Knowledge Does Not Protect Against Illusory Truth
Lisa K. Fazio
Vanderbilt University
Nadia M. Brashier
Duke University
B. Keith Payne
University of North Carolina at Chapel Hill
Elizabeth J. Marsh
Duke University
In daily life, we frequently encounter false claims in the form of consumer advertisements, political
propaganda, and rumors. Repetition may be one way that insidious misconceptions, such as the belief that
vitamin C prevents the common cold, enter our knowledge base. Research on the illusory truth effect
demonstrates that repeated statements are easier to process, and subsequently perceived to be more
truthful, than new statements. The prevailing assumption in the literature has been that knowledge
constrains this effect (i.e., repeating the statement “The Atlantic Ocean is the largest ocean on Earth” will
not make you believe it). We tested this assumption using both normed estimates of knowledge and
individuals’ demonstrated knowledge on a postexperimental knowledge check (Experiment 1). Contrary
to prior suppositions, illusory truth effects occurred even when participants knew better. Multinomial
modeling demonstrated that participants sometimes rely on fluency even if knowledge is also available
to them (Experiment 2). Thus, participants demonstrated knowledge neglect, or the failure to rely on
stored knowledge, in the face of fluent processing experiences.
Keywords: illusory truth, fluency, knowledge neglect
Supplemental materials: http://dx.doi.org/10.1037/xge0000098.supp
We encounter many misleading claims in our daily lives, some
of which have the potential to affect important decisions. For
example, many people purchase “toning” athletic shoes to improve
their fitness or take preventative doses of vitamin C to avoid
contracting a cold. How do such misconceptions enter our knowl-
edge base and inform our choices? One key factor appears to be
repetition: Repeated statements receive higher truth ratings than
new statements, a phenomenon called the illusory truth effect.
Since Hasher, Goldstein, and Toppino’s (1977) seminal study,
cognitive, social, and consumer psychologists have replicated the
basic effect dozens of times.
The illusory truth effect is robust to many procedural variations.
Although most studies use obscure trivia statements (e.g., Bacon,
1979), the effect also occurs for assertions about consumer prod-
ucts (Hawkins & Hoch, 1992; Johar & Roggeveen, 2007) and for
sociopolitical opinions (Arkes, Hackett, & Boehm, 1989). Illusory
truth occurs when people are only exposed to general topics (e.g.,
hen’s body temperature), then later asked to evaluate specific
statements (e.g., “The temperature of a hen’s body is about
104°F;” Begg, Armour, & Kerr, 1985; see also Arkes, Boehm, &
Xu, 1991). The effect emerges after delays of minutes (e.g., Begg
& Armour, 1991; Schwartz, 1982), weeks (Bacon, 1979; Giger-
enzer, 1984), and months (Brown & Nix, 1996). Moreover, Gig-
erenzer (1984) replicated the effect outside of the laboratory set-
ting using representative samples and naturalistic stimuli.
Recent work suggests that the ease with which people compre-
hend statements (i.e., processing fluency) underlies the illusory
truth effect. Repetition makes statements easier to process (i.e.,
fluent) relative to new statements, leading people to the (some-
times) false conclusion that they are more truthful (Unkelbach,
2007; Unkelbach & Stahl, 2009). Indeed, illusory truth effects
arise even without prior exposure—people rate statements pre-
sented in high-contrast (i.e., easy-to-read) fonts as “true” more
often than those presented in low-contrast fonts (Reber & Schwarz,
1999; Unkelbach, 2007). Fluency informs a variety of judgments
(e.g., liking, confidence, frequency; see Alter & Oppenheimer,
2009; Iyengar & Lepper, 2000; Schwartz & Metcalfe, 1992; Tver-
sky & Kahneman, 1973), likely because it serves as a valid cue in
our day-to-day lives (Unkelbach, 2007).
Given the strong, automatic tendency to rely on fluency, when
do people use other cues to evaluate truthfulness? The chief
constraint on illusory truth identified in the literature is source
recollection. Begg, Anas, and Farinacci (1992), for example,
paired statements with “trustworthy” or “untrustworthy” voices. At
test, statements previously spoken by untrustworthy voices re-
This article was published Online First August 24, 2015.
Lisa K. Fazio, Department of Psychology and Human Development,
Vanderbilt University; Nadia M. Brashier, Department of Psychology and
Neuroscience, Duke University; B. Keith Payne, Department of Psychol-
ogy, University of North Carolina at Chapel Hill; Elizabeth J. Marsh,
Department of Psychology and Neuroscience, Duke University.
This research was supported by a Collaborative Activity award from the
James S. McDonnell Foundation (Elizabeth J. Marsh). A National Science
Foundation Graduate Research Fellowship supported Nadia M. Brashier.
Correspondence concerning this article should be addressed to Lisa K.
Fazio, 230 Appleton Place #552, Vanderbilt University, Nashville, TN
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Experimental Psychology: General © 2015 American Psychological Association
2015, Vol. 144, No. 5, 993–1002 0096-3445/15/$12.00 http://dx.doi.org/10.1037/xge0000098
993
ceived lower truth ratings than new items (i.e., reverse illusory
truth effect). Multinomial process modeling shows that people rely
on fluency only if they fail to recollect whether or not a statement
came from a credible source (Unkelbach & Stahl, 2009). However,
people may not spontaneously retrieve source information (R. L.
Marsh, Landau, & Hicks, 1997); Henkel and Mattson (2011) found
that after a 2–3 week delay, participants exhibited an illusory truth
effect for statements that they later identified (correctly or incor-
rectly) as coming from an unreliable source. This result fits with
the well-documented finding that much of the information in the
knowledge base comes to mind relatively automatically, without
the experience of reliving the original learning event (Barber,
Rajaram, & Marsh, 2008; Conway, Gardiner, Perfect, Anderson, &
Cohen, 1997; Dewhurst, Conway, & Brandt, 2009; Herbert &
Burt, 2001, 2003, 2004; Merritt, Hirshman, Zamani, Hsu, & Ber-
rigan, 2006).
The other assumed constraint on the illusory truth effect is
pre-experimental knowledge. A recent meta-analytic review cap-
tures the literature as follows: “Statements have to be ambiguous,
that is, participants have to be uncertain about their truth status
because otherwise the statements’ truthfulness will be judged on
the basis of their knowledge” (Dechêne, Stahl, Hansen, & Wänke,
2010, p. 239). Very few studies directly test this assumption,
perhaps because it is so intuitive and widespread. Unkelbach and
Stahl (2009) tested their multinomial model with obscure materials
(knowledge parameter probabilities ranged from .01 to .05), be-
cause the authors presupposed, but did not test, a strong negative
relationship between knowledge and illusory truth. An abundance
of empirical work demonstrates that fluency affects judgments of
new information, but how does fluency influence the evaluation of
information already stored in memory?
Several studies indirectly examined the role of knowledge by
testing domain experts: That is, do people with more knowledge
about a particular topic show an illusory truth effect in that
domain? Unfortunately, different studies yielded different answers
to this question. For example, Srull (1983) asked self-rated car
experts and nonexperts to rate trivia statements about cars (e.g.,
“The Cadillac Seville has the best repair record of any American
made automobile”). No statistics were reported, but experts pro-
duced a numerically smaller illusory truth effect than nonexperts.
Parks and Toth (2006) found similar results when participants
rated claims about known and unknown companies (e.g., Chap-
Stick vs. Raven’s). Claims embedded in meaningful contexts (flu-
ent) received higher truth ratings than those in irrelevant contexts
(disfluent); critically, the illusory truth effect was more pro-
nounced for unknown than known brands. In contrast to these two
studies, Arkes, Hackett, and Boehm (1989) demonstrated that
expertise increased susceptibility to the illusion. They exposed
participants to statements from seven domains (e.g., food, litera-
ture, entertainment), then asked them to rank order their knowl-
edge about these topics. Illusory truth occurred for statements from
high-expertise domains, but not for statements from low-expertise
domains. Similar conclusions were drawn from a study where
psychology majors and nonmajors rated statements about psychol-
ogy (Boehm, 1994). Psychology majors exhibited a larger illusory
truth effect than nonmajors, corroborating Arkes and colleagues’
finding that domain knowledge can hurt rather than help.
However, these studies on expertise targeted specific facts that
participants would not know, even the domain experts. In other
words, they tested the effect of related knowledge, rather than that
of knowledge for individual statements, so it is unclear what
conclusions to draw. Only one study addressed the role of knowl-
edge for specific facts, rather than broad domains. Unkelbach
(2007) conducted a study of perceptual fluency with known (e.g.,
“Aristotle was a Japanese philosopher”) and unknown (e.g., “The
capital of Madagascar is Toamasina”) items. Some statements
appeared in high-contrast font colors (i.e., fluent), whereas others
appeared in low-contrast font colors (i.e., disfluent). Replicating
previous research (Reber & Schwarz, 1999), participants rated
fluent items as “true” more often than disfluent items. The inter-
action between fluency and knowledge was not significant, with
similar trends for known and unknown items. When tested sepa-
rately, however, illusory truth occurred for unknown, but not
known, statements. Ceiling effects in the known condition (i.e.,
strong bias to respond “true”) render these data inconclusive.
To summarize, illusory truth generalizes across a remarkably
wide range of factors. In the absence of source recollection, the
only constraint commonly identified is that participants must lack
knowledge about the statements’ veracity. In two experiments, we
evaluate the claim that illusory truth effects do not occur if people
can draw upon their stored knowledge. We used two types of
statements: contradictions of well-known facts and contradictions
of facts unknown to participants. We defined knowledge using
Nelson and Narens’s (1980) norms, as well as individuals’ perfor-
mance on a postexperimental knowledge check (Experiment 1). In
addition, we created two competing multinomial models of the
way people evaluate statements’ truthfulness. The knowledge-
conditional model reflects the assumption that people search mem-
ory for relevant information, only relying on fluency if this search
is unsuccessful. The fluency-conditional model, on the other hand,
posits that people can rely solely on fluency, even if stored
knowledge is available to them. We tested the fit of these models
of illusory truth using binary data (Experiment 2).
Experiment 1
Method
Participants. Forty Duke University undergraduates partici-
pated in exchange for monetary compensation. Participants were
tested individually or in small groups of up to five people.
Design. The experiment had a 2 (repetition: repeated, new) ⫻
2 (estimated knowledge: known, unknown) within-subjects design.
Both factors were counterbalanced across participants.
Materials. We selected 176 questions from Nelson and Na-
rens’s (1980) general knowledge norms, half of which were likely
to be known (on average, answered correctly by 60% of norming
participants) and half of which were likely to be unknown (an-
swered correctly by only 5% of norming participants).
1
These
norms likely underestimate how many facts participants can clas-
sify as true or false, because the norming study required partici-
pants to produce answers to open-ended questions. For each ques-
tion, we created a truth (e.g., “Photosynthesis is the name of the
process by which plants make their food”) and a matching false-
1
At the time of data collection, Tauber, Dunlosky, Rawson, Rhodes, and
Sitzman’s (2013) updated norms were not yet published.
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994
FAZIO, BRASHIER, PAYNE, AND MARSH
hood that referred to a plausible, but incorrect, alternative (e.g.,
“Chemosynthesis is the name of the process by which plants make
their food”). This resulted in four item types: known truths, known
falsehoods, unknown truths, and unknown falsehoods. To be clear,
a “known falsehood” refers to a contradiction of a fact stored in
memory; participants did not receive explicit labels or any other
indications that specific statements were true or false. Sample
statements can be seen in Table 1.
We divided both the known and unknown items into four sets of
22 statements. Two known and two unknown sets appeared as
truths, and the remainder appeared as falsehoods; furthermore, half
of the truths and falsehoods repeated across exposure and truth
rating phases, whereas the other half appeared for the first time
during the truth rating phase. Given our interest in how people
evaluate false claims, we limited our analyses to falsehoods and
treated truths as fillers. In addition, responses to known truths
averaged “probably true,” leaving little room for repetition to bias
judgments.
Procedure. After giving informed consent, participants com-
pleted the first phase of the experiment, the exposure phase.
Participants rated 88 statements for subjective interest, using a
6-point scale labeled 1 ⫽ very interesting,2⫽ interesting,3⫽
slightly interesting,4⫽ slightly uninteresting,5⫽ uninteresting,
and 6 ⫽ very uninteresting. The experimenter informed partici-
pants that their ratings would guide stimulus development for
future experiments, and that some statements were true and others
false.
Immediately after exposure, participants completed the second
part of the experiment, the truth rating phase. In addition to the
warning that they would encounter true and false statements, the
experimenter told participants that some statements appeared ear-
lier in the experiment, while others were new. Participants rated
176 statements for truthfulness, using a scale labeled 1 ⫽ definitely
false,2⫽ probably false,3⫽ possibly false,4⫽ possibly true,
5 ⫽ probably true, and 6 ⫽ definitely true.
The norms provide useful information about which questions
are relatively easy or difficult for participants, as documented in
numerous studies (Eslick, Fazio, & Marsh, 2011; Marsh & Fazio,
2006; Marsh, Meade, & Roediger, 2003). However, the norms
cannot predict with perfect accuracy what each individual knows.
To address this concern, we borrowed a procedure used in other
experiments (e.g., Kamas, Reder, & Ayers, 1996). Following the
exposure and truth rating phases, participants completed the
knowledge check phase. They answered 176 multiple-choice ques-
tions with three response options: the correct answer, the alterna-
tive from the false version of each statement, and “don’t know.”
For example, the answer options “Atlantic,” “Pacific,” and “don’t
know” accompanied the question “What is the largest ocean on
Earth?”
Results
The alpha level for all statistical tests was set to .05. As dis-
cussed above, we focused our analyses primarily on falsehoods.
Knowledge check. We first assessed knowledge check per-
formance, to ensure adequate proportions of known and unknown
items across participants. Overall, participants answered 44% of
the knowledge check questions correctly (known items). They
responded to 12% of the questions with falsifications and to
another 44% with “don’t know.” Collapsing across these response
types, 56% of the items were unknown. The high “don’t know”
rate indicates that correct answers corresponded to actual knowl-
edge, rather than guesses. If anything, the knowledge check un-
derestimates people’s knowledge, because viewing the false ver-
sion of a statement may bias people to later choose the wrong
answer (Bottoms, Eslick, & Marsh, 2010; Kamas et al., 1996).
Results of the knowledge check confirmed estimates of knowledge
based on norms. Participants correctly answered 67% of the questions
estimated by the norms to be known and 20% of those estimated to be
unknown (compared to 60% and 5%, respectively, in the original
norming study). Participants indicated “don’t know” of 23% of the
questions estimated to be known and for 65% of those estimated to be
unknown, again suggesting that participants did not guess.
Truth ratings. We analyzed truth ratings as a function of both
(a) individual knowledge check performance and (b) norm-based
estimates of knowledge. To preview, these analyses returned very
similar results.
Truth ratings as a function of demonstrated knowledge. We
conducted a 2 (repetition: repeated, new) ⫻ 2 (demonstrated
knowledge: known, unknown) repeated-measures analysis of vari-
ance (ANOVA) on participants’ truth ratings of falsehoods. The
number of known and unknown items varied for each participant,
depending upon his or her knowledge check performance. Every
participant’s data included a minimum of five trials per cell, and
the average trial count per cell was 22. The relevant data appear in
Figure 1A. Replicating the illusory truth effect, repeated false-
hoods (M ⫽ 3.53) received higher truth ratings than new ones
(M ⫽ 3.26), F(1, 39) ⫽ 13.06, MSE ⫽ .23, p ⫽ .001,
p
2
⫽ .25. As
expected, known falsehoods (M ⫽ 2.76) received lower (i.e., more
Table 1
Sample Known and Unknown Statements
Truth Falsehood
Known A prune is a dried plum. A date is a dried plum.
The Pacific Ocean is the largest ocean
on Earth.
The Atlantic Ocean is the largest ocean
on Earth.
The Cyclops is the legendary one-
eyed giant in Greek mythology.
The Minotaur is the legendary one-eyed
giant in Greek mythology.
Unknown Helsinki is the capital of Finland. Oslo is the capital of Finland.
Marconi is the inventor of the
wireless radio.
Bell is the inventor of the wireless
radio.
Billy the Kid’s last name is Bonney. Billy the Kid’s last name is Garrett.
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995
KNOWLEDGE AND ILLUSORY TRUTH
accurate) ratings during the truth phase than unknown ones (M ⫽
4.03), F(1, 39) ⫽ 196.05, MSE ⫽ .33, p ⬍ .001,
p
2
⫽ .83.
Surprisingly, knowledge did not interact with repetition to influ-
ence truth ratings, F(1, 39) ⫽ 1.21, p ⫽ .28. Similar illusory truth
effects occurred for contradictions of known, (repeated M ⫽ 2.93;
new M ⫽ 2.59; t(39) ⫽ 2.93, SEM ⫽ .12, p ⫽ .006), and unknown
(repeated M ⫽ 4.13; new M ⫽ 3.92; t(39) ⫽ 2.79, SEM ⫽ .07, p ⫽
.008) statements. In fact, the effect was numerically larger in the
known condition. In other words, repetition increased perceived truth-
fulness, even for contradictions of well-known facts.
For completeness, we note that an illusory truth effect also
emerged for truths, repeated M ⫽ 4.11; new M ⫽ 3.87; t(39) ⫽
3.41, SEM ⫽ .07, p ⫽ .002. Illusory truth occurred for unknown
(4.11 vs. 3.82; t(39) ⫽ 3.28, SEM ⫽ .09, p ⫽ .002) but not known
(5.28 vs. 5.15; t(39) ⫽ 1.80, SEM ⫽ .07, p ⫽ .08) statements. The
latter result likely reflects a ceiling effect.
Truth ratings as a function of knowledge estimated by norms.
We also analyzed the data using norm-based estimates of knowl-
edge. The complete data appear in Figure 1B, but we will highlight
only the key result: Participants exhibited illusory truth effects for
both known (repeated M ⫽ 3.41; new M ⫽ 2.94; t(39) ⫽ 5.02,
SEM ⫽ .09, p ⬍ .001)
and unknown (repeated M ⫽ 3.81; new
M ⫽ 3.53; t(39) ⫽ 3.41, SEM ⫽ .08, p ⬍ .001) falsehoods.
Discussion
Experiment 1 tested the widely held assumption that illusory
truth effects depend upon the absence of knowledge. Surprisingly,
repetition increased statements’ perceived truth, regardless of
whether stored knowledge could have been used to detect a con-
tradiction. Reading a statement like “A sari is the name of the short
pleated skirt worn by Scots” increased participants’ later belief that
it was true, even if they could correctly answer the question “What
is the name of the short pleated skirt worn by Scots?” Similar
results were observed when knowledge was estimated using group
norms or directly measured in individuals using a postexperiment
knowledge check. We also replicated this pattern using norm-
based estimates of knowledge in a smaller experiment (n ⫽ 16)
that we will not report in detail, other than to note that the
interaction between knowledge and repetition was not significant,
F(1, 15) ⫽ 1.43, MSE ⫽ .07, p ⫽ .25, and that there was a robust
illusory truth effect for known falsehoods, t(15) ⫽ 3.57, SEM ⫽
.13, p ⫽ .003.
These data suggest a counterintuitive relationship between flu-
ency and knowledge. Prior work assumes that people only rely on
fluency if knowledge retrieval is unsuccessful (i.e., if participants
lack relevant knowledge or fail to search memory at all). Experi-
ment 1 demonstrated that the reverse may be true: Perhaps people
retrieve their knowledge only if fluency is absent (i.e., if statement
is new or was not attended to during the exposure phase; if the
participant spontaneously discounts fluency while reading re-
peated statements). To discriminate between these two possibili-
ties, we created multinomial models in the form of branching tree
diagrams with parameters representing unobserved cognitive pro-
cesses. Each parameter represents the probability that the cognitive
process contributes to the observed behavior (from 0 to 1). This
method has successfully characterized diverse phenomena, includ-
ing the hindsight bias (Erdfelder & Buchner, 1998), the misinfor-
mation effect (Jacoby, Bishara, Hessels, & Toth, 2005), and the
engagement of racial stereotypes (Bishara & Payne, 2009).
The knowledge-conditional model assumes that when judging a
statement’s truthfulness, people search their memory for relevant
evidence (see Figure 2). If this search succeeds (probability ⫽ K),
all other processes are irrelevant, and the participant answers
correctly. If the search fails (probability ⫽ 1 ⫺ K), due to a lack
of knowledge or insufficient cues, the participant may rely on
fluency to make the judgment. If the participant relies on fluency
(probability ⫽ F), he or she exhibits a bias to respond “true”; if
fluency is absent or discounted (probability ⫽ 1 ⫺ F), the partic-
Figure 1. Mean truth ratings for falsehoods as a function of repetition and both demonstrated (A) and
normed-based estimates (B) of knowledge (Experiment 1). Error bars reflect standard error of the mean.
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996
FAZIO, BRASHIER, PAYNE, AND MARSH
ipant guesses “true” (probability ⫽ G) or “false” (probability ⫽
1 ⫺ G).
In contrast, the fluency-conditional model uses a different set of
conditional probabilities in assuming that fluency can supersede
retrieval of knowledge (see Figure 3). The participant only
searches memory if fluency is absent or discounted (probability ⫽
1 ⫺ F). In this case, the participant retrieves knowledge (proba-
bility ⫽ K) or if nothing is retrieved (probability ⫽ 1 ⫺ K), he or
she guesses “true” (probability ⫽ G) or “false” (probability ⫽ 1 ⫺
G). In those cases where the participant experiences fluent pro-
cessing and does not discount that fluency, the participant exhibits
a bias to respond “true” (probability ⫽ F).
At first glance, these models may appear to be formally equiv-
alent. While they contain the same parameters, the graphical order
(and thus conditional probabilities) of the models differ markedly.
To better conceptualize how the models differ, it is useful to
remember Bayes’s theorem, whereby the probability of A given B
is not necessarily equal to the probability of B given A. For
Figure 2. Knowledge-conditional model of illusory truth. Parameter values reflect the probability that the
cognitive process contributes to behavior (from 0 to 1). K ⫽ knowledge; F ⫽ fluency; G ⫽ guess true.
Figure 3. Fluency-conditional model of illusory truth. Parameter values reflect the probability that the
cognitive process contributes to behavior (from 0 to 1). K ⫽ knowledge; F ⫽ fluency; G ⫽ guess true.
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KNOWLEDGE AND ILLUSORY TRUTH
example, the probability that a person is male, given that he or she
is 45 years old, is not equal to the probability that a person is 45
years old, given that he is male. Applied to the present models,
failing to retrieve knowledge given disfluent processing is not
necessarily equivalent to the probability of disfluent processing
given failure to retrieve knowledge. Figures 2 and 3 illustrate how
these two models lead to different predicted responses.
To facilitate model testing, we replicated Experiment 1 with a
binary truth rating rather than a 6-point scale. The structure of the
models requires that we analyze both truths and falsehoods; if we
exclusively modeled falsehoods, we would be unable to discrim-
inate knowledge retrieval from accurate guesses. Because Exper-
iment 1 validated the use of norms in estimating participants’
knowledge, this experiment did not employ a knowledge check.
We used these binary data to compare the relative fits of the
knowledge-conditional and fluency-conditional models of illusory
truth.
Experiment 2
Method
Participants. Forty Duke University undergraduates partici-
pated in exchange for monetary compensation. Participants were
tested individually or in small groups of up to five people.
Design. The experiment had a 2 (truth status: truth, false-
hood) ⫻ 2 (repetition: repeated, new) ⫻ 2 (estimated knowledge:
known, unknown) within-subjects design. All factors were coun-
terbalanced across participants.
Materials. We used the same statements as in Experiment 1.
Procedure. The procedure was identical to that of Experiment
1, with the exceptions that (a) participants made binary truth
judgments (true or not true) instead of using a 6-point scale and (b)
there was no knowledge check.
Results
Unlike Experiment 1, we performed analyses on the proportion
of statements rated “true.”
Truth ratings. The illusory truth effects in Experiment 1
represent small shifts along the middle of a 6-point scale. To
confirm that we replicated Experiment 1 with a binary scale, we
conducted a 2 (truth status: truth, falsehood) ⫻ 2 (repetition:
repeated, new) ⫻ 2 (estimated knowledge: known, unknown)
ANOVA on the proportion of statements judged to be “true.” The
complete data appear in Figure 4; as expected there were main
effects of truth status, F(1, 39) ⫽ 163.81, MSE ⫽ .03, p ⬍ .001,
p
2
⫽ .81, and estimated knowledge, F(1, 39) ⫽ 25.65, MSE ⫽ .04,
p ⬍ .001,
p
2
⫽ .40. In addition, the basic illusory truth effect
emerged: Repeated statements (M ⫽ 0.62) were more likely to be
judged “true” than new statements (M ⫽ 0.56), F(1, 39) ⫽ 13.18,
MSE ⫽ .03, p ⫽ .001,
p
2
⫽ .25. Critically, there was no interaction
between repetition and estimated knowledge, F(1, 39) ⫽ 2.36, p ⫽
.13, MSE ⫽ .01; illusory truth occurred regardless of whether the
statements were estimated to be known, .67 vs. .62, t(39) ⫽ 2.34,
SEM ⫽ .02, p ⫽ .025, or unknown, .58 vs. .49, t(39) ⫽ 3.53,
SEM ⫽ .02, p ⫽ .001. We observed a significant three-way
interaction among truth, repetition, and knowledge, reflecting a
ceiling effect for known truths, F(1, 39) ⫽ 6.86, MSE ⫽ .01, p ⫽
.01,
2
⫽ .15.
Model testing. We tested the fit of both multinomial models
using multiTree software (Moshagen, 2010), which minimizes a
G
2
statistic (see the appendix for equations); lower G
2
values
indicate better model fit. The null hypothesis states that the model
fits, so significant p values indicate poor fit. To preserve degrees
of freedom, we placed theoretically motivated constraints on the
parameters. These constraints ensured that there were more data
cells (eight) than free parameters (five) in each model. Specifi-
cally, the knowledge parameter (K) was free to vary across
Figure 4. Proportion of statements rated “true” as a function of repetition, truth, and norm-based estimates of
knowledge (Experiment 2). Error bars reflect standard error of the mean.
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998
FAZIO, BRASHIER, PAYNE, AND MARSH
known and unknown statements but was constrained to be equal
for truths and falsehoods, as well as for new and repeated
statements. Second, the fluency parameter (F) was free to vary
across new and repeated statements but was constrained to be
equal for known and unknown statements, as well as for truths
and falsehoods. Finally, the guessing parameter (G) was held
constant across all cells.
With these constraints, the knowledge-conditional model fit the
data poorly, G
2
(df ⫽ 3) ⫽ 185.59, p ⬍ .00001. In contrast, the
fluency-conditional model fit well, G
2
(df ⫽ 3) ⫽ 2.54, p ⫽ .47.
Furthermore, as shown in Table 2, the parameter estimates for the
fluency-conditional model reflect the main effects reported in
Experiment 1. Knowledge retrieval occurred more for statements
estimated to be known (K ⫽ .72) rather than unknown (K ⫽ .13),
reflecting the main effect of knowledge. In addition, reliance on
fluency was higher for repeated (F ⫽ .44) than new statements
(F ⫽ .35), reflecting the main effect of repetition. The one result
that may appear counterintuitive is the relatively low probability of
guessing “true” (G ⫽ .21). However, this estimate is likely less
accurate than the estimates of the other parameters, given the wide
confidence interval surrounding it (0.13, 0.28). In addition, guess-
ing only influenced responses in a select few cases, where fluency
was absent or discounted and knowledge retrieval failed. In other
words, any influence of guessing was likely small.
We also compared the fit of the fluency-conditional model to a
nested model with additional parameter constraints. Here, the null
hypothesis states that the nested model fits as well as the original
model. Constraining K to be equal for known and unknown state-
ments led to poor model fit, ⌬G
2
(⌬df ⫽ 1) ⫽ 380.64, p ⬍ .00001,
as did constraining F to be equal for new and repeated statements,
⌬G
2
(⌬df ⫽ 1) ⫽ 30.78, p ⬍ .001. Finally, we tested a version of
the model where F was not constrained to be equivalent for true
and false statements (based on the arguments in Unkelbach, 2007).
This modified model (which allowed F to vary across truths and
falsehoods) had too few free parameters, but the parameter values
varied as expected.
It is important to note that the fluency-conditional model of
illusory truth is entirely consistent with the large main effects of
knowledge reported in Experiments 1 (
p
2
⫽ .83) and 2 (
p
2
⫽ .40).
The superior fit of the fluency-conditional model demonstrates that
it is possible for participants to rely on fluency despite having
contradictory knowledge stored in memory (an impossibility in the
knowledge-conditional model). This model does not assume that
participants will always rely on fluency; in fact, estimates of
fluency-based responding were relatively low (F ⬍ .5 across all
trials). Instead, participants relied on knowledge if statements
lacked fluency (i.e., they were new or were not well-attended while
rating interest) or because participants spontaneously discounted
their feelings of fluency upon reading some of the repeated state-
ments (Oppenheimer, 2004). Under either of these circumstances,
participants searched memory for relevant evidence, yielding a
main effect of knowledge. In other words, the question of which
process is conditional on the other is separate from whether the
probability of either process is high or low.
To summarize, the results of our model testing complement the
findings in Experiment 1. To further validate the modeling, we
dichotomized the truth judgments from Experiment 1 and assessed
which model fit those data. When we estimated knowledge with
the norms, the fluency-conditional model fit the data well, G
2
(df ⫽ 3) ⫽ 2.10, p ⫽ .55, and the knowledge-conditional model
did not, G
2
(df ⫽ 3) ⫽ 63.44, p ⬍ .00001. Similar patterns held
when we defined knowledge at at an individual level (i.e., perfor-
mance on the knowledge check): The fluency-conditional model
fit well, G
2
(df ⫽ 3) ⫽ 6.22, p ⫽ .10, and the knowledge-
conditional model fit poorly, G
2
(df ⫽ 3) ⫽ 65.43, p ⬍ .00001.
Thus, reanalysis of Experiment 1 data yielded the same conclu-
sions; neither study supported the knowledge-conditional model,
which has been assumed in the literature until now. The fluency-
conditional model, on the other hand, fit the data well. People
searched memory in the absence of fluency, consistent with the
idea that disfluent processing triggers more elaborate processing
(Song & Schwarz, 2008).
General Discussion
The present research demonstrates that fluency can influence
people’s judgments, even in contexts that allow them to draw upon
their stored knowledge. The results of two experiments suggest
that people sometimes fail to bring their knowledge to bear and
instead rely on fluency as a proximal cue. Participants more
accurately judged the truth of known than unknown statements, but
there was no interaction between knowledge and repetition.
Whether we defined knowledge using norm-based estimates or
individuals’ accuracy on a knowledge check, fluency exerted a
similar effect on contradictions of well-known and ambiguous
facts. Our conclusions do not contradict the few studies targeting
the moderating role of knowledge in illusory truth. As noted
earlier, the data on expertise do not really speak to the issue at
hand, as those studies targeted ambiguous statements within an
expert domain (Arkes et al., 1989; Boehm, 1994). The data also do
not contradict Unkelbach and Stahl’s (2009) multinomial model,
which included a knowledge parameter intentionally set to be near
Table 2
Parameter Estimates for the Fluency-Conditional Model
Parameter
Known Unknown
New Repeated New Repeated
F 0.35 [0.31, 0.39] 0.44 [0.40, 0.48] 0.35 [0.31, 0.39] 0.44 [0.40, 0.48]
K 0.72 [0.69, 0.76] 0.72 [0.69, 0.76] 0.13 [0.07, 0.19] 0.13 [0.07, 0.19]
G 0.21 [0.13, 0.28] 0.21 [0.13, 0.28] 0.21 [0.13, 0.28] 0.21 [0.13, 0.28]
Note. Parameter values reflect the probability that the cognitive processes contribute to the observed behavior
(from 0 to 1). The 95% confidence interval around each parameter estimate is noted in brackets. F ⫽ reliance
on fluency; K ⫽ retrieval of knowledge; G ⫽ guess “true.”
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999
KNOWLEDGE AND ILLUSORY TRUTH
zero. The fluency-conditional model extends their work to situa-
tions in which people evaluate information already stored in mem-
ory.
Although our findings contradict a dominant assumption, they
are consistent with what we know about semantic retrieval, where
the knowledge retrieved often lacks source information (Tulving,
1972). Though people can recall and evaluate source information
when judging recently acquired information (represented by Un-
kelbach and Stahl’s (2009) recollection parameter), people rarely
engage in source monitoring when evaluating information stored
in their knowledge bases. This nonevaluative tendency may render
people especially susceptible to external influences like fluency.
Kelley and Lindsay (1993), for example, demonstrated the influ-
ence of retrieval fluency, or the ease with which an answer is
retrieved from memory. Participants read a series of words, some
of which were semantically related to the answers for a later
general knowledge test. Later, participants not only incorrectly
answered questions with the lures they saw earlier, but did so with
high confidence, in what the authors termed “illusions of knowl-
edge.” These data bear a strong resemblance to the present find-
ings, where people underutilized their knowledge in the face of
repetition-based fluency.
Knowledge neglect, or the failure to appropriately apply stored
knowledge, occurs in tasks other than truth judgments. Fazio and
Marsh (2008), for example, exposed participants to errors embed-
ded in fictional stories (e.g., paddling across the largest ocean, the
Atlantic). Errors contradicted known or unknown facts, in a
knowledge manipulation similar to that of Experiment 1. Partici-
pants were no better at detecting contradictions with known than
unknown facts. Similarly, in the Moses illusion, participants an-
swer a series of questions, some of which include faulty presup-
positions (e.g., “How many animals of each kind did Moses take
on the ark?”). People often answer this question as if nothing were
wrong with it, despite knowing that Noah, not Moses, took animals
on the ark (Erickson & Mattson, 1981). The present data reveal
that knowledge neglect occurs even when participants explicitly
evaluate statements’ truthfulness.
In the experiments reported here, participants sometimes neglected
their knowledge under fluent processing conditions. Gilbert (1991) ar-
gued that people automatically assume that a statement is true because
“unbelieving” comprises a second, resource-demanding step. Even
when people devote resources to evaluating a claim, they only
require a “partial match” between the contents of the statement and
what is stored in memory (see Reder & Cleeremans, 1990; Reder
& Kusbit, 1991). In other words, we tend to notice errors that are
less semantically related to the truth (e.g., to notice the error in the
question “How many animals of each kind did Adam take on the
ark?;” Van Oostendorp & de Mul, 1990). We expect that partici-
pants would draw on their knowledge, regardless of fluency, if
statements contained implausible errors (e.g., “A grapefruit is a
dried plum,” instead of “A date is a dried plum”); in this example,
the limited semantic overlap of the words grapefruit and plum
would yield an insufficient match. Critically, what constitutes a
partial match depends on an individual’s knowledge. Experts may
be less susceptible to illusory truth, as long as they possess
knowledge directly pertinent to the statements (unlike Boehm,
1994, and Arkes et al., 1989).
Contrary to the connotations of the term illusory truth and
knowledge neglect, fluency serves as a useful cue in many every-
day situations. Inferring truth from fluency often proves to be an
accurate and cognitively inexpensive strategy, making it reason-
able that people sometimes apply this heuristic without searching
for knowledge. However, certain situations likely discourage the
use of the fluency heuristic; fact-checkers reading drafts of a
magazine article or reporters waiting to catch a politician in a
misstatement, for example, likely draw on knowledge in the face
of fluency. In addition, sufficient experience may encourage an
individual to shift from a fluency-conditional to a knowledge-
conditional approach. As an example, learners provided with trial-
by-trial feedback may learn that their gut responses are often
wrong. Our work demonstrates that fluency can emerge as the
dominant signal in some contexts, but future research should
examine the factors that encourage reliance on knowledge instead.
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Appendix
Modeling Methods
Multinomial models were implemented using multiTree soft-
ware (Moshagen, 2010) with random start values. With an alpha
level of .05, power to detect medium effect sizes (w ⫽ .3; Cohen,
1977) always exceeded .999.
Knowledge-Conditional Model Equations
P(Correct
|
True) ⫽ K ⫹ (1 ⫺ K)(F) ⫹ (1 – K)(1 – F)(G) (A1)
P(Incorrect
|
True) ⫽ (1 ⫺ K)(1 – F)(1 – G) (A2)
P(Correct
|
False) ⫽ K ⫹ (1 ⫺ K)(1 ⫺ F)(1 – G) (A3)
P(Incorrect
|
False) ⫽ (1 ⫺ K)(F) ⫹ (1 – K)(1 – F)(G) (A4)
Fluency-Conditional Model Equations
P(Correct
|
True) ⫽ F ⫹ (1 ⫺ F)(K) ⫹ (1 ⫺ F)(1 ⫺ K)(G)
(A5)
P(Incorrect
|
True) ⫽ (1 ⫺ F)(1 ⫺ K)(1 ⫺ G) (A6)
P(Correct
|
False) ⫽ (1 ⫺ F)(K) ⫹ (1 ⫺ F)(1 ⫺ K)(1 ⫺ G) (A7)
P(Incorrect
|
False) ⫽ F ⫹ (1 ⫺ F)(1 ⫺ K)(G) (A8)
Received May 5, 2014
Revision received June 25, 2015
Accepted June 29, 2015 䡲
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