalarguments generated as a function of compatibility. Inspectionof this figure reveals a consistent pattern across issues, such thatin a majority of cases when participants were responding to anincompatible position, they generated substantially more refu-tational arguments than supportive arguments. In fact, in threesuch cases, there were two times as many refutational argu-ments, and, in two cases, there were more than three times asmany refutational arguments. There was also a tendency formore supportive arguments to be generated when participantswere responding to compatible positions, but the magnitude ofthe difference between the number of refutational and support-ive arguments was much smaller for compatible than for incom-patible arguments.

For each issue, a mixed-model ANOVA was performed on thevariable representing the number of arguments generated, withcompatibility as a between-subjects variable and type of gener-ated argument (supportive vs. refutational) as a within-subjectvariable. Results of these analyses provided strong support forthe idea that the nature of the arguments generated depends onwhether the position participants are presented with is compat-ible or incompatible with their prior beliefs. The exceptions tothis pattern were, once again, restricted to two issues: death sen-tence for minors and blood alcohol level checks. Thus, for fourof the issues, there was a main effect of compatibility, as dis-cussed in relation to Hypothesis 3, and a main effect of argu-ment type, such that a greater number of refutational argu-ments were generated overall: death penalty, F( 1,25) = 10.32,p = .004; hitting a child, F( 1, 24) = 6.55, p = .017; abortion,

F( 1, 33) = 15.20, p < .001; and gay-lesbian adoption, F( 1,27)= 10.11, p- .004. However, these main effects were qualified ineach case by the presence of the predicted interaction betweenargument type and compatibility: death penalty, F(l, 25) =65.17, p< .001; hitting a child, F(l ,24) = 70.60, p < ,001;abortion, F( 1, 33) = 90.70, p < .001; and gay-lesbian adoption,F( 1, 27) = 45.02, p < .001. This interaction also emerged for afifth issue, hiring minorities, F( 1,26) = 34.32, p < .001. Simpleeffects analyses indicated that, for these five issues, significantlymore refutational arguments were generated by participants forwhom a given position was incompatible than by participantsfor whom this position was compatible: death penalty. F( 1,25)= 66.12, p < .001; hitting a child, F( 1, 24) = 48.81, p < .001;hiring minorities, F(l, 26) = 29.64, p < .001; abortion. F(l,33) = 98.52, p < .001; and gay-lesbian adoption, F( 1, 27) =47.27,/x.OOl.

Exploring Further the Nature of the Prior Belief EffectFor sake of clarity, only those participants who, for a given

issue, could be said to have a prior belief on one of the twoextremes were included in analyses. In so doing, we treatedcompatibility in categorical terms. It is possible, however, thatthe observed dependence between prior beliefs and argumentevaluation is continuous in nature, such that the magnitude ofthe bias is a continuous function of the extremity of prior be-liefs. In terms of the disconfirmation model, the more incom-patible a given argument is perceived to be, the more likely theperson will be to engage in attempts to undermine the argu-ment. To examine the tenability of this account of the priorbelief effect in our data, we conducted follow-up correlationalanalyses in which we included all participants (i.e., not onlythose who had relatively extreme views). Specifically, we exam-ined the correlations between prior beliefs on the one hand andjudgments of argument strength and reading time on the other.

DISCONF1RMATION BIAS 13

Recall that, in the pretest, prior beliefs toward the pro versionof each argument were assessed (e.g., the death penalty shouldbe abolished and it is appropriate to hit a child). Thus, evidencefor the prior belief effect would be indicated by positive corre-lations for the argu ments articulating pro positions and negativecorrelations for the arguments articulating anti positions. Thatis, the more positive a participant's prior belief (i.e., the more aparticipant is in favor of the pro position of a given issue), thestronger this participant will judge the pro argument to be, andthe weaker this participant will judge the anti argument to be.Similarly, the more positive a participant's prior belief, the lesstime this participant will spend reading the pro argument, andthe longer this participant will spend reading the anti argument.The data were, by and large, consistent with this pattern.

As can be seen in Table 4, there was a positive correlationbetween prior beliefs and judged strength for each of the 7 proarguments and a negative correlation for each of the 7 anti ar-guments. For the 5 issues for which belief biases emerged inprevious analyses, these correlations attained significance. Withregard to the reading time measure, the overall pattern was sim-ilar, although less consistent. Thus, the correlation betweenprior beliefs and reading time was in the predicted direction for9 of the 14 arguments. These correlations attained statisticalsignificance for the pro arguments concerning the hitting chil-dren and abortion issues and for the anti arguments concerningthe death penalty, hitting children, and hiring minorities issues.Of course, this pattern of correlations between prior belief andeach of the dependent measures could have been due solely tothe performance of the participants with extreme beliefs. To ex-tend the findings unequivocally to participants with moderateviews, it was necessary to determine whether similar lineartrends would be found among participants with moderate viewswhen these participants were examined independently. The re-sults of such analyses indicated that the correlations betweenprior belief and judged strength were in the predicted directionfor all of the 14 arguments. The correlation was positive, as pre-dicted, for all of the compatible arguments (range = . 10 to .58),and significantly so for two of them: death penalty (r = .46, p<.O5), and hit child (r = .58, p < .05). The correlation wasnegative, as predicted, for all of the incompatible arguments(range = —.10 to -.72), and significantly so for two of them:

Table 4Correlations of Prior Beliefs With the Measures of ArgumentStrength and Reading Time Depicted Separatelyfor Pro and Anti Arguments

Issue

Death penaltyStrike childHire minoritiesParental consent/abortionGay-lesbian adoptionsDeath sentence for minorsBlood alcohol level checks

Judged strength

Pro

.73

.78**

.38**

.77**

.66**

.11

.16

Anti

-.71**-.67**-.63**-.72**-.68**-.07-.16

Reading time

Pro

.26

.32*-.16

.38**

.20

.08

.08

Anti

-.39**-.31*-.36**-.15-.11

.02

.00

*/><.05. **/?<.001.

death penalty: (r = . 51, p < .05), and abortion (r - - . 72, p <.01). These results are important because they establish that,for judgments of argument strength, the prior belief effect holdsnot only for participants with extreme initial views but even forthose with moderate views on one or another side of an issue.This is an important extension of the findings reported by Lordet a). (1979), whose sample included only participants who ex-pressed extreme views on the death penalty.

Additional A nalyses

In scoring the protocols, we noticed that participants occa-sionally listed a particular argument more than once, articulat-ing it somewhat differently on two or more occasions. The pres-ence of such "redundant arguments" is noteworthy becauseparticipants had been instructed explicitly to provide distinctarguments on the response form. Consider the following il-lustrations. In response to the statement "Gay and lesbian cou-ples should not be allowed to adopt children," one participantincluded the following arguments refuting the position advo-cated: "These people can provide as much care as heterosexualcouples" and "These people can provide as much monitoringand monetary support as heterosexuals." This pair of argu-ments instantiates the belief that people who are homosexualdo not necessarily lack the ability to be good caregivers. Con-sidering the range of themes that participants mentioned in thethought-listing task, arguments such as these stand out as re-markably similar. Hereafter, we refer to thematically distinctclasses of arguments as types and to the instances of these argu-ment types as tokens. (Types are abstractions; only tokens canappear in a protocol.)

The question of interest is whether the generation of multipletokens of a type of argument is more common when individualsevaluate incompatible arguments than when they evaluate com-patible arguments. This idea seems reasonable given our as-sumption that people confronted with incompatible argumentswill engage in a deliberative search to undermine them. The firststep in our examination of this issue was to code the generatedarguments according to theme. Two judges, who were notknowledgeable about the experimental hypotheses or partici-pants' prior beliefs, established a coding scheme whereby gen-erated arguments could be classified into a number of themati-cally distinct categories. Each judge independently reviewed allarguments and then determined a set of category descriptorsto represent the range of themes referred to in the generatedarguments. To qualify as a category, an issue had to have beenreferred to by at least 3 participants. Arguments that could notbe so classified (6.5%) were assigned to a "miscellaneous" cat-egory and were omitted from further analyses. The second stepin this analysis was for the coders to consolidate their separatecoding schemes and resolve through discussion any differencesof opinion about the appropriate categories. The third step en-tailed having a different pair of judges use this coding scheme toassign each generated argument to a category (K = .91).

Figure 6 depicts the mean number of redundant arguments(the sum of all tokens collapsed across types of arguments) gen-erated as a function of compatibility for each of the seven issues.For each issue, an ANOVA was performed on the variable rep-resenting the number of generated arguments. In these analyses,


Figure 6. Number of redundant arguments generated in response tocompatible and incompatible presented arguments: Experiment 1.

compatibility was a between-subjects variable. For three of theseven issues, analyses yielded a significant main effect of type ofpresented argument, such that more redundant arguments weregenerated in response to incompatible (as compared withcompatible) presented arguments: hitting a child, F(1, 24) =3.68, p = .06; consent for abortion, /"(1, 33) = 3.36, p = .07;and gay-lesbian adoption, F( 1, 27) = 4.03, p = .06. This pat-tern did not emerge for death penalty, F( 1,24) = 0.93, ns\ hir-ing minorities, F( I, 26) = 0.35, nj ; death sentence for minors,F( 1, 26) = 0.03, ns; or blood alcohol level checks, F( 1, 25) =2.06, ns.

Discussion

The results of the first experiment provided strong supportfor the four main hypotheses concerning the relationship be-tween prior beliefs and the evaluation of arguments. Findingsindicated that, when presented with an argument that is incom-patible (as compared with one that is compatible) with theirprior beliefs, individuals (a) judge the argument to be weaker,(b) spend longer scrutinizing the argument, (c) generate agreater number of relevant thoughts and arguments in athought-listing task, and (d) generate a greater number of refu-tational than supportive arguments in the thought-listing task.

These findings are particularly striking considering that priorbeliefs were assessed 4-6 weeks before the actual experiment.This suggests that the pattern of results reflects relatively stableand enduring properties of prior beliefs. The findings are alsonoteworthy because they emerged consistently for five differentissues. Moreover, for each issue, participants evaluated both acompatible and an incompatible argument. The latter consider-ations permit us to rule out the possibility that the biases re-vealed are attributable to factors that covary with a particularstance (i.e., either proponent or opponent) or, more generally,factors that are specific to a particular issue.

Some comments might be offered as to why the primary

hypotheses were not supported for two of the seven issues: deathpenalty for convicted murderers less than 16 years of age andrandom checks of drivers' blood alcohol levels. It may be thatthese issues are comparatively less familiar than the others ex-amined in this study, at least to the undergraduate participantsin our sample. Unlike the other five issues included in this study,which are frequently topics of news coverage, political dis-course, and legislation, as well as public discussions and debate,these are "lower profile" issues with more specific referents.Consequently, because of their comparative lack of familiarityand relevant knowledge about these issues, participants may nothave been as motivated to undermine arguments that rancounter to their prior beliefs. Several findings are consistentwith this interpretation of why these issues behaved differentlythan the others. As can be seen in Table 1, these issues receivedthe lowest scores on the measure of prior knowledge and amongthe three lowest scores on the measure of emotional conviction.

A major objective in Experiment 2 was to examine the roleof emotion in the prior belief effect and the other phenomenarevealed in Experiment 1, Intuitively, a person who holds hisor her belief with strong emotional conviction should be moresensitive to challenges to this belief and may even defend againstsuch challenges differently than would a person who has anequally strong but less passionately held belief about this sameissue. Lord et al. (1979) considered only cognitive factors intheir analysis, as did Nisbett and Ross (1980) in their influentialdiscussion of this line of research. However, consideration of thefindings reported by Kunda (1990) and Ditto and Lopez(1992) suggests that affective factors have not been given theirdue.

Kunda (1990) had participants evaluate arguments about atopic of which they presumably had little prior knowledge (therelation of caffeine consumption to fibrocystic disease). Shevaried whether the participants had reason to be negatively in-vested in the conclusion by explaining that the disease was sup-posed to occur only in women. Kunda found that female par-ticipants, who had reason to be negatively invested in the con-clusion, considered the argument less strong than did maleparticipants who had no such reason; presumably, a negativeinvestment is associated with negative affect. In a similar vein,Ditto and Lopez (1992) varied whether participants had reasonto believe that they had an enzyme deficiency that is(ostensibly) known to make people more susceptible to pancre-atic disorders. Those who believed themselves to be deficient inthis enzyme were more likely to challenge the accuracy of thetest used to make diagnoses than were those who believed them-selves not to be enzyme deficient; presumably, believing thatone is susceptible to the disease is associated with negativeaffect. Because neither Kunda nor Ditto and Lopez includeddirect measures of participants' affective responses to the unde-sirable information, it is not possible to establish with certaintythat affective responses mediate the relationship between thedesirability of a conclusion and assessments of the validity ofinformation supporting this conclusion, although this possibil-ity seems quite plausible.

To examine the role of emotional conviction in argument dis-confirmation processes, it was important to remedy a problemin Experiment 1, Recall that for all seven issues, there was a

DISCONHRMATION BIAS 15

strong positive correlation between participants' estimates oftheir knowledge about an issue and the degree of their emo-tional conviction (see Table 2). In this light, it is difficult todisentangle the effects attributable to knowledge from those at-tributable to emotional conviction. Furthermore, there is rea-son to suspect that self-ratings of people's knowledge about anissue are not good predictors of their actual knowledge (see,e.g., Gerrard & Warner, 1991). In view of these two issues,efforts were made in Experiment 2 to control the amount ofobjective knowledge participants had about an issue while al-lowing their prior beliefs and emotional conviction to vary.

A second objective of Experiment 2 was to further explore thefinding that, in the thought-listing task, participants generatedmultiple tokens of a type, a tendency that was more pronouncedwhen they were evaluating incompatible arguments. This find-ing is especially intriguing because participants had receivedspecific instructions to list distinct thoughts in this task. Of par-ticular concern is whether the tendency to generate tokens isespecially great for individuals with high (as opposed to low)emotional conviction. One line of evidence suggests that thismight be the case. Edwards (1992) found that attitudes basedprimarily on emotion are significantly less differentiated andless complex in their cognitive structures than are attitudesbased primarily on cognition. These findings were interpretedas indicating that, relative to cognition-based attitudes, affect-based attitudes are supported by fewer distinct units of infor-mation, knowledge, or beliefs. These findings have importantimplications for understanding why individuals with strongemotional conviction would be most likely to generate redun-dant arguments. The logic of this suggestion derives from thefollowing assumptions.

1. Individuals with strong prior beliefs react to the presenta-tion of an incompatible argument by a deliberate search ofmemory for relevant material, which they use to refute the po-sition advocated (as predicted by the disconfirmation modeland supported by the data of Experiment 1).

2. Individuals high in emotional conviction are more likely tosearch for material to refute an incompatible argument than arethose low in emotional conviction.

3. Individuals with strong emotional conviction may have amore limited repertoire of knowledge and facts that can be usedfor purposes of refutation (as suggested by Edwards's, 1992,findings).

4. Individuals rely on a "more is better" heuristic, such thatthe amount of refutational material generated is viewed as anindicator of (and commensurate with) the success of the effortsto undermine the strength of an argument (e.g., see Petty &Cacioppo, 1981, 1986a).

Overview of Experiment 2

The major objectives of Experiment 2 were to examine therole of emotional conviction in the evaluation of arguments andto determine how such conviction modulates the phenomenarevealed in Experiment 1. In this study, we examined only oneissue—capital punishment—and provided participants withonly one argument about this issue (the anti-death-penalty ar-gument used in Experiment 1). Thus, in this study, version (proor anti) was not a variable in the design. Included in this study

were participants who indicated that they were clearly for oragainst the abolition of the death penalty. Among this subset,individuals were selected on the basis of their scores on a test offactual knowledge about the death penalty. By selecting onlythose proponents and opponents of the death penalty whosescores on this test fell within a specified range, we were able tocontrol for amount of knowledge concerning the death penaltywhile allowing the degree of participants1 emotional convictionto vary. This experiment took the form of a 2 X 2 factorialdesign; compatibility with prior belief (compatible vs.incompatible) and emotional conviction (low vs. high) were be-tween-subjects variables.

Again, the study was conducted in two stages. In Stage 1, par-ticipants completed a pretest survey designed to assess theirprior beliefs about the death penalty, the degree of emotionalconviction with which they held these beliefs, and the extent oftheir knowledge about the death penalty. In Stage 2, selectedparticipants completed the thought-listing task used in Experi-ment 1. The hypotheses guiding this study were as follows.

Hypothesis 5: Compatible arguments will be judged strongerthan incompatible arguments (replication of Experiment I),especially for individuals whose beliefs are associated withstrong emotional conviction.

Hypothesis 6: People will generate more arguments when thepresented argument is incompatible than when it is compatible(replication of Experiment 1), particularly when prior beliefsare associated with strong emotional conviction.

Hypothesis 7: People will generate more refutational thoughtswhen a presented argument is incompatible (as opposed tocompatible) with their beliefs (replication of Experiment 1),particularly when prior beliefs are associated with strong emo-tional conviction.

Hypothesis 8: When a person high in emotional convictionabout an issue is presented with an incompatible argument, heor she will be likely to generate more tokens per type of refuta-tional argument than a person with an equally extreme priorbelief who is low in emotional conviction.

Pretest

Participants were 212 Brown University undergraduates whohad been recruited to complete a survey on attitudes and beliefsabout capital punishment. They completed the survey ingroups of 4-8, although they worked individually in cubiclesseparated from the main room of the laboratory by doors.

The experimenter explained that the goal of the survey wasto learn what undergraduates think and know about the deathpenalty and emphasized that participants' responses would bekept confidential. To make it possible for us to contact studentslater, we asked them to write their initials and a phone numberif they would be willing to return for a subsequent study.

The survey contained 14 questions pertaining to the evidencesupporting (or refuting) the merit of the death penalty. Theitems assessed participants' knowledge about issues such as thedeterrent efficacy of the death penalty, the relative costs associ-ated with the death penalty versus life imprisonment, and thepossibility of reforming violent criminals. The questionnairewas modeled after one developed by Ellsworth and Ross (1976).Participants' prior beliefs were assessed in the same manner as


they had been in Experiment I. On completion of the question-naire, participants were thanked for their participation, de-briefed, and dismissed.

Experiment 2

Method

Participants

Included in the study proper were individuals who would be consid-ered neither novices nor experts in terms of their knowledge about thedeath penalty (their scores were within one standard deviation of themean). Individuals from this sample were contacted by telephone 45-60 days after the pretest and asked to participate in an experimentscheduled approximately 1 -2 weeks from that time. They were not toldexplicitly that the study was related to the one they had participated inseveral weeks earlier, but no deliberate attempts were made to hide thisfact. The subset of individuals who indicated a willingness to participatein the experiment and who could be classified as having either a pro-death-penalty or anti-death-penalty position were divided into twogroups corresponding to their positions on the issue. Fifty individualsfrom each group were randomly selected to participate in the experi-ment. The final sample consisted of 85 Brown University undergradu-ates who were paid $3 for a 20-min session.7 In this sample, 46 partici-pants were opposed to the death penalty and 39 were in favor. The de-gree of emotion that people reported to be associated with their beliefsranged from none at all (-3) to a great deal (3). The students partici-pated in groups of 5-7, although they completed the study in individualcubicles separated from the main room by a door, as in Stage 1.

Materials and Apparatus

The instructions, presented argument, rating scale, and thought-list-ing task were presented to participants on successive pages of a packet.Because this study was not conducted on computers, reading times werenot assessed. As described earlier, only one argument was evaluated: theanti-death-penalty argument used in Experiment 1.

Procedure

The experimenter explained that the purpose of the study was to learnmore about how people evaluate the strength of arguments. Participantswere told that they would be reading a brief argument concerning thedeath penalty and that their first task would be to judge how strong theyviewed the argument to be. Strength ratings were made on a 7-pointscale ranging from very weak {-?>) to very strong (3). Once again, par-ticipants were urged to bear in mind the difference between thinkingthat an argument is strong and agreeing with its conclusion and to basetheir ratings on only the former. They were told that they would alsocomplete another task that involved more specific reactions they mighthave to the argument and were assured that the instructions for this taskwould be provided in detail in the study packet. The thought-listinginstructions and task itself were identical to those of Experiment I.When participants had completed this task, they were debriefed,thanked for their participation, and dismissed.

Results

ipants had been chosen in part on the basis of the fact that theyexpressed a relatively extreme position on the death penalty.Comparisons indicated that the groups did differ significantlyin their positions on the death penalty {M = -2.39 vs. M -2.33), ((83) = -44.64, p < .001. Next, participants were di-vided into two groups (low vs. high emotional conviction)based on a median split of their ratings of their feelings towardthe issue of capital punishment (scores on this measure rangedfrom - 3 to 3). These designations resulted in groups with sig-nificantly different degrees of emotional conviction (M = -2.03vs. M = 2.20), /(73) = -24.29, p < .001. Note, however, thatthe levels of emotional conviction for pro-death-penalty partic-ipants (/? = 40) and anti-death-penalty participants (n - 35)were equivalent (M = 0.15, SD = 2.11 vs. M = 0.26, SD =2.15), f(83) = 0.23, ns. Analyses also established that extrem-ity of prior beliefs and degree of emotional conviction werenot correlated for either the anti-death-penalty participantsor the pro-death-penalty participants (rs = -.09 and .14,respectively).

Tests of Hypotheses

An ANOVA was performed on the variable representingjudged strength of the argument, with emotional conviction andcompatibility as between-subjects variables. As in Experiment1, incompatible arguments were judged to be significantlyweaker than compatible arguments, F( 1,71) - 96.39, p < .001(see Figure 7).8 No main effect for emotional convictionemerged, although there was a marginally significant interac-tion between compatibility and emotional conviction, F( 1,71)= 3.61, p - .07. Simple effects analyses indicated that, in accor-dance with Hypothesis 5, incompatible arguments were judgedto be especially weak by people high in emotional conviction(as compared with those low in emotional conviction), F(l,71) = 4.94,p<.05.

Preliminary Analyses

A preliminary analysis was conducted to ensure that the pro-death-penalty and anti-death-penalty groups differed in termsof their beliefs about the death penalty. Recall that these partic-

7 A total of 15 participants were not run in the study because theyfailed to show up for their scheduled session (n = 8), because theychanged their minds about wanting to participate (« = 5), or becausethey appeared to be aware of the relation between the prescreening ques-tionnaire and the study proper (n = 2).

8 Note that, in Experiment 2, we examined only half the design ofExperiment 1, in that we assessed judgments of argument strength andreading time only for the anti-death-penalty argument rather than forthe arguments representing both sides of the issue, as in Experiment I.To address the possibility that pro-death-penalty participants perceiveall arguments (not just incompatible arguments) to be weaker than doanti-death-penalty participants, we conducted a series of mixed-modelANOVAs for each of the issues in Experiment 1; prior belief (pro vs.anti) was the between-subjects variable, and position advocated (provs. anti) was the within-subject variable. In all but one of the analysesconducted on the argument strength ratings, no main effect of beliefemerged (for death penalty, hit child, abortion, and gay-lesbian adop-tion, all F$ < 1, ns). The same analyses conducted on the measure ofreading time yielded a similar pattern. In all but one of the analyses, nomain effect of belief emerged (for death penalty, hit child, abortion, andgay-lesbian adoption, all fs < 1.80, p > . 19). The only exception to thispattern was for the issue of hiring minorities. These findings rule out thepossibility that a confounding between prior belief and compatibilitybetween pro-death-penalty and anti-death-penalty participants couldexplain the results of Experiment 2.


Compatible

Incompatible

Low High

Emotional conviction

Figure 7. Judged strength of compatible and incompatible argumentsas a function of emotional conviction: Experiment 2.

The next series of analyses was aimed at testing the predictionthat the interaction of compatibility and emotional convictionis related to both the number and the types of arguments gener-ated. Using the coding scheme established in the previous study,two independent judges scored each argument as to whether itwas refutational or supportive of the anti-death-penalty posi-tion advocated (K - .92) and what type it was a token of, ifany (K - .97). A mixed-model ANOVA was conducted on thevariable representing the total number of thoughts generated,with compatibility and level of emotional conviction as be-tween-subjects variables and type of generated argument(supportive vs. refutational) as a within-subject variable. Re-sults of this analysis yielded a significant main effect for com-patibility, such that more arguments overall were generatedwhen participants were considering an incompatible argumentthan when they were considering a compatible argument (M -5.21 vs.M= 3.45),F( 1,71)= 37.18,p<.001 (seeFigure 8).This finding replicates the general pattern obtained in Experi-ment 1. Also replicating the pattern obtained in Experiment I,there was a significant interaction between compatibility andargument type, F{ 1,71)= 157.86, p < .001, such that partici-pants evaluating incompatible arguments generated more refu-tational than supportive arguments (M = 4.02 vs. M = 2.62),F( 1, 71) = 158.05, p < .001, whereas participants evaluatingcompatible arguments generated more supportive than refuta-tional arguments (M - 2.62 vs. M = 1.11), F( 1, 71) = 37.15,p < .001. As before, the tendency to generate arguments consis-tent with prior beliefs (refutational arguments in the case ofincompatible positions and supportive arguments in the case ofcompatible positions) was significantly greater for participantswho were evaluating incompatible positions.

In accordance with Hypothesis 6, there was a significant in-teraction between emotional conviction and argument type,F{\, 71) = 8.66, p = .004. Specifically, participants high inemotional conviction generated more arguments than did par-

ticipants low in emotional conviction when evaluating an in-compatible argument, F( 1,40)= 10.01, p = .003. Finally, therewas a significant Compatibility X Emotional Conviction X Ar-gument Type interaction, F( 1,71) = 11.34, p = .001. To exam-ine the nature of this interaction, we conducted separate analy-ses for participants evaluating compatible and incompatible ar-guments. As can be seen in Figure 8, among participants whoevaluated an incompatible argument, those high in emotionalconviction generated more refutational arguments than didthose low in emotional conviction. Simple effects analyses con-firmed that this difference was significant, F( 1, 40) = 26.65, p< .001. A comparable pattern did not emerge for participants

4 -

3 -

2 -

1 -

Refutational

Supportive

Low High

Emotional Conviction

Low High

Emotional Conviction

Figure 8, Mean number of supportive and refutational argumentsgenerated as a function of compatibility and emotional conviction: Ex-periment 2. Compatible argument is depicted in top panel; incompati-ble argument is depicted in bottom panel.


evaluating a compatible argument (i.e., participants high inemotional conviction did not generate more supportive argu-men ts than participants low i n emotional convicti on), F{ 1,31)< 1, us. These findings are consistent with Hypothesis 7.

Conducting a type-token analysis such as that we conductedin Experiment 1 would allow us to test the idea that people'stendency to repeat the same thoughts when trying to underminean incompatible argument is especially pronounced when theyhave high, as opposed to low, emotional conviction (Hypothesis8). An ANOVA was performed on the variable representing thenumber of redundant arguments generated, with compatibilityand emotional conviction as between-subjects variables. Moreredundant arguments were generated when the argument wasincompatible than compatible with people's prior beliefs, F( 1,71)= 17.10,/?< .001. The analysis also revealed a main effectfor emotional conviction, such that more redundant argumentswere generated in cases in which people expressed emotionalconviction in their beliefs, F(l , 71) = 20.93, p < .001. Mostimportant, however, was the interaction that emerged betweenthese two variables, F{ I, 71) - 32.95, p < .001. As can be seenin Figure 9, the pattern of means suggests that the two maineffects were attributable to the presence of this interaction. Sim-ple effects analyses indicated that the differences among the cellmeans were due to the cell represented by strong emotional con-viction and incompatibility. Thus, within the incompatibilitycondition, more tokens were generated by participants high inemotional conviction than by participants low in emotionalconviction, F{ 1,71) = 56.08, p< .001.

Discussion

In Experiment 2, as in Experiment 1, participants judged in-compatible arguments to be significantly weaker than compati-ble arguments. Moreover, participants generated more thoughts

2.0

1.5-

1.0-

Compatible

Incompatible

Low High

Emotional conviction

Figure 9. Number of redundant arguments (args.) generated in re-sponse to compatible and incompatible arguments as a function of emo-tional conviction: Experiment 2.

overall, and more refutational thoughts in particular, when eval-uating incompatible arguments. These findings indicate thatpeople are unable to judge the strength ofan argument indepen-dently of their prior belief in the conclusion and that, more gen-erally, they reveal a bias to disconfirm arguments incompatiblewith their own views. Experiment 2 provided evidence that thisbias is accentuated when prior beliefs are associated with emo-tional conviction. In addition, results indicated that partici-pants who were evaluating an incompatible argument generatedmore redundant refutational arguments than participants withequally extreme prior beliefs who had less emotionalconviction.

Taken together, the two studies reported here rule out the pos-sibility that the prior belief effect is due to differing levels ofknowledge about an issue. In Experiment 1, prior beliefs werenot systematically related to the amount of self-reported priorknowledge a person reported having about an issue, and, in Ex-periment 2, the amount of domain-relevant knowledge pos-sessed by participants was controlled. More generally, the re-sults of these studies cast doubt on the tenability of differentialstorage accounts of prior belief effects in argument evaluation.The finding, in both experiments, that participants evaluatingan incompatible conclusion generated more material overallthan did participants evaluating compatible conclusions cannotbe explained by a differential storage account, according towhich the same amount of material should be retrieved regard-less of the relationship of the position advocated to a person'sprior beliefs. Our data are also inconsistent with predictionsfrom Kunda's (1990) model of motivated reasoning, whichholds that participants should spend equally long scrutinizingincompatible and compatible evidence and should generate anequal amount of material for each.

General Discussion

In proposing the disconfirmation model as an account of theprior belief effect, we have suggested that whether a personagrees or disagrees with a position advocated determines theextent to which he or she will scrutinize an argument as wellas the strategies he or she will use in doing so. That is, whenconfronted with an incompatible argument to evaluate, peoplewill engage in a deliberative search of memory in an attemptto retrieve material for use in refuting the position advocated.Because most of the retrieved material will be refutational innature, there will be a bias to judge the argument as weak. Onthe other hand, when confronted with a compatible argument,people will allocate fewer processing resources to its scrutinyand will be more inclined to accept the argument at face valueor judge it to be strong, or both. This account receives strongsupport from the findings of both studies. First, participantsspent considerably longer scrutinizing arguments that rancounter to their prior beliefs than those that were compatiblewith these beliefs. Second, when asked to report what they werethinking about while evaluating each argument, participantsgenerated more output when the argument was incompatiblethan when it was compatible with prior beliefs. Third, relativeto participants evaluating compatible arguments, those evalu-ating incompatible arguments generated more material thatwas refutational (as opposed to supportive) in nature.


It is important to note that the discontinuation model doesnot take a position on the nature of the motivational pressuresthat give rise to or promote efforts to defend against challengesto one's beliefs. What is important in this model is that individ-uals are motivated to defend their beliefs, not why they are. Themotivation to defend beliefs could arise from any of several pre-viously identified sources and still lead to the same set of pro-cesses we have described. By focusing on the nature of themechanisms that underlie argument discontinuation processesonce they have been engaged, rather than on antecedent condi-tions, the model is general enough to encompass a range of mo-tivational antecedents, including the need for consistency(Abelson et al , 1968; Festinger, 1957; Heider, 1946), the goalof accuracy (e.g., Kruglanski, Freund, & Shpitzajzen, 1985;Pittman & D'Agostino, 1985; Simon, 1957), the desire to pro-tect self-esteem (e.g., Greenwald, 1980; Kunda, 1987; Py-szczynski, Greenberg, & Holt 1985), and the wish to maintainexisting cognitive closure (Kruglanski, Peri, & Zakai, 1991;Webster & Kruglanski, 1994). A potentially interesting venuefor future research would be to examine whether any of theargument disconfirmation processes we have identified are par-ticularly associated with specific motivational instigations.

Alternative Accounts

We have conceptualized the just-mentioned differences as in-dications that participants are expending more effort to refuteincompatible arguments than to bolster compatible arguments.Nonetheless, there are at least two possible alternative explana-tions for the findings we have reported. First, it may be easier toshow that an argument is right than to show that it is wrong;perhaps participants feel compelled to offer more justificationfor rejecting than for accepting an argument. This possibilitycould explain why participants generate more arguments in as-sociation with incompatible as compared with compatible ar-guments (and spend longer doing so). However, there are severalconsiderations that diminish the viability of this alternative ex-planation. Consider, first, that participants were instructed toevaluate the strength of arguments, not to accept or reject argu-ments. In fact, the instructions emphasized the difference be-tween believing an argument to be strong and agreeing with itsconclusion. Moreover, participants explicitly were told to keepthis distinction in mind when evaluating the strength of argu-ments. In addition, in both experiments, participants wereasked to make their judgments of argument strength before thethought-listing stage of the experiment. Thus, it seems unlikelythat the differences in reading time or judged strength of argu-ments are attributable to the fact that participants either ex-pected or felt compelled to justify their ratings.

The second alternative is that the processing time differencesobserved in our studies emerged because arguments supportingone's beliefs are more familiar and, therefore, are processedmore easily and more quickly than arguments that oppose one'sbeliefs. This account, however, seems inadequate for three rea-sons. First, one can only speculate about the possibility thatcompatible arguments in these studies were more familiar toparticipants than incompatible arguments, because there wasno independent assessment of participants' familiarity with thearguments. Second, several precautions were taken in an effort

to reduce the possibility that there would be significant discrep-ancies in the degree to which pro and anti participants werefamiliar with arguments pertaining to the issues, including se-lecting arguments that were easy to understand and familiar toparticipants and ensuring that participants were equally knowl-edgeable about the issue being evaluated (Experiment 2),Third, even if it could be established that differential familiaritycontributed to the reading time effects observed in these studies,it is clearly not the only (or the most) important determinant.The reliable correlations obtained between measures of readingtime and the number of thoughts generated suggest that, famil-iar or not, belief-incompatible arguments are associated withgreater cognitive scrutiny and counterargumentation. Note thatboth of the preceding alternative explanations are further un-dermined by the results of Experiment 2, which indicate thatthe differences in reading time and ratings of argument strengthwere most pronounced for participants with greater emotionalconviction, suggesting that the asymmetries observed in theprocessing of compatible and incompatible arguments stem, atleast in part, from emotional factors.

Role of Emotion in Argument Disconfirmation Processes

The results of Experiment 2 indicate that whether a person'sprior belief is accompanied by emotional conviction affects themagnitude of the disconfirmation bias, as well as the form ofthis bias. With regard to the latter, the tendency to producemultiple tokens of a type in the process of refuting the positionof an incompatible argument was found to be more pronouncedfor participants with high, as opposed to low, emotional convic-tion. This finding can be interpreted in two ways. One possibil-ity is that emotional conviction serves to trigger the memorysearch that a person engages in when presented with an incom-patible argument. Thus, the more emotional conviction associ-ated with a belief, the more likely one will engage in a delibera-tive memory search that can undermine an incompatible argu-ment. If one can assume that the degree of emotional convictionis not always commensurate with the amount of relevant knowl-edge a person has about an issue, one would expect that, in somecases, a person's motivation to counterargue will exhaust theinformation that can be retrieved to refute the position advo-cated, a state of affairs that would probably result in the gener-ation of multiple tokens per type. A second possibility is that aperson high in emotional conviction will become emotionallyaroused when presented with an incompatible argument (for arelated idea, see Pyszczynski & Greenberg, 1989). Whenaroused, the person may be distracted by the emotional experi-ence (e.g., Mandler, 1975) and may not recognize that he or sheis generating redundant arguments. A variation on this theme isthat arousal enhances the likelihood of dominant responses(e.g., Zajonc, 1965), which would lead to the kind of repetitiveor stereotyped behavior that is apparent in the generation ofmultiple tokens of an argument type. The data obtained fromour studies do not allow us to distinguish between these twosets of possibilities, one of which hinges on the idea that beliefsassociated with strong emotional conviction are supported byfewer distinct units of information, knowledge, or beliefs (e.g.,see Edwards, 1992) and the other of which hinges on the ideathat the process of defending beliefs associated with strong emo-


tional conviction is characterized by heightened arousal. Futureresearch might profitably be directed toward resolving this in-teresting issue.

Methodological Issues and Contributions

As stated at the outset of this article, the present researchbegan with a consideration of the seminal work by Lord et al.(1979). The results of the studies reported here add to an un-derstanding of the mechanisms underlying belief-driven biasesin the evaluation of evidence. In addition, the work reportedhere improves on the methodological approach adopted byLord et al. in several ways. First, Lord et al.'s findings were basedon a somewhat narrow and specialized methodology. For exam-ple, only beliefs about one issue (the death penalty) were stud-ied, thereby making it difficult to establish the generality ofLord et al.'s findings- Another concern is that Lord et al.'s par-ticipants were presented not only compatible and incompatiblearguments but also criticisms of these arguments, along withrebuttals to the criticisms. This procedure is quite unlike thegive and take of everyday argumentation in which one's criti-cisms of arguments must be actively retrieved from memory orgenerated on the spot. Furthermore, this procedure may haveinclined participants to generate counterarguments when, leftto their own devices, they may not have done so. Both of thesepotential limitations were addressed in our experiments so as tofoster greater generality.

A final methodological innovation of this study was our type-token analysis of the thought listings. The postmessage thought-listing technique originally developed by Brock (1967) hasyielded a number of valuable insights into the nature andcontent of cognitive processes that people engage in as they readpersuasive messages. The most common coding scheme usedwith this technique entails the use of two categories: message-consistent responses and message-inconsistent responses. Othercoding categories, although receiving markedly less attention,have been used as well (Axsom, Yates, & Chaiken, 1987; Caci-oppo, Harkins, & Petty, 1981; Chaiken, 1980; Mackie, 1987;Wood & Kallgren, 1988). A novel feature of the present re-search was the classification of arguments according to a type-token distinction. This form of analysis, although admittedlymore labor intensive than other strategies, may have value wellbeyond that associated with our own inquiry. Our findings sug-gest that there may be more information available in thought-listing protocols than previously recognized and that more cau-tion should be exercised in terms of the meaning ascribed todifferences in the number of responses generated in thought-listing tasks, at least insofar as it matters for a particular re-search question whether such responses are thematically dis-tinct. More generally, our findings suggest that it would beworthwhile to look more carefully at the content, not just thevalence and quantity, of cognitive responses.

Relationship to Other Work

The predictions of the disconfirmation model seem similar tothose of Pyszczynski and Greenberg's (1989) biased hypothesistesting (BHT) model, a general model of social inference thatoutlines the antecedent conditions and mechanisms by which

affective and motivational factors influence cognitive processesto produce biased conclusions. The models make the followingsimilar predictions: (a) Individuals will allocate greater process-ing capacity to evaluating inconsistent (undesirable orunexpected) information than they will to evaluating consistent(desirable or expected) information, especially when this infor-mation is ego relevant (or associated with emotion); (b) mostof the information-processing expenditure will be composed ofefforts to refute the unwanted information or bolster the plausi-bility of a preferred alternative; and, (c) as a consequence, mostof the material generated will be consistent with the desired con-clusion or outcome.

With regard to the preceding, the general idea that peopleadopt a more assertive approach to processing unfavorable asopposed to favorable information has been articulated by nu-merous other theorists across a range of topics, including persu-asion, stereotyping, impression formation, judgment, and deci-sion making (e.g., Brewer, 1988; Chaiken, 1980; Fiske & Neu-herg, 1990; Kruglanski, 1980, 1990; Kunda, 1990; Lordetal.,1979; Nisbett& Ross, 1980; Petty &Cacioppo, 1986a, 1986b).Accordingly, it is not especially noteworthy that the disconfir-mation model shares this fundamental premise with the BHTmodel. What is more interesting are the ways in which the twoseemingly similar models differ with respect to the domains inwhich they are applicable, the conditions that determine thetype of processing adopted, and the mechanisms that underliethe resultant biases.

The most apparent dissimilarity between the two accounts isthat the process described by BHT results in self-serving attri-butions, whereas that described by the disconfirmation modelresults in biased judgments about argument quality. In turn,this reflects a difference between the domains in which the twomodels are applicable. BHT is offered as a general model ofcausal attribution in the domain of social inference, whereas thedisconfirmation model is put forth as an account of the processof evaluating the strength of arguments related to one's beliefs.

The two models also propose very different information-pro-cessing mechanisms. Unlike the disconfirmation model, BHTadopts a general hypothesis-testing approach. Specifically, whenan unexpected event occurs, active hypothesis-testing processesare set in motion. In these circumstances, a given event cannotbe explained in terms of a preexisting causal theory, and theindividual perceives the need to resolve the ambiguity sur-rounding the event by seeking a plausible alternative causal ex-planation. According to the disconfirmation model, however,the trigger that sets in motion the more rigorous form of infor-mation processing aimed at refuting the implications of the newinformation is not the violation of an expectancy (i.e., partici-pants did not encounter anything unexpected in terms of thecontent of the arguments or the nature of the task presented tothem) but a challenge to a person's strongly held prior belief.Moreover, whereas BHT emphasizes the importance of (causal)ambiguity in the elicitation of active hypothesis testing, ambi-guity does not have a role in the disconfirmation model, nor wasit present in any meaningful way in the studies we have reportedhere (i.e., the tasks did not involve deception, the issues pre-sented to participants were relatively familiar, and the argu-ments were straightforward and written in clear and unequivo-cal language).


Thus, the disconfirmation model and BHT generally dealwith very different situations. However, Pyszczynski andGreenberg (1989) did discuss a case that is similar to the onesthat we have considered. According to Pyszczynski andGreenberg, people also engage in biased attributional process-ing and active hypothesis testing when they are confronted withan undesirable conclusion, such as one that represents a threatto their self-esteem. When an event is ego relevant, consider-ation of an undesirable hypothesis (e.g., I failed the exam) elicitsa state of aversive arousal, which in turn motivates the person toprocess information in such a way as to provide evidence for amore palatable alternative hypothesis (e.g., The test wasunfair). Pyszczynski and Greenberg provided no data to sup-port these claims, nor did any of the investigators they cited.However, the claim of interest is supported by findings that wehave reported. Specifically, in Experiment 1, only those issuesabout which participants had strong beliefs revealed signs ofeffortful processing (longer reading time, more careful scrutiny,and increased efforts to refute incompatible as compared withcompatible arguments). Furthermore, in Experiment 2, theseeffects were exaggerated for participants whose beliefs were as-sociated with emotional conviction, a construct that may be anindicator of ego relevance.

There are also points of overlap between the framework wepropose and that of cognitive response models (e.g., Brock,1967; Chaiken, 1980; Greenwald, 1968; Petty, Ostrom, &Brock, 1981), most notably the elaboration likelihood model(ELM) advanced by Petty and Cacioppo( 1981,1986a, 1986b).In the ELM treatment of attitude change, the mediational roleof generated beliefs and thoughts is emphasized. Similarly, ourmodel is concerned with the way in which people actively at-tempt to relate prior beliefs to propositions contained in a mes-sage (or argument). And although cognitive response modelsare concerned with attitude change processes, whereas ourmodel is concerned with judgments of argument strength, thepsychological mechanisms proposed to underlie the two pro-cesses are similar. First in both treatments, people generatemore beliefs and thoughts when they are presented with an in-compatible position than when they are presented with a com-patible position. Second, in both ELM and the disconfirmationmodel, the valence and number of generated thoughts are criti-cal determinants of judgmental outcomes; in the former, thenumber of counterarguments generated is inversely related tothe efficacy of the persuasive communication, and, in the latter,the number of counterarguments generated is inversely relatedto the judged strength of the argument.

Eagly and Chaiken (1993) have noted that, in the large bodyof empirical work on ELM, a range of factors have been identi-fied that affect the extent of message processing, but only one(the strength of a persuasive argument) has been shown to in-fluence the valence of message-relevant thoughts. Findings fromthe present research suggest that another variable affecting thevalence of generated beliefs and thoughts is whether a personagrees or disagrees with the position advocated in an argument.Our findings also suggest that the degree to which beliefs areassociated with emotion is a determinant of both the valenceand number of message-relevant thoughts as well as the per-ceived strength of message arguments. These results add to an

understanding of the role of emotion in ELM. Until recently,most of the empirical work on emotion in this model has dealtwith its role as a peripheral cue, enhancing the persuasive im-pact of a message when a person's ability or motivation to pro-cess this message is low. Our research suggests that affective fac-tors can play a significant role in augmenting the intensity andnature of central route processing.

In addition to its connections to well-known theoreticalframeworks, the research reported here is relevant to severalrecent findings (as mentioned in the introduction). In a studyconducted by Kunda (1990), participants evaluated argumentsconcerning the relation of caffeine consumption to fibrocysticdisease, a fictitious disease said to occur only in women. Kundafound that female participants considered the arguments lessstrong than did male participants. In a similar study by Dittoand Lopez (1992), participants evaluated the quality of a(bogus) medical test whose results indicated that participantsdid or did not have a fictitious enzyme deficiency. This determi-nation was based on a (rigged) TAA saliva reaction test (self-administered by participants and said to take from 10 s to 2min to complete). Relative to participants who received a de-termination of good health, participants who believed they hadTAA deficiency rated the saliva reaction test as less accurate,waited approximately 30 s longer to conclude the test was com-plete, and were more likely to generate alternative explanationsfor the test result. These lines of research complement ours inseveral important ways. Considered together, the three lines ofinquiry converge on the notion that desirable (compatible) andundesirable (incompatible) information is treated differently inthat the former is judged stronger and is accepted less critically.Moreover, the reliability and generality of our findings are en-hanced by the fact that the same type of bias has emerged instudies involving such different paradigms, issues, and mea-sures (see also Koehler, 1993; Mahoney, 1977; Pyszczynski etal., 1985;Wyer&Frey, 1983).

There are some important differences, however, between ourstudies and those of Kunda and Ditto and Lopez. First, in thelatter investigations, participants evaluated arguments aboutcontrived events that had a bearing on the participants' physicalwell-being. The beliefs under investigation were experimentallyinduced, and participants had no real knowledge or familiaritywith the domains of interest. By contrast, the beliefs we investi-gated were ones that participants had held for some time beforethe experiment and were, therefore, likely to be related to otherfirmly entrenched belief structures such as the self-concept andreligious or political ideology. Second, there is a difference in theway in which incompatibility or inconsistency is treated in thedifferent approaches. In our work, the concern is specificallywith the compatibility between a person's prior beliefs and aparticular conclusion offered in an argument. In the work ofKunda, Ditto and Lopez, and others associated with the moti-vated judgment tradition (Kruglanski, 1990; Pyszczynski et al.,1985; Wyer & Frey, 1983), the role of actual, entrenched priorbeliefs is of little or no importance. Instead, this work focuseson the implications of a particular conclusion for the self. Thedesirability of this conclusion for the self per se, rather than itscompatibility with a person's prior beliefs, serves as the triggerfor the judgmental biases observed. Needless to say, motivated


refutation of evidence could well be at work in both lines ofresearch. In one case, the motivation to refute evidence derivesfrom a wish to protect the self from a rather immediate threat(e.g., the possibility that one is unhealthy); in another, the mo-tivation stems from a wish to protect the integrity of one's be-liefs (e.g., the view that sex among people of the same gender isimmoral). It remains for future research to determine whetherthe prior belief effect described in this article can be assimilatedto a more general phenomenon having to do with deflecting theimplications of "undesirable arguments," broadly denned.

Normative Status oftheDisconfirmation Model

A final question that arises is whether the processing underly-ing the prior belief effect is rational. In the present context, onecan ask whether the disconfirmation model is compatible withan appropriate normative model. Consider our participants asinvolved in a choice situation: When presented an argument, aparticipant must decide whether to accept it (roughly at facevalue) or, instead, to engage in the mental work needed to de-termine whether the argument contains a fallacy. Given thischoice perspective, the appropriate normative model is ex-pected utility theory, which maps onto the disconfirmationmodel rather well.9 According to expected utility theory, whenconfronted with choices between two alternative courses of ac-tion, people should select the action that maximizes expectedutility. In the context of making judgments about the strengthof arguments, the two actions are accepting the argument atface value and searching for a fallacy in the argument, and theutilities involved include the positive utility of being right(judging a good argument strong or judging a fallacious argu-ment weak), the negative utility or cost of being wrong, and the(lesser) negative utility or cost of expending mental effort to finda fallacy. Because the option of searching for a fallacy includes acost that the option of accepting at face value does not, the for-mer option should be preferred only when a fallacy is likely (i.e.,only when the conclusion of the argument is improbable).These ideas map directly onto the disconfirmation model.When an argument is compatible, its conclusion seems proba-ble, and it is unlikely that there will be a fallacy in it; hence, thereis little justification for engaging in a mentally costly deliberativesearch of memory. In contrast, when an argument is incompat-ible, its conclusion seems improbable, and it is likely that it con-tains a fallacy; consequently, there is good reason to devote theextra mental work needed to find the fallacy. From this perspec-tive, the bias to disconfirm only incompatible arguments seemsrational.

The rationality of this reasoning process becomes even moreapparent when one considers that a disconfirmation bias is of-ten evident in scientific practice (see Koehler, 1993). If a socialpsychologist is conducting an experiment on an aspect of thediscounting principle, for example, and the results of this ex-periment fail to reveal any evidence whatsoever for the dis-counting principle, it is very likely that the investigator willquestion the experimental procedure (the "premises" of his orher "argument") and engage in a time-consuming and costlysearch to find out exactly what went wrong with the study. Incontrast, had the investigator's experiment turned out exactlyas predicted, it is likely that he or she would have accepted its

findings without misgivings and moved on to the next study. Thesame kind of disconfirmation bias also characterizes researchin the "hard" sciences (e.g., Galison, 1987;Mahoney, 1977).

The preceding considerations indicate that a disconfirmationbias can be normatively justified. Still, we do not want to leavethe reader with the impression that all aspects of the behavior ofthe participants in our experiments were rational. The case fortotal rationality hinges on the assumption that the prior beliefsinvolved (which determine whether an argument is compatibleor not) were themselves arrived at by a normative process. Itseems unlikely that this would always be the case. Consider, forexample, how many people arrive at their beliefs concerning gayrights and abortion, issues that are often surrounded by fervorand confusion. In addition, our analysis of the protocols re-vealed that participants often tried to undermine an incompat-ible argument by repeating tokens of the same counterargu-ment. It is not normative to believe that one has offered morecounterarguments against a conclusion simply because one hasrepeated oneself! Thus, when one looks at the details of thesearch for disaffirming evidence, irrationalities begin tosurface.

9 The following discussion of this mapping is based on the ideas ofPatrick Maher.

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(Appendix follows on next page)


Appendix

Seven Belief Statements (Issues) Included in Experiment 1

1. The death penalty should/should not be abolished.2. It is/is not appropriate, under certain circumstances, to strike a child.3. Employers should/should not be required to hire fixed percentage minorities.4. Minors seeking abortions should/should nol be required to have parental consent.5. Gay-lesbian couples should/should not be allowed to adopt children.6. It should/should not be possible for murderers under the age of 16 to be sentenced to death.7. Police should/should nol have the right to make random checks of drivers' blood alcohol level.

Received January 19, 1995Revision received December 22, 1995

Accepted January 3, 1996

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