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Automatic Attitudes and Health Information Avoidance
Jennifer L. Howell
Ohio University
Kate A. Ratliff and James A. Shepperd
University of Florida
Objective: Early detection of disease is often crucially important for positive health outcomes, yet people
sometimes decline opportunities for early detection (e.g., opting not to screen). Although some health-
information avoidance reflects a deliberative decision, we propose that information avoidance can also
reflect an automatic, nondeliberative reaction. In the present research, we investigated whether people’s
automatic attitude toward learning health information predicted their avoidance of risk feedback.
Method: In 3 studies, we gave adults the opportunity to learn their risk for a fictitious disease (Study 1),
melanoma skin cancer (Study 2), or heart disease (Study 3), and examined whether they opted to learn
their risk. The primary predictors were participants’ attitudes about learning health information measured
using a traditional (controlled) self-report instrument and using speeded (automatic) self-report measure.
In addition, we prompted participants in Study 3 to contemplate their motives for seeking or avoiding
information prior to making their decision. Results: Across the 3 studies, self-reported (controlled) and
implicitly measured (automatic) attitudes about learning health information independently predicted
avoidance of the risk feedback, suggesting that automatic attitudes explain unique variance in the
decision to avoid health information. In Study 3, prompting participants to contemplate their reasons for
seeking versus avoiding health information reduced information avoidance. Surprisingly, it did so by
inducing reliance on automatic, rather than controlled, attitudes. Conclusion: The data suggests that
automatic processes play an important role in predicting health information avoidance and suggest that
interventionists aiming to increase information seeking might fruitfully target automatic processes.
Keywords: information avoidance, automatic attitudes, implicit measures, contemplation, online screening
Early detection is critical for treating most medical conditions
and for prolonging life (Etzioni et al., 2003;Hall et al., 2010;
Harris & Eastman, 2000;McGoon et al., 2004;Saunders, Aasland,
Babor, de la Fuente, & Grant, 1993). Nevertheless, 29% of Amer-
icans indicate that they would avoid visiting the doctor, even if
they suspected they should (Persoskie, Ferrer, & Klein, 2014),
39% indicate that they would avoid learning their risk for cancer
(Emanuel et al., 2015), and 39% agree or strongly agree with
statements like “I would avoid learning some things about my
health,” and “when it comes to my health, sometimes ignorance is
bliss” (Howell, Crosier, & Shepperd, 2014). These findings sug-
gest that, despite the potential benefits of seeking health informa-
tion, people often opt to avoid learning such information.
Research suggests people will avoid available but unwanted
information—a phenomenon termed information avoidance—
when that information threatens their desired thoughts, emotions,
or actions (Shepperd & Howell, 2015;Sweeny, Melnyk, Miller, &
Shepperd, 2010). In previous research, investigators suppose that
people intentionally avoid information because they recognize that
the information poses a threat. However, it is also possible
that people are not entirely aware of why they seek or avoid
information. The present study examined whether automatic pro-
cesses influence health information avoidance in addition to more
controlled, and conscious motivations.
Automatic and Controlled Processes
Research suggests that it is useful to distinguish between atti-
tudes that are inferred from implicit measures—that is, relatively
indirect or uncontrollable performance-based measures—and atti-
tudes that are assessed through explicit measures—that is, directly
through self-report (Gawronski & Bodenhausen, 2011;Gawronski
& De Houwer, 2014;Petty, Fazio, & Briñol, 2012). Measures can
be thought of as “implicit” to the extent that they capture processes
that are some combination of uncontrolled, unintentional, autono-
mous, unconscious, efficient, or fast (Smith & Ratliff, 2015).
Implicit measures typically do not alert the respondent to be what
is being measured, or do not allow for the control of responses
even if the purpose is known. In the present research, we refer to
the outcome of such implicit measures as automatic attitudes and
assume that they represent relatively uncontrollable attitudes that
people are either unwilling or unable to report (Banaji & Heiphetz,
2010;De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009;
Gawronski, 2009;Gawronski, LeBel, & Peters, 2007;Nosek,
2007a,2007b). Measures can be thought of as “explicit” to the
Jennifer L. Howell, Department of Psychology, Ohio University; Kate
A. Ratliff and James A. Shepperd, Department of Psychology, University
of Florida.
This research was supported by a National Science Foundation Graduate
Research Fellowship awarded to Jennifer L. Howell (DGE-0802270) and
by a grant from Project Implicit awarded to Kate A. Ratliff. All data and
study materials are available on the Open Science Framework http://osf
.io/vq4a8/
Correspondence concerning this article should be addressed to Jennifer
L. Howell, Department of Psychology, Ohio University, 200 Porter Hall,
Athens, OH 45701. E-mail: Howellj@ohio.edu
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Health Psychology © 2016 American Psychological Association
2016, Vol. 35, No. 8, 816– 823 0278-6133/16/$12.00 http://dx.doi.org/10.1037/hea0000330
816
extent that they capture processes that are more deliberate and
controlled (Banaji & Heiphetz, 2010). In the present research, we
refer to the outcome of these explicit measures as explicit attitudes
and assume that they represent attitudes that people are both
willing and able to report (Arkin & Shepperd, 1990;Nosek, 2005).
Research suggests that automatic attitudes are especially useful
in predicting behaviors that are spontaneous, uncontrollable,
and/or arise from automatic, intuitive decision-making, and those
that involve socially sensitive topics (Greenwald, Poehlman, Uhl-
mann, & Banaji, 2009). Relevant for the current research, auto-
matic attitudes have been shown to predict health behaviors be-
yond explicit attitudes. For instance, participants’ automatic
attitudes toward tan versus pale (not-tan) faces better predict their
sun related intentions and willingness than do their self-reported
attitudes toward those groups (Ratliff & Howell, 2015).
In the present study, we investigate the hypothesis that infor-
mation avoidance operates, at least in part, automatically. Consis-
tent with this notion, as with many types of decisions (Bar-Anan,
Wilson, & Hassin, 2010;Gilbert, Brown, Pinel, & Wilson, 2000;
Nisbett & Wilson, 1977;Wilson, 2009;Wilson & Dunn, 2004;
Wilson, Hodges, & LaFleur, 1995), people are often unaware of
the reasons they avoid information. For instance, in an investiga-
tion of over 800 open-ended self-reports about why people seek
and avoid their health risks, the most common self-reported motive
for avoiding information was feeling “not at risk.” Ironically,
participants also frequently cited feeling “not at risk” to explain
why they choose to learn their risk (Weldon & Howell, 2013).
Indeed, even in studies demonstrating that people are more likely
to avoid information when it threatens their desired behavior
(Howell & Shepperd, 2013a), affect (Nielsen & Shepperd, 2012),
and cognitions (Howell, Miller, Sweeny, & Shepperd, 2013), par-
ticipants consistently failed to report that these factors motivate
their decision; ⬎2% of self-reports included these motives (Wel-
don & Howell, 2013). Furthermore, evidence suggests that induc-
ing metacognitive contemplation (i.e., considering one’s own
thoughts—which prompts controlled-system processing; Fletcher
& Carruthers, 2012), reduces information avoidance (Howell &
Shepperd, 2013b). Taken together, these results indicate that au-
tomatic processes may influence information avoidance. Never-
theless, no published study has directly investigated whether au-
tomatic attitudes are related to information avoidance.
Overview and Hypotheses
In three studies, we examined the hypothesis that automatic
processes influence the decision to avoid health information above
and beyond controlled processes. In Studies 1 and 2, we investi-
gated whether automatic and explicit attitudes predicted avoidance
of health risk feedback among college students (Study 1) and
general-population adults (Study 2). In Study 3, we attempted to
manipulate reliance on explicit over automatic attitudes by
prompting people to contemplate their motives for seeking versus
avoiding risk feedback, replicating, and extending (Howell and
Shepperd, 2013b). In all three studies, we expected that people
who had positive attitudes about avoiding information (as indi-
cated by both implicit and explicit measures) would be more likely
to avoid learning their personal risk for disease. In Study 3, we
expected that contemplating their reasons for seeking versus
avoiding their risk information would prompt people to rely more
on their controlled/self-reported attitudes, than on their automatic
attitudes about avoiding information.
Study 1: Method
Participants
Participants were 46 undergraduates (32 women) participating
in partial fulfillment of a research requirement. Participants’ ages
ranged from 18 to 81 years (M⫽34.5 years, SD ⫽12.7). We
collected data for an entire semester and stopped when the semes-
ter ended. Our sample provided sufficient power to detect a me-
dium effect (w⫽.50) at .80 power.
Procedure and Measures
When participants arrived at the lab, a researcher dressed in
medical scrubs greeted them, escorted them to computer worksta-
tions, and told them that they were participating in a study con-
ducted by the university hospital about a new (fictitious) disease
called thioamine acetlyase (TAA) deficiency (Howell & Shepperd,
2012;Jemmott, Ditto, & Croyle, 1986). After consenting, partic-
ipants viewed an informational video about TAA deficiency that
explained that TAA deficiency affects the body’s ability to process
nutrients and can lead to severe medical complications (e.g., im-
munodeficiency, physical deterioration, and heart problems), but it
did not specify the likelihood that TAA would lead to such health
problems. The video also explained that 20% of adults have TAA
deficiency, but that most adults are unaware that they have it. The
video appears online at https://youtu.be/lApOwIlbTrI.
After watching the video, participants completed a TAA risk
calculator that assessed a variety of health factors (e.g., height,
weight, blood type, past experience with a variety of medical
conditions) and viewed a screen indicating that, based on their
responses to the risk calculator, the computer could calculate their
lifetime risk for TAA deficiency. Participants chose whether they
wanted to learn their lifetime risk feedback using two options:
“Yes please give me my risk estimate for TAA deficiency,” or
“No, I do not want to learn my risk for TAA Deficiency right
now.” On the screen, the “yes” option was selected by default.
Thus, participants had to actively select not to learn their feedback
if they wished to avoid the information.
Although a variety of research suggests that default options are
important determinants of behavior (Beshears, Choi, Laibson, &
Madrian, 2009;Halpern, Ubel, & Asch, 2007;Johnson & Gold-
stein, 2003), we believe that not including a default option mud-
dies interpretation of people’s choice (see Shepperd & Howell,
2015 for a full discussion on the need for measures of information
avoidance to be active). Indeed, with “yes” as the default option,
it is clear that people who select “no” are actively avoiding the
information. Without the default choice, it would be unclear what
proportion of participants who selected “no” were avoiding infor-
mation versus choosing a response randomly. Likewise, if we had
made “no” the default, it would be unclear how many people who
did not change from the default did so because they did not care.
After they made their decision, participants completed a
speeded-self-report task (Ranganath, Smith, & Nosek, 2008)in
which they made snap judgments about the favorability of 60
stimuli usinga1(very unfavorable)to4(very favorable) scale.
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817
AUTOMATIC INFORMATION AVOIDANCE
The task was timed so that participants had to respond to each
stimulus within 1,200 ms or a message to “please respond faster”
appeared on the screen, and their response was not recorded. Of
these trials, 12–15 contained words pertinent to health information
avoidance (i.e., Learning Health Information, Health Status,
Health Information, Learning my Risk) and 12–15 contained
words pertinent to TAA-specific information avoidance (i.e.,
Knowing my TAA Risk, Learning My TAA Risk, TAA Feedback,
TAA Results, and Avoiding my TAA Risk, reverse coded). The
remainder of the task included an assortment of filler attitude
objects (e.g., Taking a Walk, Avoiding Sharks).
Researchers have used speeded self-report measures with sim-
ilar response windows to directly assess automatic attitudes (Bar-
Anan & Nosek, 2014;Ho, Sidanius, Levin, & Banaji, 2011;
Nosek, Bar-Anan, Sriram, Axt, & Greenwald, 2014). The research
commonly shows that speeded self-report tasks correlate well with
other automatic measures (e.g., the Implicit Association Test, the
Go/No-Go Association Task, Sorting Paired Features), and are
distinct from more controlled measures (e.g., self-report without
time pressure; Ranganath et al., 2008).
Overall, 21.8% of all responses were too slow and therefore not
recorded. We created an index of automatic avoidance preference
by averaging the results of the 24 –30 responses to health infor-
mation and TAA-information stimuli. We recoded both of these
indices, so that higher numbers reflected more “unfavorable” at-
titudes about learning health information (M⫽1.93, SD ⫽0.68).
After they completed the speeded self-report task, participants
completed a 10-item self-report/explicit measure of preference to
avoid health information (␣⫽.92) adapted from Howell and
Shepperd (2014). Example items include, “There is some infor-
mation that I would rather not learn about my health,” and “When
it comes to my health, sometimes ignorance is bliss” (1 ⫽strongly
disagree, 7⫽strongly agree;M⫽2.49, SD ⫽1.15). Prior
research has successfully used a version of this scale to measure
people’s self-reported desire to avoid information (Howell et al.,
2014), and the measures shows strong reliability and predicative
validity (typical associations with behavior range between r⫽.40
and r⫽.60; see Howell & Shepperd, 2014 for full discussion and
psychometric properties). After participants completed this final
measure, the experimenter conducted a thorough debriefing. Re-
searchers conducting the debriefing followed a script that asked
participants to guess the true nature of the study, explained the
deception in the study, and asked participants whether they had
thought that TAA was fictitious. No participants reported prior
knowledge of TAA deficiency, and all reported believing it was a
real disease.
Results
Overall, 32.6% of participants chose not to learn their risk for
TAA Deficiency. Implicit and explicit measures correlated posi-
tively, r(43) ⫽.40, CI
95%
⫽.11–.64, p⫽.008. The results of
simultaneous logistic regression suggested that participants who
indicated a greater preference for avoiding health information on
the implicit (b⫽2.61, SE ⫽1.06, Wald ⫽5.97, p⫽.02, OR ⫽
13.64, CI
95%
⫽1.68 –110.92) and explicit measures (b⫽1.36,
SE ⫽0.55, Wald ⫽6.14, p⫽.01, OR ⫽3.89, CI
95%
⫽
1.32–11.38) more often chose not to learn their risk for TAA
deficiency. The interaction between the implicit and explicit mea-
sures was not significant (b⫽⫺1.36, SE ⫽0.95, Wald ⫽1.98,
p⫽.16, OR ⫽0.26, CI
95%
⫽0.40 –1.69), suggesting that auto-
matic and explicit attitudes operated independently to produce
information avoidance.
Study 2
Study 1 demonstrated the role of automatic and explicit attitudes
in avoidance of a fictitious disease. In Study 2, we examined
whether these effects would replicate, that is, whether participants
would show avoidance of risk calculator feedback for an actual
disease.
Participants
Participants were 209 adults (112 men, 97 women) recruited via
Amazon.com’s Mechanical Turk
1
and paid $0.51 for their partic-
ipation. Participants’ ages ranged from 18 to 25 years (M⫽19.1
years, SD ⫽1.3). We aimed for 200 participants, to provide
enough power to detect a medium effect (w⫽.25) at .80 power.
Procedure and Measures
After consenting to participate, participants completed a risk
calculator for melanoma skin cancer adapted from an online risk
calculator (http://www.yourdiseaserisk.wustl.edu/). Participants
then completed in a counterbalanced order: (a) the speeded self-
report task described in Study 1, but tailored to melanoma skin
cancer (implicit measure; M⫽2.06, SD ⫽0.77), (b) two versions
of the self-report explicit measure described in Study 1— one
tailored to health information (e.g., “there is some information I
would rather not learn about my health”; ␣⫽.90) and one tailored
specifically to melanoma skin cancer (e.g., “I would rather not
learn my risk for melanoma skin cancer”; ␣⫽.89), which we
averaged together to create our explicit measure (M⫽2.87, SD ⫽
1.28), and (c) an item asking participants if they wished to seek or
avoid learning their risk calculator feedback. We counterbalanced
the order of the three measures to ensure that any effects we
observed were not the results of participants’ behavior (the deci-
sion about obtaining test results) influencing attitude reports (ei-
ther explicit or automatic) or vice versa. As in Study 1, the option
to seek was the default choice so that information avoidance
reflected an active choice.
Results
Prior to analysis, we eliminated all participants who provided
the same response to every item on the speeded self-report task,
including participants who skipped the task altogether and partic-
ipants who failed data quality checks (e.g., they answered some-
1
Amazon.com’s Mechanical Turk is a popular crowd-sourcing site
where researchers can post surveys for online workers to take. Consider-
able research suggests that although these samples are often, but not
always, Western, educated, industrialized, rich, and democratic—like most
samples in the behavioral sciences (Gosling, Sandy, John, & Potter, 2010;
Henrich, Heine, & Norenzayan, 2010)—these participants respond to stim-
uli and measures in ways similar to undergraduates and other Internet
samples and provide high-quality data (Buhrmester, Kwang, & Gosling,
2011).
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818 HOWELL, RATLIFF, AND SHEPPERD
thing other than strongly agree on an item that asked them to
answer strongly agree). Of the remaining 144 participants, 16.7%
avoided learning their melanoma skin cancer feedback.
Consistent with Study 1, implicit and explicit measures corre-
lated positively, r(141) ⫽.53, CI
95%
⫽.41–.64, p⫽.008. More-
over, logistic regression revealed that participants who indi-
cated a preference for health information avoidance on both
implicit (b⫽1.39, SE ⫽.66, Wald ⫽4.50, p⫽.03, OR ⫽
4.02, CI
95%
⫽1.11–14.50) and explicit measures (b⫽2.64,
SE ⫽.75, Wald ⫽12.51, p⬍.001, OR ⫽14.05, CI
95%
⫽
3.25–60.75) more often chose to avoid learning their risk for
TAA deficiency. Unlike in Study 1, the interaction between
implicit and explicit measures was significant, b⫽⫺1.18, SE ⫽
.51, Wald ⫽5.48, p⫽.02, OR ⫽.21, CI
95%
⫽0.11–.83. Thus, we
examined the role of automatic attitudes at high (⫹1SD) and low
(⫺1SD) levels of explicit information avoidance preference.
When explicit information avoidance preference was high, auto-
matic information avoidance preference did not uniquely predict
avoidance of melanoma risk calculator feedback (b⫽⫺.13, SE ⫽
.47, Wald ⫽0.07, p⫽.79, OR ⫽0.88, CI
95%
⫽0.25–2.20). By
contrast, when explicit information avoidance preference was low,
greater automatic information avoidance preference was associ-
ated with greater avoidance of melanoma risk calculator feedback
(b⫽2.91, SE ⫽1.22, Wald ⫽5.71, p⫽.02, OR ⫽18.29,
CI
95%
⫽1.69 –198.32). These results indicate that automatic in-
formation avoidance preference was particularly relevant to the
decision to avoid personal risk feedback when it was discrepant
from explicit information avoidance preference.
Study 3
Studies 1 and 2 demonstrate the role of implicit and explicit
attitudes in information avoidance with a correlational design.
Study 3 was designed to examine whether prompting people to
contemplate their motives for seeking versus avoiding information
might cause them to rely more on explicit than implicit attitudes.
Participants
Participants were 194 adults (101 men, 85 women, 8 unre-
ported) recruited via Amazon.com’s Mechanical Turk and paid
$0.51 for their participation. Participants’ ages ranged from 18 to
69 years (M⫽32.2 years, SD ⫽12.8). We initially recruited 200
participants, to provide enough power to detect a medium effect
(w⫽.25) at .80 power; however, 6 participants did not answer any
questions, resulting in 194 participants eligible for analysis.
Procedure and Measures
The procedure and measures were identical to Study 2 with two
exceptions. First, all items were tailored to heart disease, rather
than to melanoma skin cancer. Second, after completing the risk
calculator, participants were assigned to one of two conditions:
contemplation or no contemplation (as in Howell & Shepperd,
2013b). Participants in the contemplation condition listed “three
reasons” they/someone like them “should learn their risk for heart
disease” and “three reasons” they/someone like them “should not
learn their risk for heart disease,” in a counterbalanced order. Next,
we asked participants to rate the extent to which each of the
reasons they listed would be important in making the decision to
seek or avoid their risk (not at all important to extremely impor-
tant). Participants in the no contemplation condition listed “six
facts (pieces of information)” they knew “about heart disease.” As
in Study 2, the order of the tasks was counterbalanced across
participants.
Results
Prior to analysis, we eliminated all participants who provided
the same response to every item on the speeded self-report task,
including participants who skipped the task altogether or failed
data quality checks (e.g., they answered something other than
strongly agree on an item that asked them to choose strongly
agree). Of the remaining 174 participants, 25.3% avoided learning
their heart disease risk feedback. Replicating Howell and Shepperd
(2013b), participants were less likely to avoid learning their risk
for heart disease when they first contemplated their reasons for
seeking versus avoiding the information (16.9%) than when they
did not (33.0%),
2
(1, 174) ⫽5.96, p⫽.02, ⫽.19.
Participants’ attitudes did not differ between the contemplation
(M
explicit
⫽2.99, SD
explicit
⫽1.24; M
implicit
⫽1.93, SD
explicit
⫽0.74)
and no-contemplation conditions (M
explicit
⫽2.85, SD
explicit
⫽1.44;
M
implicit
⫽1.89, SD
explicit
⫽0.81), tvalues ⬍.61, pvalues ⬎.54, d
values ⬍.10. We examined the role of automatic attitudes in each
condition. Participants in the no-contemplation condition resembled
participants in Study 1. Specifically, we observed greater information
avoidance as explicit preference for information avoidance increased
(b⫽.71, SE ⫽.35, Wald ⫽4.20, p⫽.04, OR ⫽2.11, CI
95%
⫽
1.35–3.28), and a marginally significant effect of more information
avoidance as automatic preference for information avoidance in-
creased (b⫽.65, SE ⫽.36, Wald ⫽3.32, p⫽.07, OR ⫽1.91,
CI
95%
⫽0.95–3.83). The interaction between implicit and explicit
measures was not statistically significant (b⫽⫺.03, SE ⫽.26,
Wald ⫽0.11, p⫽.92, OR ⫽.97, CI
95%
⫽0.59 –1.61).
By contrast, and contrary to expectations, only automatic pref-
erence for avoidance of heart disease risk feedback predicted the
information avoidance decision, b⫽1.36, SE ⫽.59, Wald ⫽5.30,
p⫽.02, OR ⫽3.87, CI
95%
⫽1.22–12.28, among participants in
the contemplation condition (Explicit: b⫽0.53, SE ⫽.49,
Wald ⫽1.17, p⫽.28 OR ⫽1.69, CI
95%
⫽0.65– 4.38, Interac-
tion: b⫽0.66, SE ⫽.47, Wald ⫽0.02, p⫽.88, OR ⫽1.07,
CI
95%
⫽0.42–2.68), suggesting that contemplation manipulation
prompted participants to rely more on their automatic attitudes
when choosing to seek versus avoid their heart disease risk feed-
back.
Discussion
Three studies demonstrate an automatic component of informa-
tion avoidance. Specifically, to the extent that participants had
automatically unfavorable opinions regarding learning health in-
formation, they were more likely to avoid learning their risk for
TAA deficiency, melanoma skin cancer, and heart disease. Impor-
tantly, this relationship persisted even when controlling for explicit
self-report preferences to avoid health information. In Study 2, an
interaction between implicit and explicit measures emerged and
suggested that automatic preference to avoid information was
important in predicting the decision to avoid health information
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819
AUTOMATIC INFORMATION AVOIDANCE
primarily when automatic and controlled preferences to avoid
health information diverged. However, this pattern did not emerge
in the other two studies, suggesting these results should be inter-
preted cautiously.
Implications and Applications
These findings represent an important initial step in understand-
ing the role of automatic attitudes in information avoidance. To
date, most research has assumed that information avoidance is a
deliberative decision based on an analysis of threat (Shepperd &
Howell, 2015;Sweeny et al., 2010). The present results reveal that
while the avoidance behavior itself may be a deliberate decision, it
may be influenced by relatively automatic/uncontrolled processes.
Further, our findings suggest that earlier studies on health infor-
mation avoidance, which have solely examined motives for infor-
mation avoidance using traditional self-report measures, are lim-
ited in scope and should be expanded to assess automatic
preferences for information.
These results suggest that efforts to reduce health information
avoidance should target both automatic and controlled processes.
Dual process theories have long held that the outcomes of implicit
measures are driven by automatic process, whereas explicit eval-
uations incorporate more controlled processes, such as active in-
trospection and self-presentational concerns (Gawronski & Boden-
hausen, 2006,2011). Persuasive messages have been shown to
engage the type of controlled processing that is likely to influences
explicit attitudes (Eagly & Chaiken, 1993;Petty & Cacioppo,
2012) while the outcomes of implicit measures (i.e., automatic
attitudes) are typically assumed to result from associative pro-
cesses. Thus, interventions may need to specifically target these
associative processes to produce change in implicit attitudes
(Gawronski & Bodenhausen, 2011).
One intervention that appears useful in decreasing bias in auto-
matic attitudes is evaluative conditioning procedures (Lai, Hoff-
man, & Nosek, 2013) in which researchers pair positive stimuli
with attitude objects in an attempt to form a positive association
between the two (De Houwer, Thomas, & Baeyens, 2001). With
regard to seeking health information, interventionists might pair
pleasant stimuli with phrases like “seeking my risk.” The assump-
tion is that people will learn to associate information seeking with
positive feelings, thus reducing automatic preferences for infor-
mation avoidance and, ultimately, reducing avoidant behavior.
More broadly, any intervention that challenges people’s negative
automatic perceptions of seeking information or leads people to
associate seeking with positive automatic perception—the way
evaluative conditioning does—should similarly serve to promote
positive implicit attitudes and thereby reduce information avoid-
ance (Gawronski & Bodenhausen, 2006;Lai et al., 2013).
That said, it is important to note that recent evidence suggests
that there are circumstances under which direct, persuasive mes-
sages impact automatic attitudes. For example, reading a persua-
sive message about the positive aspects of eating vegetables in-
creased provegetable automatic attitudes (Horcajo, Briñol, &
Petty, 2010), and automatic attitudes toward a novel consumer
product were shown to be more positive when the source of the
persuasive message was high in expertise, trustworthiness, or
attractiveness (Smith & De Houwer, 2015;Smith, De Houwer, &
Nosek, 2013). What is of paramount importance to reducing health
information avoidance behaviors is that we design interventions
that specifically target both automatic and explicit attitudes, and
that we measure information avoidance evaluations using both
implicit and explicit measures. Otherwise, our interventions run
the risk of being impoverished from a theoretical standpoint and
ineffectual in their ability to influence behavior.
Of course, information avoidance is not always undesirable
(Sweeny et al., 2010). Although avoiding health information can
lead to delayed diagnosis, which typically results in poorer
disease-related outcomes (Centers for Disease Control and Preven-
tion, 2010;Howlader et al., 2012;Scott, Grunfeld, & McGurk,
2006), information avoidance can be tactically useful until one can
muster the resources necessary to manage bad news (Howell et al.,
2014), or to defend against unpleasant information with little
utility.
Unanswered Questions
Prompting people to contemplate their motives for seeking
versus avoiding their heart disease risk feedback (Study 3) reduced
risk feedback avoidance, replicating prior research (Howell &
Shepperd, 2013b). However, contrary to expectations, it appeared
to do so by prompting people to rely more, rather than less, on their
automatic attitudes. Indeed, we observed a marginally significant
tendency for implicit measure to predict information avoidance
beyond the explicit measure in the no contemplation condition, but
our implicit measure was the only predictor of information avoid-
ance in the contemplation condition. This unexpected finding
suggests that earlier hypothesizing that contemplation prompts
people to make decisions based on more controlled reasoning
processes may be inaccurate (Howell & Shepperd, 2013b).
We can think of several explanations for this unexpected find-
ing. One possibility is that the contemplation manipulation served
to reinforce participants’ gut reactions. Research suggests that
people who write about the reasons underlying their attitudes
become more confident and polarized in those attitudes (Chaiken
& Yates, 1985), and may gain an inaccurate illusion of self-insight
(Wilson et al., 1995;Wilson & LaFleur, 1995). Perhaps partici-
pants had a gut reaction to either seek or avoid information, and
contemplation amplified the influence of their gut reaction. Con-
sistent with this notion, research suggests that people with discrep-
ant implicit and explicit attitudes will use motivated reasoning to
think and will behave more in line with their implicit than their
explicit attitudes (Rydell, McConnell, & Mackie, 2008).
Contemplation might have also prompted people to rely less on
their explicit attitudes. Prior research suggests that when people
list reasons for their behaviors, they are subsequently less likely to
behave in line with their self-reported attitudes (Wilson & Dunn,
1986;Wilson, Kraft, & Dunn, 1989). This effect presumably
occurs because people become convinced that they feel differently
than they explicitly reported. That is, when people consider their
attitudes, they tend to embark on a biased line of reasoning—
selecting only certain information—which can dramatically in-
crease the salience of lines of thought that were inaccessible before
(Wilson & Dunn, 1986). Thus, thinking about their rationale for
seeking versus avoiding information in the present study may have
made reasons for seeking particularly salient and convinced par-
ticipants that they felt differently from how they reported they felt.
Therefore, their information avoidance decision reflected less their
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820 HOWELL, RATLIFF, AND SHEPPERD
explicit attitudes (which had changed) and more their gut reac-
tions, which is best captured by the implicit measure. Clearly,
future work is necessary to understand how contemplation influ-
ences information avoidance via attitudes.
The present study cannot speak to the causal role of attitudes in
information avoidance. Our goal was to examine the extent to which
people rely on automatic and controlled attitudes when making an
avoidance decision. Because this work is primarily correlational, we
cannot determine whether implicit or explicit attitudes cause people to
seek or avoid information. Indeed, it is possible that some third
variable (e.g., trait coping style) influenced both receptivity to infor-
mation and the decision to seek or avoid information. Research is
needed that independently manipulates implicit and explicit attitudes
to examine their role in information avoidance. Third, it remains to be
seen whether the present results replicate with other explicit and
implicit measures (e.g., the Approach-Avoidance Task; Rinck &
Becker, 2007).
Future research is also needed to understand the role of automatic
and explicit attitudes in other domains of health information avoid-
ance. In the present study, we chose to focus on avoidance of risk
calculator feedback because risk calculators are readily available on
the Internet, and unknown numbers of people may avoid them.
Although we know of no studies that have compared risk calculator
avoidance to other forms of information avoidance (e.g., avoiding the
results of an HIV test), we suspect that they share a desire to restrict
personal exposure to information. Thus, we suspect that the relation-
ships we observe for feedback from online risk calculators are also
true for other types of threatening information. Nevertheless, empir-
ical evidence is necessary to examine the relationship between differ-
ent types of avoidance, and we need additional research to explore the
generality of our findings to other types of information avoidance and
to other health information.
Finally, one possible limitation of the present study is that in both
Studies 2 and 3, we lost some participants to attrition. Unfortunately,
we have no way of knowing whether the participants who failed the
attention checks or skipped the task were systematically different
from the participants who did not. We only know that they were not
capable or attentive enough to follow instructions.
Conclusions
The present work offers an important initial step in understand-
ing the role of automatic attitudes in information avoidance. The
results provide clear evidence that both automatic and controlled
processes can affect the decision to seek or avoid health risk
information, and that situational factors (e.g., contemplation) may
influence the extent to which people rely on either or both. Re-
turning to our initial observation that millions of Americans ac-
tively avoid health information, the present study suggests that
these avoiders may be doing so, at least in part, because of an
automatic aversion to their feedback. As such, interventionists
aiming to increase health information seeking might fruitfully
target these automatic processes.
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Received March 2, 2015
Revision received October 26, 2015
Accepted November 10, 2015 䡲
Correction to Nanthakumar et al. (2016)
In the article “Are We Overestimating the Prevalence of Depression in Chronic Illness Using
Questionnaires? Meta-Analytic Evidence in Obstructive Sleep Apnoea” by Shenooka Nanthakumar,
Romola S. Bucks, and Timothy C. Skinner (Health Psychology, 2016, Vol. 35, No. 5, pp. 423– 432.
http://dx.doi.org/10.1037/hea0000280), “Cognitive items” should read “Cognition items” in column
1, line 7 of Table 2.
http://dx.doi.org/10.1037/hea0000394
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