Fluency Heuristic: A Model of How the Mind Exploits a By-Product
of Information Retrieval
Ralph Hertwig and Stefan M. Herzog
University of Basel, Switzerland
Lael J. Schooler
Max Planck Institute for Human Development, Berlin
University of Maryland, College Park
Boundedly rational heuristics for inference can be surprisingly accurate and frugal for several reasons.
They can exploit environmental structures, co-opt complex capacities, and elude effortful search by
exploiting information that automatically arrives on the mental stage. The fluency heuristic is a prime
example of a heuristic that makes the most of an automatic by-product of retrieval from memory, namely,
retrieval fluency. In 4 experiments, the authors show that retrieval fluency can be a proxy for real-world
quantities, that people can discriminate between two objects’ retrieval fluencies, and that people’s
inferences are in line with the fluency heuristic (in particular fast inferences) and with experimentally
manipulated fluency. The authors conclude that the fluency heuristic may be one tool in the mind’s
repertoire of strategies that artfully probes memory for encapsulated frequency information that can
veridically reflect statistical regularities in the world.
Keywords: fluency, fluency heuristic, recognition heuristic, ecological rationality, ACT-R
Supplemental Materials: http://dx.doi.org/10.1037/a0013025.supp
The human mind has long been regarded as tailored to register
and to exploit frequencies of occurrence. Take, for instance, David
Hume’s (1740/1978) view, expressed in A Treatise of Human
As the habit, which produces the association, arises from the frequent
conjunction of objects, it must arrive at its perfection by degrees, and
must acquire new force from each instance, that falls under our
observation. The first instance has little or no force: The second makes
some addition to it: The third becomes still more sensible; and ’tis by
these slow steps, that our judgment arrives at a full assurance. (p. 130)
Hume (1740/1978) believed that the mind unconsciously tallies
frequencies and apportions degrees of belief (for Hume, the vivac-
ity of an idea). He held that the mechanism for converting observed
frequency into belief was finely tuned: “When the chances or
experiments on one side amount to ten thousand, and on the other
to ten thousand and one, the judgment gives the preference to the
latter, upon account of that superiority” (p. 141). Today, we know
that Hume endowed the human mind with too exquisite a sensi-
tivity to frequencies and that there is typically no direct mapping
of environmental frequencies onto degrees of beliefs. Yet, numer-
ous treatments of human cognition and memory center on the
effects of environmental frequencies and repetition on memory
and judgment (e.g., Dougherty, Gettys, & Ogden, 1999). Various
mental tools exploit the “force” of these repetitions on memory.
One of these mental tools is the ACT-R fluency heuristic that
cashes in on retrieval fluency (Schooler & Hertwig, 2005). In what
follows, we describe the fluency heuristic, and its close relative the
recognition heuristic, before discussing their intellectual anteced-
The Fluency Heuristic and the Recognition Heuristic
The research program on fast and frugal heuristics (Gigerenzer,
Todd, & the ABC Research Group, 1999) has demonstrated that a
user of heuristics who invested modest amounts of cognitive
effort—for instance, in terms of searching for information and
integrating it— could nevertheless achieve high levels of perfor-
mance. Arguably the simplest of these heuristics, the recognition
heuristic is a key example of just how far a little cognitive effort
can go. It predicts which of two objects, aor b, has the higher
value on a quantitative criterion. The heuristic’s policy states:
If one of two objects, aor b, is recognized and the other is not, then
infer that the recognized object has the higher value with respect to the
Ralph Hertwig and Stefan M. Herzog, Department of Psychology,
University of Basel, Switzerland; Lael J. Schooler, Center for Adaptive
Behavior and Cognition, Max Planck Institute for Human Development,
Berlin, Germany; Torsten Reimer, Department of Communication, Uni-
versity of Maryland, College Park.
Ralph Hertwig was supported by Swiss National Science Foundation
Grants 100013-107741/1 and 100014-118283/1. Our thanks go to Julian
Marewski, Thorsten Pachur, and Tim Pleskac for many constructive com-
ments. We also thank Laura Wiles for editing the manuscript, and Gregor
Caregnato, Arne Fesche, and Renato Frey for conducting the experiments.
Correspondence concerning this article should be addressed to Ralph
Hertwig, Department of Psychology, Missionsstrasse 60/62, CH-4055
Basel, Switzerland. E-mail: firstname.lastname@example.org
Journal of Experimental Psychology: Copyright 2008 by the American Psychological Association
Learning, Memory, and Cognition
2008, Vol. 34, No. 5, 1191–1206
0278-7393/08/$12.00 DOI: 10.1037/a0013025
Recognition knowledge (i.e., knowledge of the previously ex-
perienced or believed to be previously experienced) is cognitively
inexpensive. In the process of retrieving a memory record, it
arrives automatically and instantaneously on the mental stage, thus
ready to enter inferential processes when other knowledge still
awaits retrieval (Pachur & Hertwig, 2006). Low-cost recognition
information, of course, will not inevitably result in high levels of
accuracy. For good performance, recognition needs to be corre-
lated with the criterion to be inferred, an issue to which we return
shortly. Since Goldstein and Gigerenzer (2002) proposed the rec-
ognition heuristic, numerous studies have demonstrated that rec-
ognition is an important piece of information across various infer-
ential tasks such as the prediction of outcomes at sport events (e.g.,
Pachur & Biele, 2007; Serwe & Frings, 2006) and the judgment of
demographic, geographic, and biological quantities (e.g., Pohl,
2006; Reimer & Katsikopoulos, 2004; Richter & Spa¨th, 2006).
Heuristics are not all-purpose inferential tools. Rather, they are
applicable under limited circumstance that, ideally, can be defined.
This is the case for the recognition heuristic: It cannot be applied
when both objects are either recognized or unrecognized. If both
objects are recognized, one applicable strategy is the fluency
heuristic (Schooler & Hertwig, 2005; see Gigerenzer & Goldstein,
1996, for other heuristics) that exploits ease of retrieval and can be
expressed as follows:
If two objects, aand b, are recognized, and one of two objects is more
fluently retrieved, then infer that this object has the higher value with
respect to the criterion.
Like the recognition heuristic, the fluency heuristic is useful when-
ever there is a substantial correlation—in either direction—
between a criterion and recognition and/or retrieval fluency. For
simplicity, we assume that the correlation is positive. The fluency
heuristic relies on one inexpensive piece of mnemonic information
to make an inference, namely, the fluency with which memory
records (of the objects’ names) are retrieved from long-term mem-
ory. That is, even if two objects are recognized, the fluency with
which the names are retrieved may be different. Such differences
can be exploited to make inferences about other properties of the
Schooler and Hertwig (2005) implemented the fluency heuristic
and the recognition heuristic within the ACT-R cognitive archi-
tecture (Anderson et al., 2004; Anderson & Lebiere, 1998),
thereby being able to precisely define fluency in terms of the time
it takes to retrieve memories (or chunks, to use the ACT-R termi-
nology). ACT-R makes the assumption that information in long-
term memory is stored in discrete chunks and that retrieval entails
search through these to find the one that achieves some processing
goal of the system. The explanatory power of the approach de-
pends on the system’s estimates of the probability that each record
in long-term memory is the one sought. These probabilities are
encoded in a record’s activation. Activation tracks environmental
regularities, such as an object’s frequency and recency of occur-
rence (and is also a function of parameters such as decay of
activation over time). Therefore, activation differences partly re-
flect frequency differences, which, in turn, may be correlated with
differences in objective properties of objects. A cognitive system
may be able to capitalize on differences in activation associated
with various objects by gauging how it responds to them. Two
phenomenological responses that are correlated with activation in
ACT-R are (a) whether a record associated with a specific object
can be retrieved and (b) how quickly the record can be retrieved
(henceforth, retrieval fluency). The first binary response was the
basis for Schooler and Hertwig’s implementation of the recogni-
tion heuristic. The second continuous response provided the basis
for their implementation of the fluency heuristic and the existence
proof that a cognitive system that relies on fluency can make
moderately accurate inferences about real-world quantities. It does
so by tapping indirectly—via retrieval fluency—into the environ-
mental frequency information locked in the chunks’ activation
Here are the goals of the current article: Schooler and Hertwig’s
(2005) ACT-R analysis of the fluency heuristic was theoretical in
nature. From their analysis, however, follow four empirical ques-
tions that we aim to answer: First, does retrieval fluency correlate
at all with objective properties of the world (Study 1)? Second,
how accurately can people discriminate between often-minute
differences in retrieval fluency (Study 2)? Third, to what extent do
individuals’ inferences actually agree with the fluency heuristic
(Study 3)? Fourth, is there direct experimental evidence that flu-
ency guides inferences about real-world quantities (Study 4)?
Before we turn to these studies, we review some intellectual roots
of the fluency heuristic.
Fluency, the Fluency Heuristic, and the
There are many different variants of fluency, including process-
ing fluency and the distinctions between absolute and relative
fluency, conceptual and perceptual fluency (see Alter & Oppen-
heimer, 2007; Reber, Schwarz, & Winkielman, 2004; Winkielman,
Schwarz, Fazendeiro, & Reber, 2003). The fluency heuristic as
investigated here focuses on retrieval fluency, a proximal cue that
can inform and influence human inference across a wide range of
target criteria. It has been demonstrated to underlie, for instance, a
person’s memory of the past and prediction of future memory
recall performance (Benjamin, Bjork, & Hirshman, 1998; Ben-
jamin, Bjork, & Schwartz, 1998); an eyewitness’s confidence in
her or his memory (Shaw, 1996; Shaw, McClure, & Wilkens,
2001); assessments of one’s ability to learn (e.g., Koriat &
Ma’ayan, 2005); and people’s confidence in their general knowl-
edge (e.g., Kelley & Lindsay, 1993; Unkelbach, 2007). It has also
been invoked in explaining consumer decisions (e.g., Schwarz,
Schooler and Hertwig’s (2005) use of the term fluency heuristic
hearkens back to a long research tradition on fluency, in particular
the work by Jacoby and Dallas (1981); Kelley and Jacoby (1998);
Kelley and Lindsay (1993); Whittlesea (1993); and Whittlesea and
Leboe (2003). Abstracting from the different meanings of the term
fluency heuristic across these articles, the gist involves three prop-
erties: (a) the attribution of “fluent” processing to prior experience,
(b) the resulting conscious experience of familiarity, and (c) the
At the same time, the assumption that no other probabilistic informa-
tion beyond recognition will be used if the recognition heuristic is appli-
cable has been vigorously challenged (e.g., Bro¨der & Eichler, 2006;
Newell & Fernandez, 2006; Oppenheimer, 2003; Pohl, 2006; Richter &
Spa¨th, 2006; but see also Pachur, Bro¨der, & Marewski, 2008; Pachur &
1192 HERTWIG, HERZOG, SCHOOLER, AND REIMER
assumption that relative fluency can be used as a basis for recog-
A second intellectual root of the fluency heuristic, as studied
here, is the availability heuristic, one of the key heuristics pro-
posed and investigated in the heuristics-and-biases research pro-
gram (Tversky & Kahneman, 1974). Two interpretations of this
heuristic have emerged, one of which includes the notion of
retrieval fluency (Tversky & Kahneman, 1973, pp. 208, 210; see
also Schwarz et al., 1991; Schwarz & Wa¨nke, 2002). In one
version, the availability heuristic rests on the actual frequencies of
instances or occurrences retrieved, for instance, the occurrences of
heart attack among one’s acquaintances to assess the risk of heart
attack among middle-aged people. Another rests on the ease, that
is, fluency with which the operation of retrieval of these instances
and occurrences can be performed (for more on the distinction
between these two notions of availability, see Hertwig, Pachur, &
Kurzenha¨user, 2005; Sedlmeier, Hertwig, & Gigerenzer, 1998).
Although there are similarities between the fluency heuristic and
the latter version of the availability heuristic, fluency researchers
have conceptualized them as two distinct heuristics (Jacoby &
Dallas, 1981, p. 701; Jacoby & Whitehouse 1989, p. 127). One
difference concerns the entities that are being retrieved. The avail-
ability heuristic derives its assessment of the frequency (probabil-
ity) of the target event, say, risk of heart attack among middle-aged
people, from the effortlessness (or lack thereof) with which prior
instances of the target event could be retrieved (Tversky & Kah-
neman, 1974). The fluency heuristic, as defined by Schooler and
Hertwig (2005), by contrast, bases its inferences simply on the
speed with which the event category itself (e.g., myocardial in-
farction) is recognized. We realize, however, that the extent to
which this property renders the fluency and the availability heu-
ristics distinct depends on one’s definition of availability. If avail-
ability extends to the retrieval of the event category itself, the
fluency heuristic and the availability heuristic will be indistin-
guishable. Let us therefore reiterate the view we expressed in
Schooler and Hertwig (2005, p. 626):
We would have no objection to the idea that the fluency heuristic falls
under the broad rubric of availability. In fact, we believe that our imple-
mentation of the fluency heuristic offers a definition of availability that
interprets the heuristic as an ecologically rational strategy by rooting
fluency in the informational structure of the environment.
Moreover, we believe that researchers in both the availability
heuristic and the fluency heuristic traditions will find the present
set of studies relevant. If one interprets the heuristics as distinct,
our studies will foster the understanding of the predictive power of
the fluency heuristic. Researchers interpreting availability and
fluency as two sides of the same coin may find the present studies
enriching to the extent that they provide one precise definition of
availability and investigate its predictive power in making infer-
ences about the world. That predictive power or lack thereof is, in
fact, the focus of Study 1.
Study 1: Is It Worth Exploiting Retrieval Fluency?
How can one learn the association between retrieval fluency and
a criterion when the criterion is not accessible? In the context of
the recognition heuristic, Goldstein and Gigerenzer (2002) pro-
posed that there are “mediators” in the environment that both
reflect (but do not reveal) the criterion and are accessible to
decision makers’ senses. For example, one may have no direct
information about the ability of a tennis player, say, Roger Federer.
Yet, his strength as a player may be reflected by how often his
name is mentioned in the newspaper (ecological correlation).
Because the newspaper is accessible, it can operate as a mediator.
The frequency of mentions in the newspaper, in turn, is correlated
with how likely someone is to recognize the player’s name (sur-
rogate correlation). Finally, how good or poor a proxy a person’s
recognition knowledge is of the criterion is captured in the recog-
nition validity. Based on this chain of correlations between the
criterion, the mediator, and the mind, a person would be able to
make inferences about a player’s strength depending on whether
he or she recognized his name.
The same ecological analysis can also be conducted for the
fluency heuristic, except that retrieval fluency replaces recogni-
tion. Frequency of mentions in the newspaper may be correlated
not only with recognition but also with how quickly a person can
retrieve the memory record representing the name Roger Federer.
That is, the mediator can influence the recognition latency (thus
giving rise to varying degrees of fluency) and the probability of
recognition. Fluency, however, can be high for the wrong reasons
and need not reflect environmentally valid variations in exposure.
It may be sensitive to recent exposures, thus compromising the link
between fluency and the criterion.
So, what is the ecological validity of retrieval fluency? Inves-
tigations of the chain of correlations between the criterion, fre-
quency of mentions, and retrieval fluency in a clearly defined
reference class of objects do not exist.
One rare exception is the
investigation by Alter and Oppenheimer (2006). They found that
the complexity of a share’s name and the pronounceability of
companies’ three-letter stock ticker codes—both variables are in-
dicators of the verbal fluency of a share’s name—are predictive of
the actual performance of those shares in the stock market imme-
diately after their release onto the stock exchange. This analysis is
particularly interesting insofar as fluency in their investigation is
not just an indicator of the criterion, the share’s performance on the
stock market, but appears to causally determine stock performance
in the short term.
Whittlesea and Leboe (2003; Whittlesea, 1993) make an important
distinction between two kinds of fluency heuristics. In their view, a
person’s feeling of fluency can either be a reflection of enhanced process-
ing, or, alternatively, a person’s perception of fluency can be relative to his
or her “fluency” expectations. We focus on the first notion of fluency.
Moreover, we also do not address the role of naı¨ve theories that may trigger
the use or disuse of the fluency heuristic (see Schwarz, 2004).
Two experts on fluency, P. Winkielman (personal communication, Octo-
ber 13, 2007) and C. Unkelbach (personal communication, October 12, 2007)
confirmed our impression that there are hardly any systematic analyses of the
ecological validity of fluency in general and retrieval fluency in particular.
Ecological analyses remain rare, however, even if one equates availability and
fluency. Although Tversky and Kahneman (1973) stressed that “availability is
an ecologically valid clue for the judgment of frequency because, in general,
frequent events are easier to recall or imagine than infrequent ones” (p. 209),
many subsequent studies have focused on circumstances under which reliance
on availability leads astray (for a systematic analysis of the validity of avail-
ability in a classic task, see Sedlmeier et al., 1998).
THE FLUENCY HEURISTIC
In Study 1, we investigated whether retrieval fluency, like recog-
nition, is a proxy for real-world quantities across five different refer-
ence classes in which we expected retrieval fluency to be effective. In
this and the subsequent studies, we could not directly measure re-
trieval fluency, so instead we collected recognition latencies, that is,
how long it took participants to judge an object as recognized or not.
Of course, recognition latency is not a perfect proxy for retrieval
fluency. Recognition latency includes other systematic and unsystem-
atic components such as the time it takes to read a word, to decide
whether it is recognized, and to output a motor response. It may well
be that these factors drown out the contribution that retrieval latency
makes to the overall recognition time.
Participants. One hundred and sixty students from the Uni-
versity of Basel participated in the study (98 women and 62 men,
mean age !24.9 years), which was conducted at the Department
of Psychology. Participants received either money (7.50 Swiss
Francs !US$6.10) or a course credit for their participation.
Material. To study the predictive power of fluency across
different domains, we compiled five different environments (see
supplemental materials): (a) the cities environment comprising all
118 U.S. cities with more than 100,000 inhabitants (Butler, 2003);
(b) the companies environment containing all 100 German com-
panies with the highest revenue in 2003 (“Die 100 gro¨ssten deut-
schen Unternehmen,” n.d.); (c) the music artists environment in-
cluding all 106 most successful artists in the U.S., in terms of the
cumulative sales of recordings in the U.S. from 1958 to 2003
(“Top artists,” 2003); (d) the athletes environment including the 50
richest athletes in 2004 (“The Best-Paid Athletes”, 2004); and (e)
the billionaires environment including the 100 wealthiest people in
2004 (“The World’s Richest People”, 2004). For each environ-
ment, we first determined the frequencies of mentions in the media
(the mediator), by using COSMAS, the largest online archive of
German print media (e.g., encyclopedias, books, and daily and
weekly newspaper articles).
Procedure. We recorded people’s recognition latencies for
each of the total of 474 objects. To avoid exhaustion, each partic-
ipant saw a subset of the 474 objects. Forty participants were
presented with the names of 100 German companies, 106 music
artists, 50 sportspeople, and 100 billionaires, one at a time on the
The order of objects within each environment
was randomized across participants, as was the order of environ-
ments. A second group of 120 participants saw approximately a
third of a pool of 525 cities drawn from different regions of the
world, including 118 U.S. cities with more than 100,000 inhabit-
ants (see the material used in Volz et al., 2006). Because the set of
U.S. cities is the largest and the only one that encompasses all
objects within a defined size range, we focus on the U.S. set in the
following analysis. The names of the objects were presented one at
a time (in random order), and respondents were asked to decide
whether they had heard of the object. Both groups of participants
indicated their decisions by pressing one of two keys, with the
assignment of keys to responses counterbalanced across partici-
pants. In addition, respondents were instructed to make their
decisions as quickly and as accurately as possible. The time that
elapsed between the presentation of the object’s name and the
participant’s keystroke was measured. After each response, the
screen remained blank for 1,000 ms. Participants responded to 10
How ecologically valid is retrieval fluency? To quantify the
strength of the relationship between the environmental criteria, the
frequency with which the names of the objects were mentioned in
the media, and retrieval fluency (measured in terms of recognition
latency), we calculated three measures: the fluency validity, the
ecological validity, and the surrogate validity. The strength of the
relationship between retrieval fluency and the criterion is defined
as the proportion of times a faster recognized object has a higher
criterion value than an object requiring more time to be recognized
(in a given reference class). The fluency validity v
is the number of correct (right) inferences made by the
fluency heuristic computed across all pairs in which the difference
in recognition latencies between two recognized objects equals or
exceeds a just noticeable difference (JND), and W
is the number
of incorrect (wrong) inferences under the same circumstances.
Note that validity is linearly related to Goodman-Kruskal’s #, an
ordinal measure of association. Concerning the JND, Schooler and
Hertwig (2005) assumed that there are limits to people’s ability to
discriminate between retrieval times. They assumed that the dif-
ference needs to be as large or larger than a JND of 100 ms,
otherwise people resort to guessing. This value of 100 ms was
based on Fraisse’s (1984) extensive review of the timing litera-
ture— on his conclusion that durations of less than 100 ms are
perceived as instantaneous—and is consistent with our findings in
Study 2. Although all analyses reported in this article assume a
JND of 100 ms, results do not depend on this specific value.
Ecological validity describes the relation between the criterion
to be inferred and how often an object was mentioned in the media
(its environmental frequency, measured with COSMAS). Ecolog-
ical validity is defined as the proportion of times an object with the
higher number of mentions in the media has a higher criterion
value than does an object with a lower number of mentions. The
ecological validity v
is the number of correct cases and W
the number of
incorrect cases computed across all pairs where one object occurs
more frequently in the media than does the other.
We used print material published between 2000 and 2004. COSMAS
can be accessed on the Internet: http://corpora.ids-mannheim.de/$cosmas/
For the analyses of Studies 1–3, we excluded the names of eight
companies and four musicians because of their lengths (e.g., “Bob Seger
and the Silver Bullet Band”), that is, the risk of measuring reading times
rather than recognition latencies. We also checked whether there is a
correlation between the lengths of names (i.e., number of letters) and the
criterion in question; we found none. For 23 of the 92 German companies,
the official company name is different from the colloquial name. For these
companies, the media mentions were substantially higher for the colloquial
names than for the official names. Therefore, the ecological analyses rest
on the former.
1194 HERTWIG, HERZOG, SCHOOLER, AND REIMER
Finally, the surrogate validity describes the relation between the
mediator and recognition latencies. It is defined as the proportion
of times an object with a higher number of mentions in the media
is retrieved faster than an object with a lower number of mentions.
The surrogate validity v
is the number of correct cases and W
the number of
incorrect cases computed across all pairs where one object occurs
more frequently in the media than does the other (this calculation
also assumes a JND of 100 ms). Goldstein and Gigerenzer (2002)
and Schooler and Hertwig (2005) described the relations between
criterion, mediator, and mind in terms of one validity measure and
two correlation measures (ecological correlation and surrogate
correlation). To simplify comparisons between these correlations,
we consistently quantify them in terms of one currency, validity.
All three validities can be interpreted as the conditional probability
that object ascores higher (or lower) on one dimension than does
object b, given that object ascores higher (or lower) on a second
dimension than does object b.
Table 1 shows, separately for each environment, the median
response time, the average and median number of occurrences in
COSMAS per object, and the recognition rates. Across environ-
ments, the capacity to recognize objects varied widely, ranging
from 68% (music artists) to merely 11% (billionaires) recognized
objects (Table 1). Mirroring their low recognition rates, the median
environmental frequencies of the names of the billionaires and
athletes, respectively, is a magnitude smaller than that in the other
domains. Finally, except in the billionaires environment, the me-
dian time for recognized objects was markedly shorter than the
response time for unrecognized objects.
To what extent is retrieval fluency a proxy for inferring real-
world quantities? Figure 1 depicts the relations between the crite-
rion, the frequency with which the names of the objects occurred
in the media, and recognition latency and recognition, respectively.
In all five environments, fluency validity exceeded chance level
(.50), ranging from .66 in the cities environment to .58 in the music
artists and companies environments, respectively. Figure 1 also
shows that recognition validity consistently exceeds fluency va-
lidity—an observation to which we return in the final discussion.
Figure 2 plots fluency validity as a function of the magnitude of
the objective differences in latencies (summarized for four bins:
0 –99 ms, 100 –399 ms, 400 – 699 ms, %700 ms), separately for the
five environments. There is a clear tendency that the larger the
objective difference, the higher the validity of fluency. This ten-
dency follows from the ACT-R framework, in which the activation
of a memory record tracks environmental frequencies. Objects
with larger criterion values tend to occur more frequently in the
environment, and thus their memory records tend to be more
quickly retrieved. Consequently, large differences in latencies
are likely to represent a pair of objects in which one object has
a large criterion value and one has a small value. For such pairs,
fluency can be expected to be quite valid.
To conclude, we found that differences in recognition latencies
are indicative of criteria across five different environments. The
strength of the relationship varies across environments. In the
cities environment, for which we recorded the strongest relation-
ship between the mediator and the criterion, we also observed the
highest fluency validity. Similarly, environments with low ecolog-
ical validity such as the companies and the music artists environ-
ments also yielded relatively low levels of fluency validity. Based
on this ecological analysis of fluency across five environments, we
can now conclude that, based on retrieval fluency, one can at least
theoretically infer distal properties of the world.
Study 2: Are People Able to Exploit Retrieval Fluency?
To exploit retrieval fluency, people need to be able to judge
accurately whether recognizing object a’s name takes longer than
recognizing object b’s name, or vice versa. For instance, can a
person tell the difference between, say, instantaneously recogniz-
ing BMW, and taking a moment to recognize Siemens? Such
differences in recognition latencies partly reflect retrieval time
differences; these in turn reflect the base-level activation of the
corresponding memory records, which tracks environmental fre-
quency and recency (Schooler & Hertwig, 2005). In Study 2, we
investigated the extent to which people can accurately tell such
differences apart. In this and Study 3, we excluded the athletes and
the billionaires environments, because the low recognition rates
(26% and 11%, see Table 1) make it difficult to investigate the
critical cases in which people recognize both objects.
Participants. One hundred and twenty students (81 women
and 39 men, mean age !23.2 years) participated in the study, 60
Response Time, Recognition Rates, and Environmental Frequencies Obtained Across the Five Environments Investigated in Study 1
Median response time per object (ms)
M Mdn M Mdn M Mdn M Mdn
Cities 887 835 1129 1013 .53 .51 741 85
Companies 890 871 1045 999 .47 .48 671 142
Music artists 737 699 1045 1043 .68 .71 240 55
Athletes 948 920 1027 963 .26 .22 218 7
Billionaires 1591 1460 1176 1040 .11 .09 22 1
Measured in terms of hits in COSMAS (see Footnotes 4 and 5).
THE FLUENCY HEURISTIC
from the University of Basel and 60 from the Free University
(Berlin). Participants received either money (7.50 Swiss Francs !
US$6.10 in Basel; or €10 !US$12.50 in Berlin) or course credit
for their participation.
Material. Participants responded to a total of 156 pairs of U.S.
cities (n!59), German companies (n!46), and music artists
(n!51). Pairs were formed by randomly drawing objects (without
replacement) from the sets of cities, companies, and music artists,
respectively. We repeated the process of randomly pairing objects
(separately for each environment) a total of 30 times. Each of the
30 random pairings was then presented to 4 participants.
Procedure. Participants were informed that they would be
presented with 156 pairs of objects. Pairs were blocked for each
environment, and order of environments was counterbalanced.
Participants saw the name of the first object on the computer
screen. By pressing one of two buttons, they indicated whether
they recognized it. Then they saw the second name and responded
accordingly. The order of the names within each pair was random-
ized. Immediately following the recognition task, the second task
probed participants’ ability to discriminate between the response
times for the two objects in question. Both names were presented
on the screen, next to each other (the location, left vs. right, was
randomly determined). Respondents indicated for which of the
two names they more quickly arrived at the previous judgment
Figure 1. Ecological analysis of fluency validity and recognition validity
across five environments. The triangles, adapted from Goldstein and Giger-
enzer (2002; Figure 7), show the relationship between criterion, mediator, and
the mind, measured in terms of validity. The inaccessible criterion is reflected
but not revealed by the mediator variable (ecological validity). The mediator
(environmental frequencies as measured by frequency of occurrences in
COSMAS) influences retrieval speed (and probability of recognition); this link
is expressed in terms of the surrogate validity. The mind in turn may use
recognition speed or recognition to infer the criterion (the accuracy of this
inference is captured in terms of fluency or recognition validity). Note that the
ecological validity (the relationship between mediator and criterion) is inde-
pendent of whether the mind uses retrieval speed or recognition.
Figure 2. Increasing fluency validity accuracy as a function of increasing
differences in recognition latencies. Differences in objects aand b’s
recognition latencies were classified into four bins. Then, we calculated the
fluency validity per bin. We chose to create bins such that each bin
included judgments of nearly all participants. Other ways of creating the
bins, however, result in the same general trend. The horizontal line at 0.5
represents chance level validity.
1196 HERTWIG, HERZOG, SCHOOLER, AND REIMER
(regardless of whether the judgment was “recognized” or “un-
recognized”). For half of the participants, the order of recognition
and discrimination task was reversed. That is, they were presented
with the two names and asked for which they arrived at a covert
recognized versus unrecognized judgment more quickly (covert
insofar as they were not asked for an overt recognition judgment).
Once this discrimination was made, objects were presented again,
one at a time, and respondents indicated whether—a moment
ago—they had recognized the name. Participants first responded to
six practice trials.
First, we briefly describe the recognition judgments. When
recognition preceded discrimination, the rates of recognition and
the response times replicated those obtained in Study 1 (compare
Table 1 and Table F in the supplemental materials). As in Study 1,
recognized responses are, on average, markedly faster than unrec-
ognized responses. When recognition succeeded discrimination, a
similar picture emerged, with one exception. Response times, both
for recognized and unrecognized objects, were much shorter than
those when recognition preceded discrimination; recognition rates,
however, were almost identical for both orders.
To measure how accurately people can discriminate recogni-
tion latencies, we compared participants’ objective recognition
latencies (i.e., the time it took them to judge an object to be
recognized) with their judgments of which of the two objects
was recognized faster. For this analysis, we focused on the
condition in which recognition came first and discrimination
second, and recognition latencies were not biased through the
objects’ previous presentation. Table 2 reports how accurately
participants were able to discriminate differences in latencies
longer than 100 ms. Across all environments, participants
scored a median .76 accuracy, with quite comparable levels for
all three environments (.78, .77, and .74, respectively). In
addition, Figure 3 plots the discrimination accuracy (i.e., pro-
portion of correct discriminations) as a function of the magni-
tude of the objective differences in latencies. The larger the
objective difference, the better participants are able to discrim-
inate. When differences are shorter than 100 ms, accuracy drops
close to chance level (M!.53; Mdn !.54; 95% confidence
interval (CI) !.49, .58), consistent with Fraisse’s (1984) con-
clusion that latencies that differ by less than 100 ms are per-
ceived as instantaneous. When differences exceeded 700 ms,
the average discrimination accuracy rose to .86 and higher. In
light of this ability to discriminate, it is not surprising that if one
calculates fluency validity, using participants’ subjective judg-
ments of which of the two objects was recognized faster, the
subjective fluency validities—.65, .58, and .52 for the cities,
companies, and music artists environments, respectively—track
closely the fluency validities derived by using objective differ-
ences (Table 2). Note that in the music artists environment the
confidence interval includes chance performance of .50.
The results in Figure 3 also suggest that participants’ ability to
discriminate is particularly pronounced when they stand to benefit
most from relying on fluency, namely, when the differences in
recognition latency are large (see Figure 2). Indeed, as Table 3
shows, large differences in recognition latency give rise to high
levels of discrimination accuracy and fluency validity in the cities
environment. In the other two environments, the results exhibit the
same trend, but the trend is less pronounced.
In sum, we observed three results. First, people prove to be quite
good at discriminating between recognition latencies whose dif-
ference exceeds 100 ms. Second, even when taking less-than-
perfectly accurate discriminations into account, subjective fluency
judgments are a moderately good predictor of the criterion, except
in environments in which ecological validity of fluency informa-
tion is low to begin with (e.g., music artists environment). Last, we
found that people’s ability to discriminate is highest for those pairs
in which the validity of fluency peaks, as suggested by the ACT-R
Study 3: Are People’s Inferences in Line With the
The next study addresses three issues: First, we investigated the
extent to which people’s inferences are in line with the fluency
heuristic and the recognition heuristic. Second, we examined the
extent to which differences in retrieval fluency affect people’s
accordance to the fluency heuristic. Third, we tested the prediction
that inferences agreeing with the fluency heuristic require less
time, relative to inferences conflicting with the fluency heuristic.
Let us briefly develop the latter two issues.
Applicability and Validity of the Fluency and Recognition Heuristics, Respectively, Across Three Environments
Fluency heuristic Recognition heuristic
MCI MCI MCI MCI MCI MCI
Cities .20 .18, .23 .78 .75, .82 .64 .59, .68 .65 .62, .68 .48 .47, .50 .82 .80, .83
Companies .22 .20, .25 .77 .72, .81 .58 .53, .64 .58 .55, .62 .47 .45, .49 .69 .67, .71
Music artists .31 .29, .33 .74 .70, .78 .55 .51, .58 .52 .50, .55 .40 .38, .42 .56 .54, .58
Note. Discrimination accuracy reflects how accurately respondents can discriminate between the recognition latencies of two objects. Fluency validity is
simulated using both the objective recognition latencies (objective fluency validity) and people’s judgments (subjective fluency validity) of which object
was recognized faster. CI !95% confidence interval.
Only calculated from the participants in the condition in which recognition precedes discrimination.
THE FLUENCY HEURISTIC
Does Accordance to the Fluency Heuristic Increase as a
Function of the Difference in Recognition Latencies?
In Studies 1 and 2, we found that the larger the differences
between the two objects’ recognition latencies, the more likely that
the exploitation of fluency leads to a correct inference (Figure 2)
and the more accurate the discrimination between them (Figure 3).
Rather than assuming that differences beyond 100 ms give rise to
the same degree of fluency heuristic accordance, adherence may
depend on the magnitude of the difference.
Do Inferences That Agree With the Fluency Heuristic
Require Less Time?
Pachur and Hertwig (2006) predicted and found evidence that
inferences consistent with the recognition heuristic require less
time than do inconsistent inferences. This prediction can also be
extended to fluency information. Recognizing an object requires
the memory record of the object to be retrieved, and the speed with
which this process unfolds can be used as a proxy for people’s
senses of fluency. Therefore, fluency information is essentially
produced at first sight of the names of the objects. It is available
and ready to enter the inferential process while other information
has yet to be retrieved. In contrast, inferences inconsistent with the
fluency heuristic need to rely on information beyond recognition
and recognition speed. Unless the result of mere guessing, such
inferences rely on the retrieval of probabilistic cues or explicit
knowledge about the criterion. This logic suggests that inferences
based on fluency information may be made faster than inferences
inconsistent with the fluency heuristic. Yet, if reliance on fluency
requires additional checks such as whether the difference in laten-
cies is “good enough,” then fluency-based inference may turn out
not to be made faster.
Participants and design. Eighty students from the Free Uni-
versity (Berlin) participated in the study (42 women and 37 men;
1 participant failed to indicate gender information; mean age !
25.3 years). As in Study 2, participants were presented with 156
pairs of U.S. cities (n!59), German companies (n!46), and
music artists (n!51), and within each pair were asked to choose
the object with the higher value on the criterion (inference task).
Furthermore, each participant indicated which objects he or she
recognized (recognition task). Half of participants took this rec-
ognition test before the inference task and half after. They received
a flat fee of €10 (US$12.50).
Material. For the inference task, items were constructed in a
similar way as in Study 2. For each environment, we randomly
created 40 lists of pairs of objects (with the constraint that each
object could occur only once in a list). Each of these lists was then
presented to 2 participants; 1 received the recognition test before
and the other after the inference task. Pairs of objects were pre-
sented in three blocks (representing the three environments). Once
all inferences were made, the individual objects were again pre-
sented in three environmental blocks (in the same order as in the
inference task). Participants were asked to report whether they
recognized the name. The order of the blocks, the order of objects
within each block, and the location of objects within each pair (left
Figure 3. Increasing discrimination accuracy as a function of increasing
differences in recognition latencies. Differences in objects aand b’s
recognition latencies were classified into four bins. Then, we calculated the
mean discrimination accuracy per bin (across participants). Error bars
indicate 95% confidence intervals. The horizontal line at 0.5 represents
chance level accuracy.
Mean Discrimination Accuracy and Mean Fluency Validity Across Three Environments as a Function of Small Versus Large
Differences (&) in Recognition Latencies
Discrimination accuracy Fluency validity
Small &Large &
Small &Large &
Cities .72 .84 .12 .06, .18 .55 .71 .16 .08, .23
Companies .67 .84 .17 .09, .25 .56 .60 .04 –.08, .15
Music artists .67 .80 .13 .08, .18 .53 .57 .04 –.03, .11
Note. Across all pairs of objects and within each person, we calculated all differences in the recognition latencies for objects aand b. Using a person’s
median value, we classified all differences into sets of large and small ones, respectively, and calculated the average discrimination accuracies and fluency
validities, averaged across participants. The mean difference columns represent the mean differences between both sets, averaged across participants. CI !
95% confidence interval.
1198 HERTWIG, HERZOG, SCHOOLER, AND REIMER
side vs. right side of the screen) were determined at random for
Procedure. After an introductory text explaining the inference
task, pairs of objects were displayed on a computer screen. Par-
ticipants indicated their inferences by pressing one of two keys.
They were encouraged to respond as quickly and accurately as
possible. The time that elapsed between the presentation of the
objects and participants’ keystroke was measured. Each inference
trial began with the presentation of a fixation point (a cross in the
center of the screen), followed after 500 ms by the respective pair.
The names appeared simultaneously (left and right of the fixation
point) and remained on the screen until a response was given. After
each response, the screen remained blank for 1,100 ms. Partici-
pants first responded to six practice trials. The group of partici-
pants that had completed the inference task first then immediately
took the recognition task, in which they indicated whether or not
they had ever heard the name of the respective object.
Recognition rates and latencies mimicked those observed in
Studies 1 and 2 (see Table G in the supplemental materials), except
in the companies environment, in which participants recognized
more objects than in the previous studies. This boost in recogni-
tion, however, is consistent with the fact that Study 3 recruited
only German students (and no Swiss students).
To what degree do people’s inferences agree with the fluency
heuristic? For each participant, we computed the percentage of
inferences that were in line with the fluency heuristic among all
cases in which it could be applied (i.e., pairs in which both objects
were recognized), excluding pairs with differences in recognition
latencies smaller than 100 ms. Table 4 shows fluency applicability
and fluency validity across environments. The values track those
obtained in Study 2 (compare Tables 2 and 4). The fluency
heuristic applicability ranged between about a fourth and a third of
all inferences. The mean fluency heuristic accordances (i.e., pro-
portion of inferences consistent with the fluency heuristic) were
.74, .63, and .68 in the cities, the companies, and the music artists
As Figure 4a shows, in all environ-
ments there was substantial interindividual variation in the pro-
portion of judgments that agreed with the heuristic. The rate of an
individual’s fluency heuristic accordance ranged between 1.00 and
.30 across environments. Only a few participants appear to have
systematically decided against fluency: Across environments,
merely 12% of participants’ accordance rates were below .50.
Finally, Table 4 also shows participants’ average score of correct
inferences (in pairs in which both objects were recognized),
which ranged between .58 (companies environment) and .71
Next to comparing fluency accordance with a 50% baseline, one
can also test whether observed fluency accordance is higher than
that expected by chance: Let us assume that a person does not
make use of the fluency heuristic. By mere chance one would
expect that his or her inferences and those predicted by the heu-
ristic coincide in more than 50% of the cases (assuming the
person’s accuracy and fluency validity are better than chance).
There are two ways in which the person’s and the heuristic’s
inferences can coincide: (a) both choose a(the larger object) or (b)
both choose b(the smaller object). The probability that both
choose aequals the person’s level of accuracy (acc) times the
fluency validity: acc 'v
. The probability that both choose bis the
product of the complementary probabilities: (1 – acc)'(1 – v
Consequently, the overall probability of the person and heuristic
choosing the same object by chance (henceforth, baseline accor-
dance) equals the sum of both products. Averaged across individ-
uals, the rates of baseline accordance were 59%, 53%, and 52% in
the cities, the companies, and the music artists environments,
For each person, we then calculated the difference between his
or her baseline accordance and his or her observed fluency accor-
dance. In the cities environment, the observed accordance was 16
percentage points (SD !15%; CI !12%, 19%; Mdn !16%; d!
1.03) higher than the individual-specific baseline accordance. In
the companies environment, the respective difference was 11 per-
centage points (SD !13%; CI !8%, 13%; Mdn !12%; d!
0.83); finally, in the music artists environment the difference was
15 percentage points (SD !11%; CI !13%, 18%; Mdn !15%;
d!1.38). That is, when comparing the empirically found fluency
accordance against a benchmark that takes the occurrence of
coincidental accordance into account, the predictive power of the
fluency heuristic remains sizeable.
To what degree do people’s inferences agree with the recogni-
tion heuristic? Figure 4b shows individuals’ accordance rates
across environments. A vast majority of inferences can be captured
by the recognition heuristic, when it can be applied (see Table 4 for
applicability rates). The mean proportion of recognition heuristic
accordance was .91, .90, and .93 in the cities, companies, and
music artists environments, respectively. Averaged across envi-
ronments, 27% of respondents conformed to the recognition heu-
ristic every time. Participants’ average score of correct inferences
(in pairs in which one object was recognized and the other was not)
ranged between .59 (music artists environment) and .80 (cities
Challenging Goldstein and Gigerenzer’s (2002) assumption that
the recognition heuristic takes into account only whether an object
is recognized or not, Newell and Fernandez (2006, p. 333) found
a negative correlation between the proportion of times a city was
chosen over unrecognized cities and the speed with which partic-
ipants correctly categorized the name of this city (r!–.382).
Consistent with Newell and Fernandez and across all environ-
ments, we also found that a recognized object was more often
inferred to be the larger one (relative to the unrecognized object),
the faster it was recognized: cities (Spearman r!–.343, p!
.001), companies (r!–.268, p!.01), and music artists (r!
–.325, p!.001), respectively. Convergent evidence that recogni-
tion strength correlates with following the recognition heuristic
comes from an fMRI study by Volz et al. (2006). They found that
decisions in accordance with the heuristic correlate with higher
The order of the recognition and inference tasks had no statistically
significant effect on the accordance to the fluency and recognition heuris-
tics in the cities, companies, and music artists environments, respectively.
None of the implications of the reported analysis changed when analyzing
the two task orders separately.
THE FLUENCY HEURISTIC
activation in areas of the brain that have previously been associ-
ated with greater recognition confidence.
Is accordance to fluency a function of the difference in recog-
nition latencies? Study 2 showed that the larger the differences
between the objects’ recognition speed, the more accurately people
can discriminate between the two objects’ recognition latencies
(see Figure 3). Does the regularity also relate to the likelihood with
which people accord to the fluency heuristic? Figure 5 plots
fluency heuristic accordance as a function of differences in recog-
nition latencies. Accordance rates, indeed, do increase with larger
Do inferences that agree with the fluency heuristic and the
recognition heuristic require less response time? To answer this
question, for each participant we calculated the median response
time—separately for each of the three environments—for infer-
ences consistent and inconsistent with both the recognition and the
fluency heuristics. Figure 6 shows the mean of the differences
between these two values. Across all environments, inferences
conflicting with the fluency heuristic take markedly longer than do
inferences consistent with it. The same regularity also holds for the
recognition heuristic, replicating Pachur and Hertwig’s (2006)
finding. As the confidence intervals indicate, the mean of each of
the six differences is significantly greater than zero. Moreover, the
effect sizes (Cohen’s d; Cohen, 1988) for the differences in re-
sponse times are greater for the recognition heuristic than for the
fluency heuristic across all three environments (cities: d!0.74 vs.
d!0.54; companies: d!0.62 vs. d!0.48; and music artists: d!
0.85 vs. d!0.53).
To summarize, in about two thirds to three fourths of inferences
in which the fluency heuristic was applicable, people’s actual
choices conformed to those predicted by the heuristic. We also
found that the larger the difference between recognition latencies
(for two objects), the greater the likelihood that the actual infer-
ence adheres to that predicted by the fluency heuristic. Moreover,
consistent with the notion of recognition and fluency’s retrieval
primacy, we found that inferences that agree with both heuristics
take less time than do those that conflict with the heuristics (see
Pachur & Hertwig, 2006). Last but not least, it is worth pointing
out that the fluency heuristic was applicable in 23% to 33% of
inferences, depending on the environment (Table 4). These rates
appear modest. The rate of applicability, however, is a function of
both the heuristic and the environment in which it is used. If we
had chosen, for instance, German or Swiss cities with more than
100,000 inhabitants, rather than U.S. cities, the rate of fluency
applicability would have skyrocketed (given our German and
Swiss respondents). Even when applicable, however, people would
likely have not relied on any inferential tool because of their direct
knowledge of the criterion variable (Pachur & Hertwig, 2006),
thus rendering tests of strategies such as the fluency heuristic mute.
Study 4: Are People’s Inferences in Line With the
Fluency Heuristic: Experimental Evidence?
The goal of the final study is to provide evidence whether or not
fluency itself, rather than other factors possibly associated with
fluency (e.g., the amount of knowledge about the objects, ease of
retrieving this information, etc.), can guide inferences. Specifi-
cally, in Study 3, what made respondents choose the more fluently
retrieved object need not have been fluency per se, but may have
been, for example, the mere amount of knowledge associated with
it. Reliance on fluency would then just be a spurious phenomenon.
To find out whether fluency per se can drive inferences about
environmental variables, we experimentally manipulated fluency
rather than measuring naturally existing fluency.
Participants and design. Fifty students from the University of
Basel participated in the study (36 women and 14 men; mean
age !24.1 years). Participants were presented with 49 pairs of
cities. For each pair, participants were asked to infer which of the
two cities has more residents (inference task). This task was
identical to that in Study 3. Furthermore, each participant indicated
which objects he or she recognized (recognition task). Students
received a flat fee of CHF 15 (US$13) or course credit.
Material. For experimentally induced fluency to have a rea-
sonable chance of overcoming people’s naturally existing retrieval
fluency, we selected items that had similar degrees of pre-existing,
relatively low levels of fluency. Specifically, we took advantage of
Volz et al.’s (2006) pool of 525 cities drawn from different regions
Repeated measures analyses of variance showed linear trends in ac-
cordance rates over the four bins: cities environment, F(1, 65) !26.66,
MSE !0.079, partial (
!.29; companies environment, F(1, 61) !
24.15, MSE !0.080, partial (
!.28; and music artists, F(1, 61) !25.55,
MSE !0.066, partial (
Applicability, Accordance, and Validity Rates for the Recognition and Fluency Heuristics, Respectively, Across Three Environments
Investigated in Study 3
Fluency heuristic Recognition heuristic
MCI MCI MCI MCI MCI MCI MCI MCI
Cities .23 .21, .26 .74 .71, .78 .67 .64, .70 .71 .67, .74 .47 .45, .49 .91 .89, .93 .84 .83, .86 .80 .77, .83
Companies .30 .27, .32 .63 .60, .66 .60 .57, .63 .58 .55, .61 .45 .42, .47 .90 .87, .93 .74 .72, .76 .70 .67, .73
Music artists .33 .31, .35 .68 .65, .70 .56 .53, .59 .59 .56, .62 .39 .37, .41 .93 .91, .96 .57 .55, .60 .59 .56, .61
Note. Participant accuracy denotes the level of inferential accuracy that respondents reached. CI !95% confidence interval.
1200 HERTWIG, HERZOG, SCHOOLER, AND REIMER
of the world. From this pool we first removed 10% of cities with
the longest names (thus reducing uncontrolled variance in recog-
nition time due to vastly different reading times). Then, we re-
moved all cities that were not recognized by at least two thirds of
those participants in a previous study, to increase the chance of
obtaining pairs of cities in which both cities are recognized and the
fluency heuristic is thus applicable. Finally, we winnowed down
the pool further by splitting it into two halves according to recog-
nition latency; the subset of the fastest recognized items was
excluded to avoid ceiling effects in recognition latency. By using
this procedure, we arrived at 68 cities (target items), with about
equally long names and substantially reduced variance in pre-
existing fluency (i.e., recognition latency). In addition, we
randomly sampled from the set of excluded items 15 of the quickly
recognized cities and 15 rarely recognized cities to form 15 filler
pairs as well as 100 cities to serve as fillers in the fluency
manipulation task (see below).
We assigned the 68 target items to two sets. They had compa-
rable (a) recognition rates, (b) average median recognition speeds,
(c) average number of letters in the names, and (d) average
population sizes. Finally, we constructed pairs of cities by ran-
domly selecting one city from each of the two sets of target items,
thus arriving at 34 pairs. This whole procedure was implemented
25 times, thus creating 25 different lists of 34 pairs. Each of the 25
lists was administered in two versions. In one version, city Xin
each pair was selected for the fluency manipulation; in the other
version, city Ywas selected. That is, in each pair, one item was the
experimental, the other the control item. By thus counterbalancing
the manipulated city within each pair, the fluency manipulation
was not confounded with any idiosyncrasy of the constructed pairs.
Our fluency manipulation was a syllable counting task. Partic-
ipants saw a sequence of 134 names of cities in a random order
(the 34 target cities for which we intended to boost fluency, and the
100 filler city items) and were asked to judge whether each name
had an even or uneven number of syllables. This fluency manip-
ulation task was adapted from Yonelinas (2001, Experiment 3).
Procedure. After an introductory text explaining the syllable
task, participants were presented with a sequence of 134 names of
cities. By pressing one of two keys, people indicated whether the
city name consisted of an even or uneven number of syllables.
Following the syllable task, there was a 1-min break after which
people worked on the inference task (as implemented in Study 3),
involving 34 pairs of target items and 15 filler pairs. Once the
inference task was completed, participants took the recognition
task (as implemented in Study 3). The recognition judgments were
used to identify those pairs in which a respondent recognized both
items, a precondition for the fluency heuristic. Finally, partici-
pants’ memories of the experimental manipulation were probed.
Specifically, they indicated for each of the 68 target cities whether
they thought that it was included in the syllable task at the outset
of the study. To avoid invalid assumptions about the base rates,
they were informed that 34 of the 68 cities were indeed included.
After indicating whether a city was or was not included, they were
asked to indicate on a 3-point scale how confident they were in
Figure 4. Accordance to the fluency and recognition heuristic. Percentage of inferences consistent with the
fluency heuristic (Panel a) and recognition heuristic (Panel b) for participants in the cities, companies, and music
artists environments, respectively. The individuals are ordered from left to right according to how often their
judgments agreed with the respective heuristic. The horizontal line at 0.5 represents chance level accordance.
THE FLUENCY HEURISTIC
their assessments. The rationale for monitoring memory of the
experimental manipulation was that if people attributed their
senses of fluency to the experiment rather than to their naturally
acquired senses of fluency, they would be less likely to base their
inferences on fluency (e.g., Jacoby & Whitehouse, 1989; Lom-
bardi, Higgins, & Bargh, 1987).
How did the fluency manipulation affect people’s inferences of
city size? To answer this question, we computed people’s tendency
to select the “manipulated” city (e.g., the city processed in the
syllable task) over the non-manipulated one. Across all respon-
dents, in .55 of all inferences, the manipulated city was chosen
over the non-manipulated one (SD !.13; CI !.52, .59), one-
sample ttest against .5: t(49) !2.94, p!.005. According to
Cohen’s (1988) classification, this effect of the fluency manipula-
tion amounts to a small to medium size (d!.37). In addition,
double as many people had a fluency adherence rate above .5 than
below .5 (28 vs. 14; binomial test: p!.02).
The effect of fluency is moderated by people’s memory of the
experimental manipulation. Based on each individual’s 68 signal
detection judgments and his or her confidence ratings (see above),
we calculated A
, fitting a parametric smooth receiver operating
characteristics (ROC) curve (ROCFIT; see Metz, Shen, Wang, &
Kronman, 1994; using JROCFIT; Eng, n.d.). A
is a measure of
how well people can discriminate between signal and noise (tech-
nically speaking, it is the area under the ROC curve). The average
value was .66 (SD !.08; CI !.64, .68.), suggesting that the
ability to discriminate is better than chance (.5) but also far from
perfect (1.0). Figure 7 plots people’s fluency adherence as a
function of A
. The slope of the robust regression line suggests
that those who were poorer at remembering which items had been
part of the syllable task had a higher adherence rate than did those
who remembered better (slope !– 0.31; SE !0.19, p!.06,
A final analysis concerns the combined effect of experimental
and pre-experimental fluency. By using recognition speed data
from a previous study, we determined that the faster recognized
cites were chosen in Study 4 to be larger, on average, in 55% of
cases. How did the experimentally induced fluency change this
proportion? If the manipulated city was indeed the one recognized
faster than the non-manipulated one (based on the previous re-
sults), fluency adherence grew from .55 to .60 (SD !.12; CI !
.57, .63). In contrast, if the manipulated city was the one recog-
nized slower than the non-manipulated one (based on the previous
results), fluency adherence grew from .45 to .49 (SD !13; CI !
To conclude, Study 4 shows that experimentally manipulated
fluency shapes inferences. This result is compatible with the vast
literature on the influence of fluency on preferences (see, e.g.,
Alter & Oppenheimer, 2007, for a review) and with previous
results reported by, for instance, Kelley and Lindsay (1993), who
demonstrated experimentally the impact of fluency of confidence
Figure 5. Increasing fluency heuristic accordance as a function of increasing differences in recognition
latencies. Differences in objects aand b’s recognition latencies were classified into four bins. Then, we
calculated the mean fluency accordance per bin (across participants). Error bars indicate 95% confidence
intervals. The analyses reported in Footnote 7 show a significant linear trend across bins. The horizontal line at
0.5 represents chance level accordance.
Figure 6. Inferences consistent with the fluency and recognition heuris-
tics require less time. Bars represent the differences between median
response times for inferences inconsistent and consistent with the fluency
(left) and recognition heuristic (right), respectively. Positive differences
indicate that inconsistent inferences took longer than consistent inferences.
Error bars indicate 95% confidence intervals.
1202 HERTWIG, HERZOG, SCHOOLER, AND REIMER
in the domain of general knowledge questions. The effects in
Study 4 are small to medium in size, but it is worth keeping in
mind that our experimental manipulation of fluency consisted of
merely the judgment of whether or not a city name has an even or
uneven number of syllables. Thus it was just the proverbial drop in
the ocean compared to the lifetime of exposure to these city names.
Automatically generated in the process of retrieval from mem-
ory, recognition and retrieval fluency are two pieces of information
that are likely to precede the arrival of many other probabilistic
cues retrieved from memory. The recognition heuristic and the
fluency heuristic are two mind tools that have been proposed to
exploit both pieces of mnemonic information. In several studies,
we have investigated the fluency heuristic side by side with the
recognition heuristic. In Study 1, we found that retrieval fluency
can be a valid predictor of objective properties of the world, but the
degree to which it is indicative of the criterion varies across
environments. To the best of our knowledge, this is one of the first
systematic demonstrations of the ecological validity of fluency
information, based on the chain of correlations between criterion,
mediators, and retrieval fluency within precisely defined reference
classes. In Study 2, we examined whether people can reliably
discriminate differences in retrieval fluency, a prerequisite for
employing the fluency heuristic. Indeed, latencies that differ by
more than 100 ms can be distinguished. The larger the differences,
the better people can discriminate and the larger fluency validity
proved to be. In Study 3, we found that between about three
fourths and two thirds of the inferences, in which the fluency
heuristic could be applied, conformed to the heuristic. Further-
more, accordance with the fluency heuristic increased with larger
differences in recognition latencies—a manifestation of ecological
rationality insofar as retrieval fluency is more likely to yield
accurate inferences with larger differences (given an ecological
correlation). Finally, in Study 4, we experimentally manipulated
fluency and found that it had a direct impact on inferences.
In what follows, we turn to a possible explanation of the robust
observation that recognition validity surpasses fluency validity,
and we discuss what people can gain from the fluency heuristic
and under what circumstances it may be used.
Why is Recognition More Valid Than Fluency
In our studies, recognition validity consistently exceeded flu-
ency validity. Why is that? Recognition and retrieval fluency both
depend on activation, which encodes environmental frequencies,
which, in turn, can be associated with the criterion (ecological
validity). Within ACT-R, the difference between recognized and
unrecognized objects maps onto the difference between items that
exceed a retrieval threshold and those that fall below. For a
moment, let us treat the threshold as the median value of a
predictor variable that splits the criterion distribution into two
parts. On average, recognized objects will score higher on the
criterion than will unrecognized ones, just as items above the
median will, on average, have a higher criterion value than those
below. The fluency heuristic is akin to restricting the calculation of
a correlation between predictor and criterion to those items that
score above the median of the predictor variable. In all likelihood,
the correlation and, by extension, the fluency validity will be lower
relative to calculating the correlation across the whole range of the
criterion value. Because the fluency heuristic requires both objects
to be recognized, the relevant range of objects’ activations is
restricted, and, consequently, the link between activation and cri-
terion is less pronounced. In contrast, the recognition heuristic
requires one object to be recognized and the other not. Conse-
quently, the range of activations exceeds that for the fluency
heuristic, and therefore the link between activation and criterion
will be more pronounced, relative to the fluency heuristic. On this
view, recognition validities exceed fluency validities, because the
fluency heuristic, unlike the recognition heuristic, applies to ob-
jects whose activations exceed the retrieval threshold. Metaphor-
ically speaking, the fluency heuristic deals with a world full of
shades of white, whereas the recognition heuristic faces a black-
What Do People Gain From Relying On Fluency?
When this question is interpreted simply with respect to accu-
racy, the answer depends on which of two benchmarks is used. If
the alternative to the fluency heuristic is guessing (for example, in
cases in which no further knowledge is available), the heuristic
clearly tops chance performance. As Study 1 demonstrated, flu-
ency validity surpasses chance performance, ranging from .58 to
.66 (Figure 1). Although one would hesitate to go mushroom
picking with such accuracy, one could make a comfortable living
picking stocks. If the alternative to relying on fluency is mustering
additional cue knowledge, however, the performance of the flu-
ency heuristic depends on the accuracy of other decision strategies
Figure 7. Fluency heuristic accordance as a function of the ability to
remember correctly which items were subjected to the experimental manipu-
lation of fluency. A
is the area under the ROC curve and can be interpreted
as the probability of a correct response. The line represents the robust
regression of the fluency heuristic accordance as a function of the ability to
correctly remember which items were included in the syllable task. The
horizontal lines at 0.5 and 1.0 represent chance level and maximum
THE FLUENCY HEURISTIC
that depend on this knowledge (see Gigerenzer & Goldstein, 1996,
for candidate strategies). Should the validity of these knowledge-
based strategies exceed that of fluency, by using fluency a person
sacrifices some accuracy.
We have no direct measure of the validity of these knowledge-
based strategies but we can use a proxy to extrapolate it, namely,
the level of accuracy in those cases in which inferences are
inconsistent with the fluency heuristic. In all three environments in
Study 3, the average fluency validity was as high or higher than the
average accuracy of additional knowledge: .66 vs. .59 (Mdn !.67,
SD !.34), .58 vs. .47 (Mdn !.5, SD !.26), and .58 vs. .52
(Mdn !.5, SD !.25) in the cities, companies, and music artists
environments, respectively. These numbers stem from a selective
set of items and thus need to be interpreted with care. Yet, they
provide a first indication that even when compared with the
accuracy of knowledge-based strategies the fluency heuristic
stands its ground in the environments investigated. At the same
time, let us emphasize that there are domains in which reliance on
fluency can undoubtedly result in disadvantageous decisions.
Companies, for instance, pay great sums to influence the public’s
recognition and fluency of product names (Goldstein, 2007). No
doubt, the more fluent product name need not always signal the
In any consideration of what the fluency heuristic has to offer,
two of its properties deserve attention. First, the heuristic is least
likely to be used in cases in which its validity hits bottom, that is,
when people find it difficult to tell the recognition latencies apart
(see Figures 2 and 3). In the cities environment, for instance, the
adherence rate in the 50% of inferences with the smallest differ-
ences in recognition latencies is .62, relative to .77 in the 50% of
inferences with the largest differences. The corresponding fluency
validities for the smaller and larger differences are .56 and .69,
respectively. In other words, the heuristic’s requirement that the
recognition latencies be discernible inures the user from using
fluency when it is least beneficial. Second, the wisdom of the
fluency heuristic should be evaluated in light of the fact that the
retrieval of other cue knowledge is effortful and time consuming.
In other words, the decision maker faces an effort–accuracy
tradeoff (Payne, Bettman, & Johnson, 1993). Using the fluency
heuristic may not be the best people can do, but it enables them to
arrive at inferences swiftly and to surpass chance performance if
they have no other cue knowledge; in addition, they are protected
from using it when it is least appropriate.
When Do People Resort to the Fluency Heuristic?
In light of the fluency heuristic’s competitors (Gigerenzer &
Goldstein, 1996), what are the circumstances under which people
use it? Some critics of the adaptive toolbox metaphor suggest that
the issue of strategy selection runs the risk of making the dubious
assumption of some kind of omniscient homunculus or u¨ber-
heuristic that selects from the toolbox (e.g., Newell, 2005). How-
ever, possible solutions to the thorny issue of strategy selection
have been proposed that make do without a mysterious homuncu-
lus. One solution is that people learn to match specific heuristics to
specific statistical structures in the world through feedback
(Rieskamp & Otto, 2006). Alternatively, the various mind tools in
the toolbox may be arranged according to the same criteria that the
ACT-R framework (Anderson et al., 2004; Anderson & Lebiere,
1998) uses in order to determine the activation strength of a
memory record, namely, recency and frequency of occurrence.
That is, the various tools may be ordered such that the most
frequently and most recently used tool is examined first; if it does
not enable an inference, the next tool in the hierarchy will be
examined, and so on.
There is still another, albeit not exclusive, route to strategy
selection. Knowledge and time, or lack thereof, may also guide the
selection process. Recently, Marewski and Schooler (2007) found
that the fluency heuristic appears most likely to be used when both
objects are recognized and no other probabilistic cue knowledge is
available. When knowledge was available, a knowledge-based
strategy described people’s inferences better than the fluency heu-
ristic. This result suggests the possibility that people may be
inclined (a) to use the recognition heuristic if one object is recog-
nized and the other is not (see Figure 4b) and if people have no
definite and conclusive knowledge of the target variable that
renders use of cues unnecessary (Pachur & Hertwig, 2006); (b) to
use the fluency heuristic if both objects are recognized and no
other knowledge (e.g., in terms of probabilistic cues) is available;
and (c) to use knowledge-based strategies when both objects are
recognized and additional knowledge (e.g., in terms of probabilis-
tic cues) is available.
Let us end with a clarification. We have specified the fluency
heuristic in terms of a condition–action production (“If–then”)
rule, so that it can be instantiated as a computer program. This
specification, however, does not mean that we have already com-
pletely understood which antecedence conditions need to be met
for the action to be executed. Our studies have confirmed that the
difference between recognition latencies needs to be at least 100
ms, otherwise the action cannot not be executed (e.g., Figures 3
and 5). Future investigations will help to clarify which additional
conditions need to be met. Such other conditions may relate, for
instance, to the presence or absence of other probabilistic cue
knowledge, or to the validity of present cue knowledge.
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Accepted May 5, 2008 !
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