Misinformation and its Correction: Cognitive Mechanisms and Recommendations for Mass Communication

Full text

1
Misinformation and its Correction: Cognitive Mechanisms and Recommendations
for Mass Communication
In. B. Southwell, E. A. Thorson, & L. Sheble. (Eds), Misinformation and Mass
Audiences. Austin, TX: University of Texas Press.
Briony Swire and Ullrich Ecker
School of Psychology
University of Western Australia
Contact: briony.swire-thompson@research.uwa.edu.au
Website: https://utpress.utexas.edu/books/southwell-thorson-sheble-misinformation-and-mass-audiences

2
Misinformation and its Correction: Cognitive Mechanisms and Recommendations for Mass
Communication
In 2007, a man in the United Kingdom posted a photograph on his website of a
“mummified fairy” which he created as an April Fools’ prank. After receiving 20,000 visitors to
the site in one day, he explicitly revealed that he had fabricated the scenario, yet many accused
him of covering up the truth and vehemently insisted that the fairy was real (“Fairy fool”, 2007).
This anecdote highlights a valid concern to mass communicators: regardless of how ridiculous
information seems, once it is in the public sphere, it can take on a life of its own and may never
be fully retractable.
It has become a societal norm that the media and the internet provide vast quantities of
information, placing the onus on the individual to sort fact from fiction. However, individuals
have limited time, cognitive resources, or motivation to understand complex topics such as
scientific findings or political developments, and misconceptions are commonplace.
Unfortunately, once inaccurate beliefs are formed, they are remarkably difficult to eradicate
(Ecker, Lewandowsky, Swire, & D. Chang, 2011a). Even after people receive clear and credible
corrections, misinformation continues to influence their reasoning: in cognitive psychology, this
is known as the continued influence effect of misinformation (H. Johnson & Seifert, 1994;
Lewandowsky, Ecker, Seifert, Schwarz, & Cook, 2012). The mummified fairy is a benign
example, but the ramifications can be serious. Belief in misinformation can adversely impact
decision making, and the continued influence effect has real-world implications in areas as
disparate as education, health, and the economy.
One prominent example is the misconception that the measles, mumps, rubella (MMR)
vaccine causes autism. This falsehood has been repeatedly—and convincingly—retracted by the

3
media and scientific community over a number of years since the original myth was
disseminated in a fraudulent article. Despite these debunking efforts, the myth has led to a drop
in vaccination rates, and an increase in vaccine-preventable disease (Poland & Spier, 2010). The
economic burden of 16 measles outbreaks in the US in 2011 alone has been estimated
somewhere between $2.7 million and $5.3 million (Ortega-Sanchez, Vijayaghavan, Barskey, &
Wallace, 2014). Thus, developing evidence-based recommendations on how to adequately
communicate corrections and minimize reliance upon inaccurate information is not only
important for individual decision making but also has ramifications for society as a whole.
The most important recommendation for both traditional mass media such as
newspaper and television, as well as more recent technologies such as Twitter—which have
essentially transformed ordinary citizens into would-be journalists—is to take greater care to
ensure that information is correct to begin with. However, this is not always realistic due to the
fast pace of modern information consumption and dissemination, and the fact that ordinary
citizens are not bound by rules of journalistic integrity. Social media is thus an ideal breeding
ground for the propagation and transition of misinformation, which can be exemplified by its
role in rumors surrounding the Boston Marathon bombing in 2013. For example, a well-
intentioned Reddit thread was created to help find the perpetrators, yet the accusation of an
innocent and deceased Brown University student subsequently went viral (Guzman, 2013).
Information shared through social media is usually disseminated without fact-checking, based
merely on its potential to elicit emotional responses or support a personally motivated argument
(Peters, Kashima, & Clark, 2009).
This chapter focuses on cognitive mechanisms and theories accounting for the
continued influence of misinformation. In particular, we will discuss what drives belief in

4
inaccurate information, why certain individuals are predisposed to refrain from belief change
even in the face of good corrective evidence, and how corrections can be designed to maximize
impact. We therefore provide six practical recommendations based upon our current knowledge
of cognitive processes. We first discuss theoretical accounts for the continued influence effect
such as mental models, dual processing theory, the necessity of co-activation of misinformation
and new information, and the impact of the information’s source. We then discuss individual
predispositions to the continued influence effect, in particular a person’s worldview and
skepticism.
Mental Models
When people initially encounter information, a situation model of integrated memory
representations is built, and this model is continuously updated as new information becomes
available and relevant (Bower & Morrow, 1990). If the required changes are small, they can be
integrated into the situational model incrementally (Bailey & Zacks, 2015), yet if a larger change
is required, a “global” update is necessary, which involves discarding the old mental model and
creating a new one (Kurby & Zacks, 2012). However, even if there are sufficient cognitive
resources to notice a difference between one’s mental model and the current environment, people
are often quite inadequate at assimilating new information or mapping it onto existing memory
representations (van Oostendorp, 2014). It is possible that the continued influence effect occurs
when people update incrementally when in fact a global update is called for. Reliance on
inaccurate information is less likely in instances when there is an alternative to replace the

5
inaccurate information in a person’s mental model, as a readily available alternative explanation
facilitates global updating (Ecker, Lewandowsky, Cheung, & Maybery, 2015).
A classic paradigm for studying the continued influence effect involves presenting
participants with fictitious scenarios involving the retraction of an event cause. One common
example is a narrative where negligent storage of gas cylinders is initially held responsible for
starting a warehouse fire, yet their presence is retracted shortly thereafter (H. Johnson & Seifert,
1994; Wilkes & Leatherbarrow, 1988). If participants are explicitly queried about the gas
cylinders, they typically acknowledge the gap in their understanding (i.e., a gap in their mental
event model) created by the earlier retraction, and correctly state that there were none. However,
when answering inferential reasoning questions regarding the event—such as “what was the
cause of the explosions?”—participants often still rely upon the outdated information. This
indicates that people prefer to have an inaccurate over an incomplete event model, which can
lead to reliance upon discredited information even after an explicit correction (Ecker,
Lewandowsky, & Apai, 2011b).
Recommendation 1: Providing Factual Alternatives
One of the most effective methods of correcting misinformation is to provide an
alternative factual cause or explanation to facilitate “switching out” the inaccurate information in
an individual’s initial situation model. For example, if people are told that it was not gas
cylinders that caused the warehouse fire, but that there was evidence of arson, people are
dramatically less likely to rely upon the original inaccurate information (H. Johnson & Seifert,
1994; Ecker, Lewandowsky, & Tang, 2010). The alternative explanation effectively plugs the
model gap left by the retraction. The alternative should ideally have the same explanatory
relevance as the misinformation it replaces, and it is important that it is plausible—in fact, if the

6
new information is more plausible and easy-to-understand than the original, updating is even
more efficient (Baadte & Dutke, 2012).
In the real world, providing an alternative explanation to ameliorate reliance upon
inaccurate information can be problematic, as often there is no available substitute—sometimes
all that can be said about a piece of misinformation is that it is not true. For example, if a person
is accused of a crime, they might simply turn out to be “not guilty” without an alternative suspect
being readily available. The lack of adequate alternatives can have profound ramifications. For
example, the ongoing rumors regarding missing Malaysian Airlines flight MH370, which
disappeared over the Indian Ocean in 2014, have proven difficult to retract: In the absence of
unequivocal evidence regarding what happened to the plane, traditional and social media was rife
with speculations that the plane was hijacked by terrorists or a suicidal pilot (e.g., Quest, 2016).
Arguably, belief in the hijacking speculation has been difficult to shift because a convincing
factual alternative has not been available.
Dual Process Theory: Strategic and Automatic Memory Processes
The notion that retractions create gaps in mental models is useful to understand the basic
phenomenon that is the continued influence effect. Invalidated information is not simply deleted
from memory—memory does not work like a whiteboard and retractions do not simply erase
misinformation. To explain why corrected misinformation is used during reasoning, some
theorists have focused on the memory processes governing information retrieval, where a
common assumption is that there are two separates types of memory retrieval, strategic and
automatic (Yonelinas, 2002).
Strategic memory processes are effortful and allow for the controlled recollection of the
information’s contextual details. Similar to the meta-data of a computer file, contextual details

7
include information about the information itself. This includes qualities such as the information’s
spatiotemporal context of encoding, source, and veracity (Frithsen & Miller, 2014). A person’s
ability to use strategic memory processes efficiently will depend upon factors such as effort,
motivation, the period of time since encoding, and age (e.g., Herron & Rugg, 2003). In contrast,
automatic processes are fast and relatively acontextual, and serve to quickly provide an
indication of memory strength or familiarity with an item or notion (Zimmer & Ecker, 2010).
Automatic retrieval processes can contribute to misinformation effects in two ways.
Firstly, the evaluation of a statement’s veracity is influenced by its familiarity; this is
problematic as information can be accepted as true just because it seems familiar. When
increased familiarity gives the illusion that information is valid, this is known as the illusory
truth effect (e.g., Begg, Anas, & Farinacci, 1992). Secondly, when questioned about an event or
otherwise cued, retracted misinformation can be automatically retrieved from memory without
any accompanying contextual details, and potentially without recalling that the information has
been retracted (cf. Ayers & Reder, 1998; Ecker et al., 2010). To illustrate, it has been argued that
once misinformation has been encoded and then retracted, a “negation tag” is linked to the
original memory representation (e.g., “Flight MH370 was hijacked–NOT TRUE”; cf. Gilbert,
Krull, & Malone, 1990). When queried about the topic, fast automatic memory processes might
simply retrieve the familiar claim, while strategic memory processes are required to retrieve the
negation tag and dismiss the familiar statement as untrue. If strategic memory processes are not
engaged, familiar claims are thus likely to be judged as true even after plausible retractions
(Dechene, Stahl, Hansen, & Wanke, 2010).

8
Recommendation 2: Boosting Retrieval of the Retraction, Not Familiarity of the Myth
The extent to which people engage their strategic memory processes can be actively
encouraged, and this can reduce misinformation effects. Ecker et al. (2010) found that presenting
participants with a pre-exposure warning detailing the continued influence effect greatly reduced
reliance on misinformation, and was as effective as providing a factual alternative. The authors
argued that warnings not only allowed individuals to more effectively tag misinformation as
false when encoding its retraction, but also boosted later recall of the retraction (or the “negation
tag”). The effect of warnings was investigated mainly for theoretical reasons, and providing a
pre-exposure misinformation warning will not be a viable option in most real world settings.
However, any incentive to engage in strategic memory processes should be useful, such as
boosting source-monitoring (Lindsay & M. Johnson, 1989; Poole & Lindsay, 2002).
Enhancing recollection is one way of reducing reliance on misinformation, but
circumventing the inflation of a misconception’s familiarity is potentially another way. This
involves minimizing unnecessary explicit repetition of misinformation. For example, an
educational pamphlet using a “myth-busting” format that repeats the myth before indicating that
it is false (e.g., “Flight MH370 was hijacked—FALSE”) can boost the familiarity of the
misconception, potentially increasing the risk that misconceptions are later mistakenly
remembered as being true. This misremembering of myths as facts was demonstrated by
Skurnik, Yoon, Park, and Schwarz (2005), as well as Peter and Koch (2016). In both these
studies, participants misremembered the originally false statements as true more often than
misremembering originally true statements as false. Additionally, Swire, Ecker, and
Lewandowsky (2016) found that retracting myths and affirming facts led to comparable belief
change initially (i.e., belief reduction for myths, belief increase for facts), but that belief change

9
was less sustained with myths over the course of a one-week period. In other words,
misinformation began to be “re-believed” while fact belief remained stable. Thus, where
possible, communicators should focus on the facts and explicit repetition of a myth should be
minimized if the retraction does not provide adequate information to allow people to revise their
understanding.
Co-activation of Misconception and Corrective Facts
Despite the theoretically motivated suggestion to avoid myth repetition, for practicality,
corrections usually do require repetition of the myth—the question then becomes how best to
execute this. As discussed previously, presentation of factual alternative information is
conducive to successful mental-model revision. Beyond that, several theoretical accounts have
proposed that the co-activation of inaccurate knowledge and newly encoded factual information
facilitates knowledge revision. Co-activation is believed to increase the likelihood that the
individual notices discrepancies between originally-held misconceptions and factual evidence,
and that they update their knowledge accordingly (Kendeou & van den Broek, 2007).
After a correction, both the outdated and new information may co-exist in memory, and
can both be activated by relevant cues (cf. Ayers & Reder, 1998). Thus, it is crucial for efficient
updating and knowledge revision that a sufficient amount and quality of factual information is
provided, and ideally, that the correction also explains the reasons as to why the misconception is
wrong (Seifert, 2002). Adding adequate detail to the new accurate information can systematically
strengthen the correction by slowly decreasing interference from the outdated information
(Kendeou, Smith, & O’Brien, 2013). This illustrates how when ample factual information is
available, misinformation can be used as an educational tool (Bedford, 2010).

10
Recommendation 3: Refutations of Misinformation as an Educational Tool
A refutation involves not only a statement that the misconception is false, but a
comprehensive explanation as to why it is incorrect (Hynd, 2001). The efficacy of refutations has
primarily been investigated in the field of education, and has often focused on the updating of
scientific misconceptions held by students in a classroom. A meta-analysis of 70 studies by
Guzzetti, Snyder, Glass, and Gamas (1993) indicated that corrections are most successful when
they include sufficient explanation as to why a misconception is false (and why the facts are
true). Other educational strategies aimed at reducing reliance on misinformation such as class
discussions, demonstrations, and non-refutational texts (which simply present the correct
information without a description of the misconception itself), are often successful in the short
term, but not after a delay (Guzetti, 2000).
It has been argued that the relative success of the refutation at promoting belief change is
that, by design, it increases the likelihood of the old and new information being co-activated in
memory (Kowalski & Taylor, 2009). It follows that when debunking a myth, its repetition seems
acceptable (despite the potential myth-familiarity boost) as long as (1) the repetition serves to
highlight a discrepancy between a misconception and factual evidence, thus promoting co-
activation, (2) the focus of the intervention can be shifted promptly from the myth to the factual
evidence, and (3) the target audience has the necessary resources—in particular in regards to
time and motivation—to engage with the provided materials and sees the information source as
credible, as would hopefully be the case in a classroom setting.
Retraction Source Credibility
People often do not have the time or inclination to be an expert in all fields, so most
knowledge, to a degree, is reliant upon accepting what someone else (or google) claims to be

11
true. Thus, people hold many opinions and beliefs about events and causal relationships without
having relevant involvement or expertise. For example, trust in climate scientists is a predictor of
whether or not an individual acknowledges that climate change is anthropogenic (Mase, Cho,
Prokopy, 2015). In general, high-credibility sources are more persuasive than low-credibility
sources (Eagly & Chaiken, 1993), and the lower one’s prior knowledge regarding a topic, the
more influential source credibility becomes (Jung, Walsh-Childers, & Kim, 2016). The two core
factors of source credibility discussed in the literature are (1) expertise—the extent to which the
source is capable of providing accurate information, and (2) trustworthiness—the perception that
the source is willing to provide information that the source itself believes to be accurate
(Pornpitakpan, 2004). A source can independently have varying degrees of these two qualities,
for example, a doctor may have a high degree of (perceived) expertise, but if found to be paid by
pharmaceutical companies may have relatively low (perceived) trustworthiness.
When it comes to retracting inaccurate information or belief change, intriguingly
trustworthiness seems to play a much larger role than expertise (McGinnes & Ward, 1980). For
example, Guillory and Geraci (2013) investigated the credibility of retraction source by
presenting participants with a story about a politician who was witnessed taking a bribe. This
was later retracted by people with varying degrees of trustworthiness and expertise. The authors
found that although trustworthiness was integral to the success of the retraction, expertise was
not. It should be noted that the way expertise was operationalized in this study was more akin to
“involvement in an event” rather than expertise in its perhaps more common meaning (i.e.,
“possessing relevant knowledge”). However, Ecker and Antonio (2016) replicated Guillory and
Geraci’s main finding with a more traditional interpretation of expertise and also found an effect
of trustworthiness but not expertise on the effectiveness of retractions.

12
Recommendation 4: Building Credibility
The ability to successfully correct misinformation appears to rely more upon the source’s
perceived honesty and integrity than its expertise. This means that Leonardo DiCaprio’s 2016
Oscar speech correcting climate-change misconceptions (Goldenberg, 2016) could be more
effective than an expert communication. Additionally, Paek, Hove, Jeong, and Kim’s (2011)
found that YouTube videos created by peers had more impact in terms of attitude change than
videos created by a non-profit organization. This means that social media can be an effective
vehicle for influencing others, and Facebook or Twitter posts may have more influence on
friends’ opinions than expert advice.
Ideally, and ethically, science communicators should aim to combine high
trustworthiness with high expertise. The quality and accuracy of the presented information will
influence how the source itself is perceived—this includes factors such as the information’s
presentation, plausibility, and whether or not it is supported by good examples (Jung et al. 2016;
Metzger, 2007). In general, perception of a source seems to be an iterative process in that the
more quality information is released, the greater the level of perceived credibility. In mass
communications in particular, basing claims on evidence, adequately referencing the evidence,
and presenting data in an easily accessible way to minimize misinterpretations—and doing this
consistently—will build credibility and thus contribute to a greater efficacy of corrections
(Gigerenzer, Gaissmaier, Kurz-Milcke, Schwartz, & Woloshin, 2007).
Worldview
If an individual holds a strong belief that is fundamental to their identity, even the most
credible source may not be able to shift it. A person’s ideology often influences how information
is sought out and evaluated, and if the information runs counter to prior beliefs, it is likely to be

13
ignored or more critically appraised (Wells, Reedy, Gastil, & Lee, 2009). This is known as
motivated reasoning (Kunda, 1990). Motivated reasoning can be compounded due to the
formation of ideological “echo-chambers,” where information is exchanged primarily amongst
people with similar viewpoints, such that corrections are less likely to reach the “target”
audience (Barbera, Jost, Nagler, Tucker, & Bonneau, 2015). This is fostered by social media,
where misinformation tends to circulate quicker than associated corrections (Shin, Jian, Driscoll,
& Bar, 2016).
Even if a correction reaches the misinformed target audience, simply providing the
correct information is inefficient, as continued reliance on misinformation is likely when the
misinformation conforms to a person’s pre-existing belief system, yet the correction does not
(Lewandowsky, Stritzke, Oberauer, & Morales, 2005s). Retracting misinformation that runs
counter to a person’s worldview can ironically even strengthen the to-be-corrected
misinformation, a phenomenon known as the worldview backfire effect; this has been
demonstrated when correcting misinformation surrounding contentious issues such as climate
change (Hart & Nisbet, 2012), or vaccine safety (Nyhan & Reifler, 2015). Worldview biases are
particularly difficult to overcome, as even neutral coverage of an issue can lead to polarization
(Jerit & Barabas, 2012).
Recommendation 5: Provide worldview- or self-affirming corrections
If a correction is regarding a contentious topic or politically sensitive subject matter, it is
beneficial to frame the correction in such a way that it is congruent with the person’s values in
order to reduce perceived threat (Kahan, 2010). For example, conservatives are more likely to
accept anthropogenic climate science if it is presented as a business opportunity for the nuclear
industry (Feygina, Jost, & Goldsmith, 2010). Additionally, in line with the above-mentioned

14
effects of source credibility, worldview congruence can potentially be conveyed through the
appropriate choice of messenger. Callaghan and Schnell (2009) found that attitudes towards gun
control were affected not only by the way the information was framed, but also the source of the
message. Participants who were presented an argument regarding the impacts of crime and
violence were 19% more likely to support gun control measures if the message came from a New
York Times journalist than if it was presented without a source. People also seem less defensive
regarding counter-attitudinal information when their self-worth is strengthened. For example,
Cohen, Aronson, and Steele (2000) demonstrated this effect of self-affirmation: participants who
had been instructed to write about a personal quality that made them feel good about themselves
were subsequently more likely to respond positively to evidence that challenged their beliefs
regarding the death penalty.
Skepticism
Rather than evidence-denial driven by motivated reasoning, skepticism is the awareness of
potential hidden agendas and a desire to accurately understand the evidence at hand (Mayo,
2015). Skepticism can reduce misinformation effects, as it leads to more cognitive resources
being allocated to the task of weighing up the veracity of both the misinformation and the
correction. For example, people rely less upon misinformation when given the task of fact
checking, looking for inconsistencies and correcting inaccuracies as they read a text (Rapp,
Hinze, Kohlhepp, & Ryskin, 2014). The increased deliberation over the accuracy of information
is often instigated when the information counters an individual’s worldview (Taber & Lodge,
2006). To illustrate, Lewandowsky et al. (2005) found that a greater degree of skepticism led to
better discounting of retracted real-world news reports, and DiFonzo, Beckstead, Stupak, &
Walders (2016) found that individuals with greater dispositional skepticism tended to believe

15
inaccurate rumors to a lesser extent. The ability to maintain doubt, question evidence and
scrutinize the original data—even when it aligns with one’s worldview—is conducive to
avoiding reliance on misinformation, but it is a difficult task. Thus, honing the skill of knowing
when to trust evidence, and when not to, can potentially have great benefits.
Recommendation 6: Fostering Skepticism
Skepticism is a quality that can be encouraged and even temporarily induced—for
example, negative mood increases skepticism and improves accuracy in detecting deceitful
communications (Forgas & East, 2008). There is also a growing movement suggesting that
evidence-based evaluation and critical thinking should formally be taught in schools. Schmaltz
and Lilienfeld (2014) suggested that activities such as asking students to identify pseudoscience
on campus and in the media could highlight the plethora of falsifiable claims in the public
sphere. Alternatively, the authors recommended activities where students create their own
pseudoscience to demonstrate and experience the ease with which anecdotal evidence or
“psychobabble” can be fabricated. Even examining real-world false advertising cases can be
educational, for example, investigating the Federal Trade Commission’s verdict to charge
Lumosity $2 million for claiming its brain training could protect against cognitive impairment, or
Dannon $21 million for claiming their yoghurt can prevent the flu (Lordan, 2010; Rusk, 2016).
Lastly, the ability to question causal illusions—the perception that one event caused another,
where in fact they are unrelated—can also be taught, and a better understanding about the
probability of an outcome, the probability of a cause, and cause-outcome coincidences can help
promote skepticism (Matute et al., 2015).

16
Conclusion
Assessing the accuracy of information can be a difficult task. In today’s fast-paced
society, mass communication and social media play a key role in the sharing and receiving of
current events. In reality, the public do not have time to investigate each claim they encounter in
depth; therefore, providing quality information is essential. In the aftermath of Brexit and the
2016 US election, where the political landscape was rife with misinformation and fake news
(Barthel, Mitchell, & Holcomb, 2016; McCann & Morgan, 2016), the ability to correct
inaccuracies has never seemed more pertinent. The six recommendations provided can serve as
guidelines for mass communication as to how best to retract the plethora of misinformation in
the public sphere. However, it is important to note that no corrective technique can reduce belief
to base level, as if the misinformation was never previously mentioned. In addition, even if
people do shift their opinion and acknowledge that information they previously believed to be
true is incorrect, they are unlikely to change their voting preferences or feelings towards political
candidates (Swire, Berinsky, Lewandowsky, & Ecker, 2016). Given what we know about
misinformation and its correction, communicators thus hold a great deal of responsibility to
ensure that the information initially released is as accurate as possible.

17
References
Ayers, M.S., & Reder, L.M. (1998). A theoretical review of the misinformation effect:
Predictions from an activation-based memory model. Psychonomic Bulletin & Review, 5,
1–21.
Baadte, C., & Dutke, S. (2012). Learning about persons: the effects of text structure and
executive capacity on conceptual change. European Journal of Psychology of Education,
28, 1045–1064. http://doi.org/10.1007/s10212-012-0153-2
Bailey, H.R., & Zacks, J.M. (2015). Situation model updating in young and older adults: Global
versus incremental mechanisms. Psychology and Aging, 30,232–244.
Barbera, P., Jost, J., Nagler, J., Tucker, J., & Bonneau, R. (2015). Tweeting from left to right: Is
online political communication more than an echo chamber? Psychological Science, 26,
1531-1542.
Barthel, M., Mitchell, A., & Holcomb, J. (2016) Many Americans believe fake news is sowing
confusion. Pew Research Center, Available from http://www.journalism.org/2016/12/15/
many-americans-believe-fake-news-is-sowing-confusion/
Bedford, D. (2010). Agnotology as a teaching tool: Learning climate science by studying
misinformation. Journal of Geography, 109, 159–165.
Begg, I..M., Anas, A., & Farinacci, S. (1992). Dissociation of processes in belief: Source
recollection, statement familiarity, and the illusion of truth. Journal of Experimental
Psychology: General, 121,446-458.
Bower, G.H., & Morrow, D.G. (1990). Mental models in narrative comprehension. Science,
247,44–48.

18
Callaghan, K.C., & Schnell, F. (2009). Who says what to whom: Why messengers and citizen
beliefs matter in social policy framing. The journal of Social Science, 46,12-28.
Cohen, G.L., Aronson, J., & Steele, C.M. (2000). When beliefs yield to evidence: Reducing
biased evaluation by affirming the self. Personality and Social Psychology Bulletin,
26,1151–1164.
Dechene, A., Stahl, C., Hansen, J., & Wanke, M. (2010). The truth about the truth: A meta-
analytic review of the truth effect. Personality and Social Psychology Review, 14,238–
257.
DiFonzo, N., Beckstead, J.W., Stupak, N., & Walders, K. (2016). Validity judgments of rumors
heard multiple times: the shape of the truth effect. Social Influence,11,22–39.
Eagly, A. H. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich
College Publishers.
Ecker, U.K.H., & Antonio, L. (2016). Source credibility and the continued influence effect.
Unpublished manuscript.
Ecker, U.K.H., Lewandowsky, S., & Apai, J. (2011b). Terrorists brought down the plane!—No,
actually it was a technical fault: Processing corrections of emotive information. The
Quarterly Journal of Experimental Psychology, 64,283–310.
Ecker, U.K.H., Lewandowsky, S., Cheung, C.S.C., & Maybery, M.T. (2015). He did it! She did
it! No, she did not! Multiple causal explanations and the continued influence of
misinformation. Journal of Memory and Language, 85,101–115.
Ecker, U.K.H., Lewandowsky, S., Swire, B., & Chang, D. (2011a). Correcting false information
in memory: Manipulating the strength of misinformation encoding and its retraction.
Psychonomic Bulletin & Review, 18,570–578.

19
Ecker, U.K.H., Lewandowsky, S., & Tang, D.T.W. (2010). Explicit warnings reduce but do not
eliminate the continued influence of misinformation. Memory & Cognition, 38,1087–
1100.
Fairy fool sparks huge response. (2007, April 1). BBC. Retrieved from
http://news.bbc.co.uk/2/hi/uk_news/england/derbyshire/6514283.stm
Feygina, I., Jost, J.T., & Goldsmith, R.E. (2010). System justification, the denial of global
warming, and the possibility of “system-sanctioned change.” Personality and Social
Psychology Bulletin, 36,326–338.
Forgas, J.P., & East, R. (2008). On being happy and gullible: Mood effects on skepticism and the
detection of deception. Journal of Experimental Social Psychology, 44,1362–1367.
Frithsen, A., & Miller, M.B. (2014). The posterior parietal cortex: Comparing remember/know
and source memory tests of recollection and familiarity. Neuropsychologia, 61, 31–44.
Gilbert, D.T., Krull, D.S., & Malone, P.S. (1990). Unbelieving the unbelievable: Some problems
in the rejection of false information. Journal of Personality and Social Psychology, 59,
601.
Gigerenzer, G., Gaissmaier, W., Kurz-Milcke., E., Schwartz L.M., & Woloshin S. (2007).
Helping Doctors and Patients Make Sense of Health Statistics. Psychological Science in
the Public Interest, 8,53-96.
Goldenburg, S. How Leonardo DiCaprio became one of the world's top climate change
champions. The Guardian. Retrieved from
http://www.theguardian.com/environment/2016/feb/29/how-leonardo-dicaprio-oscar-
climate-change-campaigner

20
Guillory, J.J., & Geraci, L. (2013). Correcting erroneous inferences in memory: The role of
source credibility. Journal of Applied Research in Memory and Cognition, 2, 201–209.
Guzman, M. (2013). After Boston, Still learning. Quill, 101,22-25.
Guzzetti, B.J. (2000). Learning counter-intuitive science concepts: what have we learned from
over a decade of research? Reading & Writing Quarterly, 16, 89–98.
Guzzetti, B.J., Snyder, T.E., Glass, G.V., & Gamas, W.S. (1993). Promoting conceptual change
in science: A comparative meta-analysis of instructional interventions from reading
education and science education. Reading Research Quarterly, 28,117–159.
Hart, P.S., & Nisbet, E.C. (2012). Boomerang effects in science communication: How motivated
reasoning and identity cues amplify opinion polarization about climate mitigation
policies. Communication Research, 39, 701–723.
Herron, J.E., & Rugg, M.D. (2003). Strategic influences on recollection in the exclusion task:
Electrophysiological evidence. Psychonomic Bulletin & Review, 10,703–710.
Hynd, C.R. (2001). Refutational texts and the change process. International Journal of
Educational Research, 35,699–714.
Jerit, J., & Barabas, J. (2012). Partisan perceptual bias and the information environment. Journal
of Politics, 74,672–1684.
Johnson, H.M., & Seifert, C.M. (1994). Sources of the continued influence effect: When
misinformation in memory affects later inferences. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 20,1420–1436.
Jung, E.H., Walsh-Childers, K., & Kim, H.S. (2016). Factors influencing the perceived
credibility of diet-nutrition information web sites. Computers in Human Behavior, 58,37–
47.

21
Kahan, D. (2010). Fixing the communications failure. Nature, 463, 296–297.
Kendeou, P., & van den Broek, P. (2007). The effects of prior knowledge and text structure on
comprehension processes during reading of scientific texts. Memory & Cognition, 35,
1567–1577.
Kendeou, P., Smith, E. R., & O’Brien, E.J. (2013). Updating during reading comprehension:
Why causality matters. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 39, 854–865.
Kowalski, P., & Taylor, A.K. (2009). The effect of refuting misconceptions in the introductory
psychology class. Teaching of Psychology, 36, 153–159.
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108, 480-498.
Kurby, C.A., & Zacks, J. M. (2012). Starting from scratch and building brick by brick in
comprehension. Memory & Cognition, 40,812–826.
Lewandowsky, S., Ecker, U.K.H., Seifert, C.M., Schwarz, N., & Cook, J. (2012).
Misinformation and its correction: Continued influence and successful debiasing.
Psychological Science in the Public Interest, 13, 106–131.
Lewandowsky, S., Stritzke, W.G.K., Oberauer, K., & Morales, M. (2005). Memory for fact,
fiction, and misinformation: The Iraq war 2003. Psychological Science, 3, 190.
Lindsay, D. S., & Johnson, M. K. (1989). The eyewitness suggestibility effect and memory for
source. Memory & Cognition, 17, 349–358.
Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology,
55, 232–257.

22
Lordan, B. (2010). Dannon agrees to drop exaggerated health claims for Activia yogurt and
DanActive dairy drink. Federal Trade Commission. Retrieved from
https://www.ftc.gov/news-events/press-releases/2010/12/dannon-agrees-drop-
exaggerated-health-claims-activia-yogurt
Mase, A.S., Cho, H., & Prokopy, L.S. (2015). Enhancing the Social Amplification of Risk
Framework (SARF) by exploring trust, the availability heuristic, and agricultural
advisors’ belief in climate change. Journal of Environmental Psychology, 41,166–176.
Matute, H., Blanco, F., Yarritu, I., Diaz-Lago, M., Vadillo, M., & Berberia, I. (2015). Illusions of
causality: how they bias our everyday thinking and how they could be reduced. Frontiers
in Psychology, 1, 1-13.
Mayo, R. (2015). Cognition is a matter of trust: Distrust tunes cognitive processes. European
Review of Social Psychology, 26, 283–327.
McCann, K. & Morgan, T. (2016) Nigel Farage: £350 million pledge to fund the NHS was 'a
mistake'. The Telegraph. Available from
http://www.telegraph.co.uk/news/2016/06/24/nigel-farage-350-million-pledge-to-fund-
the-nhs-was-a-mistake/
McGinnes, E., & Ward, C. (1980). "Better liked than right": Trustworthiness and expertise in
credibility. Personality and Social Psychology Bulletin, 6, 67-472.
Metzger, M.J. (2007). Making sense of credibility on the Web: Models for evaluating online
information and recommendations for future research. Journal of the American Society
for Information Science and Technology, 58, 2078–2091.
Nyhan, B., & Reifler, J. (2015). Does correcting myths about the flu vaccine work? An
experimental evaluation of the effects of corrective information. Vaccine, 33, 459–464.

23
Ortega-Sanchez, I.R., Vijayaraghavan, M., Barskey, A.E., & Wallace, G.S. (2014). The
economic burden of sixteen measles outbreaks on United States public health
departments in 2011. Vaccine, 32, 1311–1317.
Paek, H.J., Hove, T., Jeong, H., Kim, M. (2011). Peer or expert? The persausive impact of
YouTube public service announcement producers. International Journal of Advertising,
30,161-188.
Peter, C., & Koch, T. (2016). When debunking scientific myths fails (and when it does not): The
backfire effect in the context of journalistic coverage and immediate judgments as
prevention strategy. Science Communication, 38,3–25.
Peters, K., Kashima, Y., & Clark, A. (2009). Talking about others: Emotionality and the
dissemination of social information. European Journal of Social Psychology, 39,207–
222.
Poland, G. A., & Spier, R. (2010). Fear, misinformation, and innumerates: How the Wakefield
paper, the press, and advocacy groups damaged the public health. Vaccine, 28, 2361–
2362.
Poole, D.A., & Lindsay, D.S. (2002) Reducing child witnesses' false reports of misinformation
from parents. Journal of Child Psychology, 81,117-40.
Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five
decades’ evidence. Journal of Applied Social Psychology, 34,243–281.
Quest, R. (2016). MH370: Did the pilots do it? CNN. Retrieved from
http://www.cnn.com/2016/03/07/asia/mh370-quest-pilots/.
Rapp, D.N., Hinze, S.R., Kohlhepp, K., & Ryskin, R.A. (2014). Reducing reliance on inaccurate
information. Memory & Cognition, 42,11–26.

24
Rusk, M. (2016). Lumosity to Pay $2 Million to Settle FTC Deceptive Advertising Charges for
Its “Brain Training” Program. Federal Trade Commission. Retrieved from
https://www.ftc.gov/news-events/press-releases/2016/01/lumosity-pay-2-million-settle-
ftc-deceptive-advertising-charges from
Schmaltz, R., & Lilenfeld, S. (2014) Hauntings, homeopathy, and the Hopkinsville Goblins:
using pseudoscience to teach scientific thinking. Frontiers in Psychology, 5, 1-5.
Seifert, C. M. (2002). The continued influence of misinformation in memory: What makes a
correction effective? Psychology of Learning and Motivation: Advances in Research and
Theory, 41,265-292.
Shin, J., Jian, L., Driscoll, K., & Bar, F. (2016). Political rumoring on Twitter during the 2012
US presidential election: Rumor diffusion and correction. New media and society,1-22.
Skurnik, I., Yoon, C., Park, D.C., & Schwarz, N. (2005). How warnings about false claims
become recommendations. Journal of Consumer Research, 31,713–724.
Swire, B., Berinsky, A., Lewandowsky, S., & Ecker, U. K. H. (2017). Processing political
misinformation—Comprehending the Trump phenomenon. Royal Society Open Science,
4, https://doi.org/10.1098/rsos.160802
Swire, B., Ecker, U. K. H., & Lewandowsky, S. (2017). The role of familiarity in correcting
inaccurate information. Journal of Experimental Psychology: Learning, Memory, and
Cognition.
Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs.
American Journal of Political Science, 50, 755–769. http://doi.org/10.1111/j.1540-
5907.2006.00214.x

25
Van Oostendorp, H. (2014). The ambivalent effect of focus on updating mental representations.
In D. N. Rapp & J. L. Braasch (Eds). Processing inaccurate information and applied
perspectives from cognitive science and the educational sciences (pp. 223-244).
Cambridge, MA: MIT Press.
Wells, C., Reedy, J., Gastil, J., & Lee, C. (2009). Information distortion and voting choices: The
origins and effects of factual beliefs in initiative elections. Political Psychology, 30,953-
969.
Wilkes, A.L., & Leatherbarrow, M. (1988). Editing episodic memory following the identification
of error. Quarterly Journal of Experimental Psychology: Human Experimental
Psychology, 52,165-183.
Yonelinas, A.P. (2002). The nature of recollection and familiarity: A review of 30 years of
research. Journal of Memory and Language, 46,441–517.
Zimmer, H.D., & Ecker, U.K.H. (2010). Remembering perceptual features unequally bound in
object and episodic tokens: Neural mechanisms and their electrophysiological correlates.
Neuroscience & Biobehavioral Reviews, 34,1066–1079.