SOCIAL INFLUENCE

2012, 7 (4), 269–284

The effects of primed causal uncertainty and causal

importance on persuasion

Stephanie J. Tobin1, Kristen I. Capuozzo

2, and

Melissa M. Raymundo3

1School of Psychology, University of Queensland, St Lucia, QLD,Australia2Department of Psychology, University of Houston, Houston,TX, USA3Department of Psychology, University of Houston-Downtown,Houston, TX, USA

We examined the effects of temporarily accessible causal uncertainty andcausal importance on persuasion. Participants completed a scrambled sentencepriming task, read strong or weak, causal or noncausal arguments, and thenreported their attitudes. We found that participants primed with high levels ofcausal uncertainty and importance thought carefully about the causalarguments. They were more persuaded by the strong compared to weakcausal arguments, and this effect was partially mediated by the favorability oftheir message-relevant thoughts. Participants primed with high causal uncer-tainty and low causal importance were more persuaded by strong compared toweak noncausal arguments, but this effect was not mediated by thoughts.We discuss the implications of these findings for the causal uncertainty andpersuasion literatures.

Keywords: Persuasion; Causal uncertainty; Causal importance; Causal arguments.

When trying to create lasting attitude change it is typically best to take thecentral route to persuasion (Petty & Cacioppo, 1986; Petty & Wegener,1999). By presenting motivated and able perceivers with strong arguments,one can mold attitudes in the desired direction. Because these attitudeswould be based on elaborative processing, they should persist over time,predict behaviour, and resist attacks (Petty & Cacioppo, 1986; Petty &Wegener, 1999). Another promising approach to lasting attitude change

Address correspondence to: Stephanie Tobin, School of Psychology, University of

Queensland, St. Lucia, QLD, 4072, Australia. E-mail: s.tobin@uq.edu.au

� 2012 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business

http://www.psypress.com/socinf http://dx.doi.org/10.1080/15534510.2012.667572

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examines the content of arguments beyond the basic strong versus weakdichotomy. This approach examines the relative effectiveness of causal andnoncausal arguments (Slusher & Anderson, 1996). Causal argumentsexplain why a particular variable is associated with a particular outcome.Noncausal arguments provide only covariation information about twovariables. Slusher and Anderson (1996) found that causal arguments weremore effective than noncausal arguments in changing people’s attitudes andbehavioral intentions, even when these outcomes were assessed 3 weeks afterthe message. Causal arguments were more effective than noncausalarguments because they changed people’s theories about how and whyvariables were related (Slusher & Anderson, 1996). Furthermore, it was alsoharder for people with opposing attitudes to discount the causal comparedto noncausal arguments (Slusher & Anderson, 1996).

The question then becomes, how can we motivate people to attend tothese potentially powerful causal arguments? Prior research has found thatpeople are more motivated to attend to arguments when they think thearguments will be valid (Heesacker, Petty, & Cacioppo, 1983). Consistentwith this idea, Tobin and Raymundo (2009) found that increasing people’sconfidence in the source’s understanding of why events happen can increaseattention to causal arguments. In addition, prior research has found thatpeople are more motivated to attend to arguments when the argumentsaddress their needs (for a review see Brinol & Petty, 2006). Consistent withthis idea, Tobin and Weary (2008) found that people who doubted their ownunderstanding of why events happen (high causal uncertainty), yet valuedsuch an understanding (high causal importance), paid more attention tocausal arguments. In the current research we were interested in whethercausal uncertainty and causal importance could be temporarily induced inthe average perceiver to motivate them to attend to causal arguments.

Causal uncertainty refers to doubt about one’s understanding of whyevents occur (Weary & Edwards, 1994). People who are chronically high incausal uncertainty are generally unsure about what causes their own positiveand negative outcomes, as well as what causes other people’s behavior andoutcomes. Chronic causal uncertainty can stem from and further underminea person’s sense of prediction and control (Edwards & Weary, 1998;Jacobson, Weary, & Edwards, 1999). In addition, discrepancies betweencurrent and desired levels of understanding can produce negative affect(Weary, Tobin, & Edwards, 2010). Accordingly, people with chronicallyhigh levels of causal uncertainty are motivated to reduce their uncertaintyand restore a sense of control and well-being. Research has shown thatcausally uncertain perceivers typically try to improve their understanding ofothers by seeking out and thinking carefully about causally relevantinformation. They try to form accurate impressions of others by askingdiagnostic questions (Weary & Jacobson, 1997) and when making

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judgments about others, they go beyond stereotypes and factor inindividuating information (Tobin, Weary, Brunner, Gonzalez, & Han,2009; Weary, Jacobson, Edwards, & Tobin, 2001) and situationalconstraints (Weary, Vaughn, Stewart, & Edwards, 2006).

Causal importance refers to the value that people place on causalunderstanding, which can influence how people respond to causaluncertainty. Higher levels of causal importance are thought to makecausal uncertainty more noticeable and increase the motivation to resolve it(Weary & Edwards, 1996; Weary et al., 2010). As mentioned earlier, Tobinand Weary (2008) examined how individual differences in causal uncertaintyand causal importance influence people’s responses to causal persuasivearguments. They found that when participants were chronically high incausal uncertainty and causal importance, they paid more attention tocausal, counterattitudinal arguments than did participants who were lowin causal uncertainty and/or causal importance. Greater attention wasindicated by their sensitivity to argument quality: they were more persuadedby strong compared to weak arguments. Tobin and Weary argued that thecounterattitudinal nature of the message activated causal uncertainty beliefsby introducing unexpected associations. Individuals high in causaluncertainty and causal importance then thought carefully about causalarguments in order to learn the reasons behind the outcomes described inthe message (e.g., the negative effects of casino gambling). Because they werepaying attention, they were more persuaded when the arguments werestrong compared to weak (Chaiken, 1987; Chen & Chaiken, 1999; Petty &Cacioppo, 1986; Petty & Wegener, 1999).

Tobin and Weary’s (2008) research provides a good first step inunderstanding the associations between chronic causal uncertainty, chroniccausal importance, and attention to causal arguments. However, theindividual difference approach has its limitations. Chronic causal uncertaintyis correlated with a host of other individual differences, including negativeaffect, anxiety, depression, neuroticism, pessimism, self-esteem, perceivedcontrol, and locus of control (Edwards, Weary, & Reich, 1998; Tobin &Raymundo, 2010; Weary & Edwards, 1994). Tobin and Weary (2008)measured depression and showed that it was not driving the observedassociations. However, the many other known correlates of causaluncertainty were not assessed. And even if all known correlates wereassessed, it is always possible that some other unknown correlate wasproducing the observed associations. An experimental test of the effects ofcausal uncertainty and causal importance on persuasion would go a long wayin increasing our confidence about the role they play in a persuasion context.

With this goal in mind we primed our participants with high or low levelsof causal uncertainty and causal importance, and then provided them with acounterattitudinal message that contained strong or weak, causal or

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noncausal arguments. The causal uncertainty prime should temporarilyinfluence participants’ perceptions of their causal knowledge, with the highrelative to low causal uncertainty prime reducing perceived understanding.The causal importance prime, on the other hand, should influence theirsensitivity to less-than-desired levels of causal knowledge, with the highrelative to low causal importance prime increasing sensitivity. When primedwith both high causal uncertainty and high causal importance, then,participants should have the greatest motivation to improve their causalknowledge. This motivation ought to lead them to think carefully about thecausal arguments, which should be reflected in their sensitivity to ourargument quality manipulation. When elaboration is high, people are morepersuaded by strong compared to weak arguments (Petty & Cacioppo,1986). Thus our high causal uncertainty/high causal importance participantsshould be more persuaded by strong compared to weak causal arguments.

In addition to manipulating rather than measuring causal uncertainty andcausal importance, we extended earlier research (Tobin & Weary, 2008) byexamining participants’ message-relevant thoughts. When elaboration ishigh, attitudes should be based on message-relevant thoughts (Petty &Cacioppo, 1986). Thus we tested whether the favorability of participants’message-relevant thoughts would mediate the effects of causal argumentquality on attitudes in our target group: participants who were primed withhigh causal uncertainty and high causal importance.

PILOT STUDY

To ensure that the primes activated the intended constructs, we conducted apilot study.

Participants

A total of 87 participants were randomly assigned to condition in a 2 (causaluncertainty prime: high, low)� 2 (causal importance prime: high, low)between-participants design. The data from 14 participants were excluded: 5participants did not complete the priming task or one of the dependentmeasures, 4 participants were outliers (43 SD from the mean), 4participants did not complete the study within a reasonable amount oftime (41.5 hours), and 1 participant reported a high level of distraction.After these exclusions the sample consisted of 73 participants (61 female, 12male) who ranged in age from 18 to 63 (M¼ 24.77, SD¼ 7.23). The samplewas 31% Caucasian, 25% Hispanic, 22% African American, 11% AsianAmerican, and 11% other. All participants were currently enrolled in one ofseveral undergraduate psychology classes at a southwestern university in theUnited States.

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Procedure

The study was conducted online. Participants were asked to complete theentire study in a single sitting, without taking any breaks and without anydistractions (i.e., no music or television in the background, no other peopletalking to them, no checking e-mail, instant messaging, or visiting otherwebsites). Participants completed a scrambled sentence priming task, a fillertask, a causal judgment task, and a noncausal judgment task.

Causal uncertainty and causal importance priming task. The causaluncertainty primes were similar to those used in past research. They wereidentical to Tobin and Raymundo’s (2009) primes, with one exception: wechanged the pronouns from third person (he, she, they, him, her, them) tofirst person (I, me, my) so that the primes would create self-uncertainty/certainty rather than signaling that other people were uncertain/certain.Tobin and Raymundo (2009) had modified the content of Wichman,Brunner, and Weary’s (2008) high causal uncertainty sentences to bettercapture the construct of causal uncertainty and introduced a low causaluncertainty comparison condition. The causal importance sentences werecreated for the purposes of this study, as there was no existing causalimportance prime. We based the sentences on items from the causalimportance scale (Tobin & Weary, 2008).

Our scrambled sentence priming task consisted of 30 sets of five words.For each set participants were asked to form a grammatically correctsentence using four of the words. The word that is not part of the four-wordsolution is shown in parentheses in the examples that follow. Depending oncondition, 10 of the sentences expressed either high causal uncertainty, e.g.,their behavior puzzles me (cause), I found it unclear (why), or low causaluncertainty, e.g., I understand their behavior (cause), I found it clear (why).Furthermore, five of the sentences expressed either high causal importance,e.g., understanding why is important (car), I must understand why (book),or low causal importance, e.g., understanding why is unimportant (car),I don’t care why (book). The other 15 sentences were fillers, e.g., I ate mylunch (magazine), she looks very fit (sidewalk).

After unscrambling all 30 sentences participants filled out a consumersurvey as a filler task (Tobin & Raymundo, 2009), as previous research hasfound that individuals spontaneously inhibit their causal uncertaintyimmediately after the priming task (Wichman et al., 2008). Specifically,participants indicated how often they consumed different types of beveragesand how often they ate at various restaurants.

Causal and noncausal judgments. The causal and noncausaljudgment tasks were based on those used in prior validation studies

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(Weary & Edwards, 1994; Wichman et al., 2008). First, for the causaljudgment task participants were asked to read a passage that had ostensiblybeen adapted from a diary study where participants wrote about recent lifeevents. They read about a woman who was anxious going into a meetingwith her boss, hoping for a promotion. They learned that she did, in fact,receive a promotion, along with a number of other people in her company.Participants were first asked to rate the extent to which they expected her toreceive the promotion (1¼ not at all expected, 9¼ very much expected), asexpected events are typically better understood. Participants were thenasked to list all of the possible relevant causes of the event that they couldimagine. Lastly they were asked to rate how certain they were in theirunderstanding of why the woman was promoted (1¼ absolutely uncertain,9¼ absolutely certain) and how important it is to understand why people getpromoted (1¼ not at all important, 9¼ very important).

Next, for the noncausal judgment task participants were asked to make avariety of estimates (e.g., how often men reveal negative things aboutthemselves to friends, percentage of office space in the U.S. that is currentlyvacant). They were asked to rate how certain they were in each estimate(1¼ absolutely uncertain, 9¼ absolutely certain) and how important it wasto know each fact (1¼ not at all important, 9¼ extremely important).Ratings were averaged to form noncausal certainty (a¼ .79) and noncausalimportance (a ¼ .68) indices.

Lastly participants completed the Positive and Negative Affect Schedule(Watson, Clark, & Tellegen, 1988). Specifically, they rated on 5-point scalesthe extent to which 10 positive (e.g., interested, alert, excited; a¼ .85) and 10negative (e.g., irritable, distressed, ashamed; a¼ .76) words described theircurrent feelings.

Pilot results

We predicted that the causal uncertainty prime would influence causalcertainty judgments and that the causal importance prime would influencecausal importance judgments. We used expectancies as a covariate toaccount for unexplained variance in causal certainty. Expectancies werecorrelated with causal certainty, r¼ .58, p5 .001. The more participantsexpected the outcome, the more certain they were in their understanding ofwhy the outcome occurred. Expectancies were not correlated with causalimportance, r¼ .09, p¼ .47, noncausal certainty, r¼ .10, p¼ .38, ornoncausal importance, r¼�.10, p¼ .39. Additionally, a 2 (causal uncer-tainty prime)� 2 (causal importance prime) ANOVA revealed no significanteffects of our manipulations on expectancies, ps4 .51. Thus the expectancyvariable captures natural variability in the way participants interpreted thescenario. A 2� 2 ANCOVA on causal certainty revealed only significant

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main effects of expectancies, F(1, 68)¼ 41.65, p5 .001, Z2p ¼ :38, and causal

uncertainty prime, F(1, 68)¼ 4.97, p5 .05, Z2p ¼ :07. Participants were less

certain about why the woman was promoted when they had received thehigh (M¼ 6.11, SE¼ .32) compared to low (M¼ 7.07, SE¼ .29) causaluncertainty prime.

A 2� 2 ANOVA on causal importance revealed only a significantmain effect of causal importance prime F(1, 69)¼ 4.06, p5 .05, Z2

p ¼ :06.Participants thought it was more important to understand why people gotpromoted when they had received the high (M¼ 8.29, SE¼ .20) comparedto low (M¼ 7.73, SE¼ .19) causal importance prime. Thus, as intended, ourcausal uncertainty prime influenced causal certainty and our causalimportance prime influenced causal importance.

As expected, 2� 2 ANOVAs revealed no significant effects of the primeson noncausal certainty, ps4 .21, noncausal importance, ps4 .56, positiveaffect, ps4 .35, or negative affect, ps4 .28.

MAIN STUDY

Method

Participants

A total of 327 undergraduate psychology students were randomlyassigned to conditions in this 2 (causal uncertainty prime: high, low)� 2(causal importance prime: high, low)� 2 (argument type: causal, non-causal)� 2 (argument quality: strong, weak) between-participants factorialdesign. The data from 36 participants were excluded: 21 participants did notlist any thoughts, 4 participants did not complete the priming task, 10participants were outliers (43 SD from the mean), and 1 participant did notcomplete the study within a reasonable amount of time (41.5 hours). Afterthese exclusions the sample consisted of 291 participants (236 female, 53male, 2 gender not reported) who ranged in age from 17 to 44 (M¼ 22.65,SD¼ 4.75). The sample was 28% Caucasian, 27% Hispanic, 20% AsianAmerican, 18% African American, and 7% other.

Procedure

The basic procedure was the same as the pilot study except that, instead ofmaking causal and noncausal judgments, participants read a persuasivemessage and answered questions about it.

Priming task. As in the pilot study, participants completed a scrambledsentence priming task. Depending on condition, 10 sentences conveyed highor low causal uncertainty, 5 sentences conveyed high or low causal

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importance, and 15 sentences were fillers. Participants then completed thebeverage and restaurant filler task.

Persuasive message. In an ostensibly separate study participants read anessay about recent changes happening at some universities in the U.S. Theauthor of the essay was described as a leading social scientist who specializesin the health and well-being of university students. The byline on the articleread: Jason Phillips, Ph.D., Social Scientist.

As in Tobin and Raymundo (2009) the essay contained strong or weak,causal or noncausal arguments against the idea of extending Spring Breakfrom 1 to 2 weeks. The strong arguments presented very undesirableconsequences of an extension (i.e., negative emotions during the break;increased stress and lower grades after the break) whereas the weakarguments presented somewhat undesirable consequences (i.e., reducede-mail usage during the break; sleeping late and reduced reading efficiencyafter the break). The causal arguments explained why the negativeconsequence would occur (e.g., stress levels would increase ‘‘because thelonger students are away from their classes, the more effort it takes to startup again’’), whereas the noncausal arguments provided covariationinformation consistent with the proposed consequence (e.g., ‘‘Studies havefound that vacations longer than 5–7 days increased post-vacation stressamong students by up to 16%’’).

Tobin and Raymundo (2009) established that the strong argumentspresented significantly more undesirable consequences than the weakarguments, and that there was no effect of causal vs noncausal argumenton consequence undesirability. Furthermore, a pilot study had shown that,prior to reading any arguments, participants were generally in favor of theextension, making the position of the essay counterattitudinal (Tobin &Raymundo, 2009).

Dependent measures. After reading a version of the essay, participantsreported their opinion on the issue. Specifically, they rated a 2-week SpringBreak on the following 7-point semantic differential scales: bad/good,unfavorable/favorable, foolish/wise, harmful/beneficial, dislike/like,negative/positive, undesirable/desirable. After the attitude items participantswere asked to list any thoughts they had while reading the essay. There wereprovided with up to eight text boxes in which to enter their responses.

Results

Attitudes

We reverse-scored and then averaged responses to the attitude items(a¼ .94) so that higher numbers indicated greater persuasion. Next we ran a

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2 (causal uncertainty prime)� 2 (causal importance prime)� 2 (argument

type)� 2 (argument quality) ANOVA on the attitude index. This analysisrevealed a main effect of argument quality, F(1, 275)¼ 8.92, p5 .01,

Z2p ¼ :03. Participants were more persuaded by strong (M¼ 4.35, SE¼ .14)

compared to weak (M¼ 3.79, SE¼ .13) arguments. The Causal UncertaintyPrime�Causal Importance Prime�Argument Type�Argument Quality

interaction was also significant, F(1, 275)¼ 9.46, p5 .01, Z2p ¼ :03.

To probe the four-way interaction we examined the effects of causalimportance prime, argument type, and argument quality within the high and

low causal uncertainty prime conditions. In the low causal uncertainty primeconditions there was a marginally significant main effect of argument

quality, F(1, 138)¼ 3.33, p¼ .07, Z2p ¼ :02, such that participants were

somewhat more persuaded by strong (M¼ 4.37, SE¼ .20) compared to

weak (M¼ 3.87, SE¼ .19) arguments, but the other effects were non-significant, ps4 .15. In the high causal uncertainty prime condition, we

observed a significant Causal Importance Prime�Argument Type�Argument Quality interaction, F(1, 137)¼ 8.84, p5 .01, Z2

p ¼ :06 (see

Figure 1). Subsequent analyses revealed significant Argument Type�Argument Quality interactions in both the high causal uncertainty/highcausal importance prime, F(1, 65)¼ 4.73, p5 .05, Z2

p ¼ :07, and high causal

uncertainty/low causal importance prime, F(1, 72)¼ 4.04, p5 .05, Z2p ¼ :05,

conditions.

1

2

3

4

5

6

7

Noncausal Causal Noncausal Causal

High Causal Uncertainty/Low Causal Importance

High Causal Uncertainty/High Causal Importance

Atti

tude

s

Weak

Strong

Figure 1. Causal Importance Prime�Argument Type�Argument Quality interaction in the

high causal uncertainty prime condition. Error bars represent the standard error of the mean.

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Follow-up t-tests revealed that participants primed with high causal

uncertainty and high causal importance were more persuaded by strong

(M¼ 4.85, SE¼ .38) compared to weak (M¼ 3.35, SE¼ .35) causalarguments, t(137)¼ 2.92, p5 .01, but were equally persuaded by strong

(M¼ 4.15, SE¼ .38) and weak (M¼ 4.38, SE¼ .40) noncausal arguments,

t(137)¼�0.42, p¼ .68. Conversely, participants primed with high causaluncertainty and low causal importance were more persuaded by strong

(M¼ 4.64, SE¼ .37) compared to weak (M¼ 3.33, SE¼ .37) noncausal

arguments, t(137)¼ 2.54, p5 .05, but were equally persuaded by strong(M¼ 3.71, SE¼ .40) and weak (M¼ 3.78, SE¼ .31) causal arguments,

t(137)¼�0.12, p¼ .90.

Thoughts

Blind to condition, a coder indicated whether each thought was favorable,

unfavorable, or neutral/irrelevant with respect to the advocacy (Cacioppo,Harkins, & Petty, 1981; Cacioppo & Petty, 1981; Petty & Cacioppo, 1986).

Inter-rater reliability (Kappa¼ .77) was established by having a second

coder, also blind to condition, rate a subset of the thoughts (n¼ 49).A thought index was created by subtracting the number of unfavorable

thoughts from the number of favorable thoughts, and then dividing by the

total number of favorable and unfavorable thoughts (Cacioppo et al., 1981;Cacioppo & Petty, 1981; Petty & Cacioppo, 1986). Higher numbers

indicated more favorable message-relevant thoughts.Of particular interest was whether thoughts would mediate the argument

quality effects we observed in the high causal uncertainty/high causalimportance/causal argument and high causal uncertainty/low causal impor-

tance/noncausal argument conditions. If participants in these conditions were

thinking carefully about the message, we would expect to observe argumentquality effects on both attitudes and thoughts (e.g., more persuaded by strong

compared to weak arguments). Furthermore, their thoughts about the

message should predict their attitudes, and once thoughts are controlled theargument quality effect on attitudes should be significantly reduced.

Regression analyses in the high causal uncertainty/high causal impor-

tance/causal argument condition revealed that argument quality signifi-

cantly predicted both attitudes, b¼ .51, t(35)¼ 3.50, p5 .01, and thoughts,b¼ .41, t(35)¼ 2.69, p5 .05. When we examined attitudes as a function of

argument quality and thoughts, we found that both thoughts, b¼ .59,

t(34)¼ 4.71, p5 .001, and argument quality, b¼ .26, t(34)¼ 2.08, p5 .05,were significant predictors. However, controlling for thoughts reduced the

magnitude of the argument quality effect on attitudes. Furthermore, a Sobel

test revealed significant mediation, Z¼ 2.33, p5 .05 (see Figure 2).

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In the high causal uncertainty/low causal importance/noncausal argument

condition, a different picture emerged. Argument quality significantly

predicted attitudes, b¼ .43, t(34)¼ 2.74, p5 .05, but did not predict

thoughts, b¼ .22, t(34)¼ 1.29, p¼ .21. When we examined attitudes as a

function of argument quality and thoughts, we found that both thoughts,

b¼ .49, t(33)¼ 3.60, p5 .01, and argument quality, b¼ .32, t(33)¼ 2.33,

p5 .05, were significant predictors. Lastly, the Sobel test was not

significant, Z¼ 1.22, p¼ .22 (see Figure 3).

Argument Quality

Thought Favorability Index

Attitudes

.26* (.51*)

Sobel 2.33*

.59*.41*

High Causal Uncertainty/High Causal Importance/Causal Argument Condition

Figure 2. Thoughts as a mediator in the high causal uncertainty/high causal importance/causal

argument condition.

Argument Quality

Thought Favorability Index

Attitudes

.32* (.43*)

Sobel 1.22

.49*.22

High Causal Uncertainty/Low Causal Importance/Noncausal Argument Condition

Figure 3. Thoughts as a mediator in the high causal uncertainty/low causal importance/

noncausal argument condition.

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In sum, these analyses revealed that thoughts partially mediated the effectof argument quality on attitudes in the high causal uncertainty/highcausal importance/causal argument condition, but not in the high causaluncertainty/low causal importance/noncausal argument condition. Ofcourse, given the correlational nature of the data, we cannot definitivelysay that message-relevant thoughts caused the observed effects of argumentquality on attitudes in the high causal uncertainty/high causal importance/causal argument condition. It is also possible that reporting their attitudesinfluenced the thoughts that participants recalled and reported.

Discussion

As predicted, participants who received both the high causal uncertaintyand high causal importance primes showed increased attention to causalarguments. These participants had more favorable thoughts about and weremore persuaded by the strong compared to weak causal arguments.Furthermore, their message relevant thoughts partially mediated the effectof argument quality on attitudes. However, as predicted, these participantsdid not devote much thought to the noncausal arguments, presumablybecause those arguments did not address their needs. Participants wouldhave needed to generate their own causal explanations in the noncausalargument conditions. This task may have seemed too difficult, and even ifparticipants had generated their own causes, the explanations would likelyhave been unpersuasive given their lack of confidence in their own thoughts(Petty, Brinol, & Tormala, 2002).

When low causal importance had been primed, high causal uncertaintydid not lead participants to think about the causal arguments. This isconsistent with our theorizing that low causal importance reduces sensitivityto less-than-desired levels of causal understanding. Unexpectedly, however,these participants were more persuaded by the strong compared to weaknoncausal arguments. Although such an argument quality effect can indicateelaborative processing, in this case we also found that participants’ messagerelevant thoughts did not differ in the strong and weak noncausal argumentconditions. Thus it may be the case that their attitudes were based on cuesthat were present in the strong or weak noncausal arguments. Across allconditions, we tried to minimize cues that would distinguish the strong fromthe weak arguments. In the noncausal argument conditions, that meantkeeping constant the number of statistics. However, we inadvertently usednumerals more consistently in the strong noncausal message (e.g., stressincreased by 16%) than in the weak noncausal message (e.g., snooze pressesdoubled). Numerals might have served as a cue to the amount of evidence.Nonetheless, the fact that these participants showed an effect in thenoncausal argument condition suggests that when uncertain perceivers

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devalue causal understanding, they turn to other types of information toresolve their uncertainty. Statistics may be particularly appealing if theyseem to be more ‘‘objective’’ than potentially confusing causal explanations.

The finding that individuals respond differently to causal uncertainty as afunction of causal importance supports the causal uncertainty model, inwhich causal importance magnifies the effects of causal uncertainty andintensifies reduction efforts (Weary et al., 2010). Understanding the role ofcausal importance helps to shed light on how individuals may cope withcausal uncertainty on a day-to-day basis. According to the causaluncertainty model, higher levels of causal importance increase themotivation to resolve causal uncertainty, but if attempts are unsuccessful,individuals experience negative affect (Weary et al., 2010). By recognizingthat it is not equally important to understand why all events occur,individuals can conserve their cognitive resources and protect theirpsychological well-being. The development of a causal importance primein the current research should facilitate future investigations into the rolethat causal importance plays in uncertainty reduction efforts andpsychological well-being.

The findings of the current research strengthen and extend prior researchwhich found that perceivers who were chronically high in causal uncertaintyand causal importance thought carefully about causal arguments (Tobin &Weary, 2008). By manipulating rather than measuring causal uncertaintyand causal importance in the current research we have shown thattemporary elevations in causal uncertainty and causal importance increaseattention to causal arguments, as indicated by participants’ sensitivity toargument quality. Furthermore, by presenting data consistent with the ideathat high causal uncertainty/high causal importance participants base theirattitudes on their message-relevant thoughts we have provided additionalevidence of systematic processing.

The findings also relate more broadly to research that has examined otherforms of uncertainty and importance. The heuristic systematic model’s(HSM) sufficiency principle posits that people will use whatever processesnecessary, starting with heuristic processes, to bring their current level ofconfidence up to their desired level of confidence (Chaiken, Liberman, &Eagly, 1989; Chen & Chaiken, 1999). So when large gaps exist, as would bethe case when people feel uncertain, systematic processing should increase.Consistent with this idea, past research has found that participants engagedin more systematic processing of a persuasive message after writing abouta time they felt sad and uncertain, compared to when they wrote aboutfeeling sad and certain (Tiedens & Linton, 2001). Similarly, otherresearchers found more systematic processing of persuasive messageswhen there were inconsistencies between old and new attitudes (Petty,Tormala, Brinol, & Jarvis, 2006).

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Additionally, both the HSM (Chaiken et al., 1989; Chen & Chaiken, 1999)and the elaboration likelihood model (Petty & Cacioppo, 1986; Petty &Wegener, 1999) posit that as a message becomes more important or morerelevant to a perceiver, systematic processing increases. Thus our effects areconsistent with general persuasion principles in that participants primedwith causal uncertainty and causal importance engaged in systematicprocessing of causal arguments, which would have been most relevant totheir current need state.

Overall, then, we see that when trying to motivate perceivers to attend tocausal explanations, one can elicit high levels of causal uncertainty andcausal importance. This strategy could be used strategically by socialpsychology instructors, for instance, when they describe experiments to theirclasses. They could present students with puzzling effects to increase causaluncertainty and highlight the real-world implications of the phenomenon toincrease causal importance before explaining the underlying mechanism.Students would then pay much more attention to the mechanism andlearning outcomes may improve.

Similarly, if one knows that perceivers are likely to have a strong pre-existing need to understand why (perhaps because of the particular audienceor the topic), one would do well to explicitly address underlying causes. Suchcausal arguments can persuade people even in the absence of concrete datathat a proposed effect will occur. If a policy has counterintuitive effects thatwill impact people’s lives, for instance, it would be important to address whythe effects would occur. However, care should be taken to craft the strongestpossible argument in this type of situation, as perceivers would be thinkingcarefully about the provided information. In these potential applications wewould expect elaborative processing of strong, causal arguments to lead tolong lasting attitude change in the desired direction.

Manuscript received 22 November 2011

Manuscript accepted 13 February 2012

First published online 12 March 2012

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