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Cognition xxx (2007) xxx–xxx
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A rose in any other font would not smell as sweet:
Eﬀects of perceptual ﬂuency on categorization q
Daniel M. Oppenheimer a,*, Michael C. Frank bab

Department of Psychology, Princeton University, Princeton, NJ 08544-1010, USA
Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139, USA
Received 27 May 2005; revised 19 May 2007; accepted 25 May 2007

Abstract
Fluency – the ease with which people process information – is a central piece of information
we take into account when we make judgments about the world. Prior research has shown thatﬂuency aﬀects judgments in a wide variety of domains, including frequency, familiarity, andconﬁdence. In this paper, we present evidence that ﬂuency also plays a role in categorization
judgments. In Experiment 1, participants judged a variety of diﬀerent exemplars to be worse
category members if they were less ﬂuent (because they were presented in a smaller typeface).
In Experiment 2, we found that ﬂuency also aﬀected judgments of feature typicality. In Experiment 3, we demonstrated that the eﬀects of ﬂuency can be reversed when a salient attribution
for reduced ﬂuency is available (i.e., the stimuli are hard to read because they were printed by aprinter with low toner). In Experiment 4 we replicated these eﬀects using a within-subject
design, which ruled out the possibility that the eﬀects were a statistical artifact caused byaggregation of data. We propose a possible mechanism for these eﬀects: if an exemplar andits category are closely related, activation of one will cause priming of the other, leading toqThis material is based upon work supported by the Norman Anderson research fund and by Grant #0518811 from the National Science Foundation. The authors would like to thank two anonymous
reviewers, Sara Etchison, David Collister, Anuj Shah, Steve Sloman, Melissa Miller, Jessica Laughlin,
Benoit Monin, Rebecca Norwick, Max Abelev, Teenie Matlock, and Pamela Sawyer for their help in all
aspects of this research, and thanks to mom and dad on principle.
*
Corresponding author. Tel.: +1 609 258 7465.
E-mail address: doppenhe@princeton.edu (D.M. Oppenheimer).
URL: www.princeton.edu/sites/opplab (D.M. Oppenheimer).
0010-0277/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.cognition.2007.05.010

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increased ﬂuency. Over time, feelings of ﬂuency come to be used as a valid cue that can become
confused with more traditional sources of information about category membership.
Ó 2007 Elsevier B.V. All rights reserved.
Keywords: Perceptual ﬂuency; Categorization; Priming; Categorical induction

1. Eﬀects of perceptual ﬂuency on categorization
Imagine the subjective experience of reading this article in a poor photocopy:
words are blurry, in places illegible and obscured at the sides of the page by therise of the journal’s binding. In contrast, imagine the article printed crisply oﬀ a
new laser printer: the text is clear, centered on the page, high in contrast, andeasy to read. Naively we might imagine that these superﬁcial factors involvedin presentation will not aﬀect readers’ perception of our work. However, a growing psychological literature suggests that the ease with which we are able toaccess information, the meta-cognitive experience of ease of processing that wecall ﬂuency, can aﬀect a wide variety of our judgments. Eﬀects of ﬂuency havebeen shown in domains as disparate as intelligence (Oppenheimer, 2005), frequency (Tversky & Kahneman, 1973), familiarity (Monin, 2003), duration (Witherspoon & Allan, 1985), risk for disease (Rothman & Schwarz, 1998), andattractiveness (Reber, Winkielman, & Schwarz, 1998). In fact, the ﬂuency causedby the presentation of this article might inﬂuence a wide variety of judgments
about its contents.
Fluency may also aﬀect categorization. The most typical exemplars of a category are often the most frequent in our experience, the most easily accessible inour memories, and the most primed by related experiences. Because of this, theexperience of ﬂuency and the judgment of good category membership frequently
co-occur over the course of a lifetime’s experience. These correlations make ﬂuency a highly reliable cue to category membership, one that our experiments suggest can inﬂuence decision-makers. More generally, our experiments suggest that
individuals do not fully distinguish between the cues for category membershipcited by traditional theories of categorization – such as similarity to a prototype(e.g., Rosch & Mervis, 1975) or to other members of that category (e.g., Medin &Schaﬀer, 1978) – and experiential cues such as ﬂuency. In this article, we present
evidence that changing the ﬂuency of a category member can alter participants’
judgments of its typicality (Experiments 1 and 4); that changing the ﬂuency of
category features can change judgments of the features’ typicality (Experiment2); and that, given a salient alternative explanation, participants can discountthe eﬀects of ﬂuency (Experiment 3). In our discussion, we present several suggestions for how such experiential cues could be integrated into a variety of existingmodels of categorization.
Perceptual ﬂuency is generally deﬁned as the subjective experience of ease or speed
in processing perceptual information (Jacoby & Dallas, 1981; Benjamin, Bjork, &Please cite this article in press as: Oppenheimer, D. M., & Frank, M. C., A rose in any otherfont would not smell as sweet: ..., Cognition (2007), doi:10.1016/j.cognition.2007.05.010

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Hirshman, 1998; Reber, Wurtz, & Zimmerman, 2004).1 Experimenters have manipulated ﬂuency in a variety of ways, most often by changing font size of text (e.g.,
Norwick & Epley, 2003; Epley & Norwick, submitted for publication), adding visual
noise for images and text (e.g., Kleider & Goldinger, 2004; Buchner, 1994), or
manipulating phonological properties for nonce words (e.g., Whittlesea & Leboe,
2002).
Fluency can be thought of as a metacognitive experience, in which one has a subjective feeling that a cognitive process is running smoothly. When people have suchan experience, they often attempt to interpret it, and those interpretations can inﬂuence their judgments (for a review see Schwarz, 2004). This is especially true when
the ﬂuency experience is surprising (Whittlesea & Williams, 2000). People often havediﬃculty determining the source of a given ﬂuency experience and, as such, arebiased in their judgments (Jacoby & Dallas, 1981). For example, Jacoby and Dallas
showed that people would misattribute ﬂuency due to recent exposure to ﬂuency
caused by fame, and that fame judgments were biased accordingly.
As the widespread impact of ﬂuency on a variety of cognitive phenomena grows,
researchers have begun to accumulate evidence that ﬂuency may also aﬀect categorization judgments. However, most common accounts of categorization do not currently account for the eﬀects of metacognitive attributes such as ﬂuency.
The issue of how we represent the world and break down continuous perceptual
stimuli into discrete categories has been historically central to the ﬁeld of cognitive
science (Goldstone & Kersten, 2002). In the modern literature on categorization,
most theories can be assigned to one of three groups: prototype models; exemplar
models; and theory- or knowledge-based models. In prototype theories, the central
tendency – or prototype – of a category is extrapolated by ﬁnding the common features of the known exemplars of the category. Further exemplars are judged to be
category members if they are closer to the prototype of that category than to the prototype of any other (e.g., Rosch & Mervis, 1975). In contrast, exemplar theories do
not explicitly represent a central tendency – instead judging category membership bycomparing novel exemplars to all previous examples in that category (e.g., Medin &Schaﬀer, 1978). Finally, theory- and knowledge-based models of categorization take
into account the semantic content of categories and posit that the process of categorization is governed by background knowledge about the domain of categorization(e.g., Keil, 1989; Murphy & Medin, 1985). For example, exemplars may be judged tobe members of a category to the extent that they can be generated by the category’s
causal model (e.g., Rehder & Hastie, 2001).

1
In addition to perceptual ﬂuency, there are other cognitive operations that can have an inﬂuence on
ﬂuency. Retrieval ﬂuency, also known as availability, reﬂects how easily instances of an item are brought
to mind (Tversky & Kahneman, 1973). Conceptual ﬂuency is deﬁned by the contextual situation andsemantic predictability of a stimulus (Whittlesea, 1993). While these variations in ﬂuency are subtly
distinct, all of them have been shown to have similar eﬀects on processing (Alter & Oppenheimer,
submitted for publication; Winkielman, Schwarz, Fazendeiro, & Reber, 2003). As such, for the remainderof this paper, we shall refer to them as a single construct.

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Taken as a whole, theories of categorization remain largely silent on issues of presentation. Presumably, under a standard exemplar or prototype model of categorization, categorization of a novel exemplar should not depend on properties of thepresentation format of the stimulus – such as the font size in which the exemplar’sname was written – that are not relevant to the feature space within which it is being
categorized. Theory- and knowledge-based models of categorization also currently
fail to address issues of presentation, unless presentation bears a speciﬁc causal relationship to why exemplars should be judged to be category members. However, presentation can have a large impact on ﬂuency (for a review, see Schwarz, 2004), and to
the extent that ﬂuency is used as a cue in categorization, can play a role in categorization judgments as well. Overall, current models of categorization do not parsimoniously account for the eﬀects of ﬂuency.

2. Why there might be a link between ﬂuency and category membership
There are a variety of reasons to suspect that there may be signiﬁcant eﬀects of
ﬂuency on categorization. First, researchers have suggested that ﬂuency heuristics areused in a variety of diﬀerent situations as a decision-making tool (e.g., Tversky &Kahneman, 1973; Whittlesea, Jacoby, & Girard, 1989; Lindsay & Kelley, 1996).
Given the pervasive eﬀects of ﬂuency on these other aspects of decision-making,one might well suppose that categorization judgments would be anomalous if they
were not aﬀected by ﬂuency.
Second, ﬂuency can act as a robustly accurate way of making decisions about priorexperiences (Reber et al., 1998). Exemplars that are highly typical of a category areprimed more by the category than less typical exemplars. For example, if you arelooking for an exemplar of the category ‘‘bird’’, ‘‘robin’’ should be more primed than‘‘penguin.’’ This greater priming in turn leads to a greater increase in ﬂuency for theprimed exemplar; that is, the primed exemplar will be easier to process in a variety ofdiﬀerent ways (Meyer & Schvaneveldt, 1971; Stark & McClelland, 2000). If ‘‘robin’’ ismore ﬂuent – easier to produce or to remember – it might be in part due to thefact that it has been diﬀerentially primed by the activation of ‘‘bird’’. Over time, easeof processing should become correlated with category member typicality. This correlation may cause exemplar ﬂuency to be taken into account in judgments of category
typicality, whether by raising the judged typicality of ﬂuent exemplars or lowering thejudged typicality of unusually disﬂuent ones. In other words, the ﬂuency of an exemplar (‘‘robin’’) might aﬀect how typical of the category (‘‘bird’’) we think it is.
There have been several documented attempts to test whether ﬂuency does, infact, aﬀect categorization, but to date the evidence is mixed. In two experiments,
Buchner (1994) trained participants on consonant strings produced by a particular
grammar and then tested them on the grammaticality of novel strings. Fluency
was manipulated by gradually removing visual noise from each string in training
until participants indicated that they could identify the string. Buchner found that
although ﬂuent strings – those strings which were identiﬁed more quickly – were
judged to be more familiar, they were not classiﬁed as legal or illegal strings more
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5easily. However, the grammaticality judgment that Buchner asked his participants tomake diﬀers from a categorization task in an important way. In contrast to judging
category typicality, succeeding in a grammaticality judgment task requires knowing
the correct answer. Greater ﬂuency may have led to more false positives as well as
more correct grammaticality judgments, leaving the overall rate of correct performance virtually unchanged (c.f. Whittlesea & Leboe, 2002).
Whittlesea and Dorken (1993) presented similarly indirect – although positive –evidence for eﬀects of ﬂuency on categorization. In one of a number of studies, theypresented participants with nonce words generated by an artiﬁcial grammar. During
the training phase, participants were asked to spell or pronounce these words, andthen to repeat the pronounciation or spelling of the word from memory immediately
afterwards. In the subsequent test phase, participants were told that the items they
had seen were generated by a complex set of rules and were again asked to pronounce or spell a word before judging it to be either a ‘‘good’’ or ‘‘bad’’ word,
depending on whether it conformed to those rules. They found that if an item wasprocessed the same way at training and at test – that is, if it was either pronounced
in both parts of the experiment or spelled in both – participants tended to classify it
more accurately than if it was processed diﬀerently in the two diﬀerent parts. However, since Whittlesea and Dorken reported no direct measure of the ﬂuency of mismatched objects, this evidence must be taken as only indirectly supporting the
hypothesis that ﬂuency aﬀects categorization. Westerman, Miller, and Lloyd(2003) have since reported that changes in modality or task can reduce the eﬀects
of ﬂuency on recognition memory, but it remains an open question whether a comparable reduction in ﬂuency across mismatched tasks was responsible for the eﬀects
reported by Whittlesea and Dorken.
Most recently, Whittlesea and Leboe (2002) ran a series of elegant studies giving
evidence of strong eﬀects of ﬂuency on the classiﬁcation of nonce words. They asked
participants to learn nonce words with either glide or non-glide consonant transitions (i.e., BAMDEN vs. BAGDEN) by pronouncing them several times over thecourse of a training phase. They then asked their participants to make a forced
choice decision about a set of similar but novel words, judging them to be either legal
or illegal according to the abstract pattern found in the initial word set. They foundthat participants judged the more ﬂuent, more easily pronounced glide stimuli as
legal to the category more often than the less ﬂuent, non-glide stimuli, even whenparticipants had no knowledge about what the category was. In a separate experiment, Whittlesea and Leboe asked participants to perform the same classiﬁcation
task on stimuli that were presented either in uniform or alternating case (i.e., bAmDeN vs. BAMDEN) and found that participants were more likely to classify uniform case stimuli as legal.
Taken together, Buchner (1994), Whittlesea and Dorken (1993), and Whittleseaand Leboe (2002) along with others (e.g., Kinder, Shanks, Cock, & Tunney, 2003;
Newell & Bright, 2001) present fairly solid evidence about the role of ﬂuency in categorization judgments. However, all of these experiments use exclusively novel stimuli, about which participants had no prior experience or knowledge. None of theprevious evidence directly addresses the issue of whether these eﬀects of ﬂuency
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are large enough to play a role in categorization decisions in which participants havea large body of conceptual knowledge about the exemplars, although there are solid
theoretical reasons to believe that it should.
We propose that by manipulating the ﬂuency of well-known category exemplars
we can inﬂuence judgments about their pre-existing category memberships.
Based on the priming relationship between typicality and ﬂuency, good category
members should ‘‘feel’’ more ﬂuent. When ﬂuency is independently manipulated,people are likely to misread their experience of ﬂuency as resulting from highco-activation. Therefore, we should observe eﬀects of ﬂuency on typicality if participants use ﬂuency as one of the variety of cues aiding in their categorization
judgments.

3. Experiment 1
To test whether ﬂuency would have an impact on category judgments in familiar
domains, we used a standard font manipulation (Epley & Norwick, submitted for
publication) and had participants rate exemplars on their typicality as members ofa given category (e.g., how good a category member ‘‘robin’’ was for the category‘‘bird’’). We predicted that making the names of the category members more diﬃcult
to read would decrease participants’ typicality ratings.
3.1. Method
3.1.1. Participants
Seventy one Stanford University undergraduates participated as part of a course
requirement. The survey was included in a packet of approximately 20 unrelated
one-page questionnaires. Packets were distributed in class, and participants were
given a week to complete the entire packet.
3.1.2. Design, stimuli and procedure
Participants were presented with a set of exemplars and a target category, andasked rate how good a category member each exemplar was on a scale of 1–9, (with
1 being least typical and 9 being most typical). Four categories – comprised of twonatural kinds (bird and mammal), one artifact (vehicle), and one ad-hoc (unusual
foods) – with 15 exemplars each were used. Exemplars were selected so as to vary
both on the degree to which they were typical of the category and their commonness in everyday life (Stimulus materials are listed in Table 1). In the lowered
ﬂuency condition, the text of the study – including category members’ names –was printed using 10 point Mistral type, while in the control condition the study
was printed in 12 point Times New Roman type. In both conditions, instructions
were presented in the same font as the category members in order to prevent participants from guessing the manipulation. The stimulus materials in the lowered ﬂuency condition were still clearly legible, but signiﬁcantly degraded from those in thecontrol condition.
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Table 1
The category exemplars used in Experiment 1
Bird

MammalVehicleUnusual food

PigeonHawk
OstrichRobin
Dodo
Sparrow
Eagle
TurkeyGoose
Hummingbird
Wren
PenguinBuzzardMockingbird
ParrotDog
Kangaroo
Elephant
Human
Dolphin
Whale
Mole
Cow
Koala
Sloth
Platypus
Bat
Walrus
RabbitMoose

Car
AirplaneHang glider
Cart
Submarine
Truck
BicycleUnicycle
Carriage
Tractor
Train
Scooter
Skateboard
Motorboat
Bus

Kiwi
JicamaBorshtKumquatHamburger
HummusPeanut butter
Pasta
Garbanzo beans
Sausage
Pomegranate
Steak
Chocolate
Waﬄes
Muﬃn

3.2. Results
Typicality ratings ranged from a mean of 1.6 in the case of from ‘‘pasta’’ in theunusual food category, to a mean of 8.9 for ‘‘car’’ in the vehicle category, conﬁrmingthat the stimuli indeed varied in typicality.
The mean of the typicality ratings across categories was 6.11 out of 9 for low ﬂuency exemplars and 6.26 out of 9 for control exemplars. A paired sample t-test compared mean typicality ratings for each exemplar in the low-ﬂuency condition against
mean typicality ratings for the corresponding exemplar in the control condition, andthe diﬀerences were found to be reliable. (t(59) = 2.00, p < 0.014, Cohen’s d = .52).
3.3. Discussion
Simply changing the font of an exemplar can make people view it as less typical ofa given category. This ﬁnding held over a wide range of categories and exemplars.
Our ﬁndings were congruent with the results of Whittlesea and Leboe (2002), providing a demonstration that the eﬀects they reported generalize to stimuli about which
participants already know a great deal. Even when participants have pre-experimental notions about what features of an exemplar are relevant to category membership(e.g., ‘‘has wings’’ rather than ‘‘printed in a legible typeface’’), ﬂuency still plays asigniﬁcant role.
The proposed mechanism of these eﬀects – namely that the priming relationshipbetween categories and their members leads to ﬂuency, which, in turn, serves as acue for category membership – implies that ﬂuency should be used as a cue wheneveran individual rates the relationship between two items that prime one another. In
experiment two, we test the generality of these eﬀects by examining whether a similar
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font manipulation will inﬂuence ratings of the likelihood that category members will
have a particular feature. If the eﬀects in Experiment 1 were caused by some mechanism speciﬁc to the process of categorizing exemplars, then we would predict thatvarying ﬂuency would not inﬂuence judgments of feature likelihood from categories.
If, on the other hand, the eﬀects are due to the ﬂuency account then varying the ﬂuency of features should also vary judgments of the features’ likelihood, because features of a category should prime the category and vice versa.

4. Experiment 2
4.1. Method
4.1.1. Participants
Forty three Princeton University undergraduates were recruited from an introductory psychology course and asked to complete a single-page questionnaire as partof a group of studies done in exchange for course credit.
4.1.2. Design, stimuli and procedure
Participants were asked to rate on a scale of 1–9 (1 being least likely and 9 being
most likely) the likelihood that any given member of a category had a given feature.
For example, they were asked, ‘‘Imagine you come across a ﬁsh. What are the odds it
has ﬁns?’’ Six separate categories were listed, (ﬁsh, dog, bird, tree, pig, and cat), with
ﬁve features varying in their typicality presented for each category (see Table 2).
There were two between-subject conditions: a control condition and a lowered ﬂuency condition. In the control condition, stimuli were presented in a normal, 12 point
Arial font, while in the lowered ﬂuency condition, stimuli were presented in italic
Blackadder ITC font, printed in 50% gray color, halfway between black and whitetext colors (see Fig. 1). Again, lowered ﬂuency stimuli were fully legible, but significantly degraded from those in the control condition.

Table 2Category features used in Experiment 2
Fish
Has ﬁns
Is less than 1 foot long
Is considered edible
Is colorful
Lives in a stream

Bird
Flies
Eats worms
Lays eggs
Builds nests
Chirps/singsDog
Has a tailBarks
Wears a collar
Weighs less than 100 lbs.
Chases cats

Tree
Has leaves
Is in a forest
Produces fruit or ﬂowers
Houses birds
Is over 10 feet tallPig
Is pink
Has a squiggly tail
Oinks
Lives on a farm
Is dirty/muddy

Cat
Kills birds
Gets stuck up in trees
Is someone’s pet
Meows
Has soft fur

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9Fig. 1. Example stimuli from Experiments 1 and 2 in which lowered ﬂuency was due to a fontmanipulation.

4.2. Results
Features ranged greatly in typicality from ‘‘gets stuck in trees’’ as a feature of cat(mean = 4.0) to ‘‘has ﬁns’’ as a feature of ﬁsh (mean = 8.5). Features in the lowered
ﬂuency condition were judged to be, on average, less likely for any given exemplar(mean control = 6.70, mean lowered ﬂuency = 6.54). A comparison of exemplar
means in each condition in a paired-sample t-test revealed this diﬀerence to be reliable (t(29) = 2.13, p = 0.042, Cohen’s d = .79).
4.3. Discussion
The diﬀerential priming model predicted that lowering ﬂuency would lower exemplar typicality judgments (in Experiment 1) and would also lower judgments of feature likelihood (in Experiment 2). This prediction was borne out by our data.
However, there are several alternative explanations that could also account for these
data. For instance, participants might feel frustration with the diﬃculty of pronounciation (in the case of the Whittlesea and Leboe artiﬁcial words) or experience a general decrease in aﬀect from the diﬃculty of reading the stimulus materials, whichmight cause them to give lower ratings.
To eliminate aﬀective confounds, one must dissociate the predictions of an aﬀective model and a ﬂuency-based model. This can be done through the phenomenon of
causal discounting. In explaining events, we tend to prefer a single cause to multiplecauses (Einhorn & Hogarth, 1986; Kelley, 1973). Thus, the known presence of one
cause for a particular outcome tends to lower people’s likelihood estimates of thepresence of other causes. Research has demonstrated that people often attempt tomake causal attributions about their meta-cognitive experience of ﬂuency (Oppenheimer, 2004a, 2005; Schwarz, 2004). As such, when there is a salient explanation
for ﬂuency that is not related to the judgment at hand, people tend to discountﬂuency as a cue.
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In a series of experiments, Oppenheimer (2004a) showed that individuals failed
to use the availability heuristic (how easily something comes to mind) to makejudgments about frequency when an alternate explanation for the availability ofthe particular stimulus was salient. He asked participants to judge surname frequency (a judgment usually made by using the availability heuristic) in cases in whichsome surnames were held by famous individuals (i.e., Clinton) and in cases in which
they were not (i.e., Callaway). When surnames were famous, participants tended torate them as less common, discarding availability information based on the salienceof an alternate explanation for availability: fame. In other words, individuals disregarded an internal heuristic cue because there were speciﬁc alternate causes for that
judgment.
This suggests that in the presence of a salient alternative explanation for decreased
ﬂuency, participants should actually overcompensate for lowered ﬂuency, giving
higher ratings of typicality than they might otherwise. However, in the case thatthese eﬀects are due to lowered aﬀect, and that unhappy participants give lower ratings, there is no reason to suppose that the presence of a salient explanation for lowered ﬂuency should reverse the ﬁndings.2
As such, in Experiment 3 we dissociate ﬂuency and aﬀect by providing a salient but unstated explanation for the lowered ﬂuency of the stimulus materials: theprinter on which they were printed was running out of toner. This manipulation
was based upon previous work demonstrating that participants observing low
toner print tend to spontaneously attribute lack of ﬂuency to the toner issues,and subsequently engage in discounting (Oppenheimer, 2005, Experiment 5).
Across four studies, Oppenheimer demonstrated that the reduced ﬂuency due tocomplex vocabulary, grammar, and font led to lower intelligence ratings of atext’s author. However, when participants viewed texts in a ‘‘low toner’’ font,they rated the author as more intelligent. Participants spontaneously identiﬁedthat the source of their disﬂuency was unrelated to the judgment at hand (i.e.,
the author’s intelligence) and in an attempt not to be biased by their ﬂuency
experience ended up overcompensating and showing bias in the other direction(c.f. Oppenheimer, 2004a).
If participants accept this as a cause for lowered ﬂuency, we predict that they will
over-discount ﬂuency as a factor in their typicality judgments, leading to ratings oftypicality higher than those in the control condition. However, if the lowered ﬂuencyof the stimulus materials simply elicits a diﬀerent aﬀective response, typicality judgments in the low toner condition should be lower than typicality judgments in thecontrol condition, congruent with Experiments 1 and 2.

2

Note that Schwarz and Clore (1983) reported that participants in negative aﬀective states are more
likely than those in positive aﬀective states to search for and explain their state based on available
information. However, this type of aﬀective discounting would not explain an increase in typicality
judgments. Participants might use available information to explain a negative aﬀective state, but theywould still be in that aﬀective state and hence still be predisposed to give lowered judgments of typicality orfeature likelihood.

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5. Experiment 3
5.1. Method
5.1.1. Participants
One hundred sixty four Stanford University undergraduates participated as partof a course requirement. The survey was included in a packet of approximately 20
unrelated one-page questionnaires. Packets were distributed in class, and participants were given a week to complete the entire packet.
5.1.2. Design, stimuli and procedure
The design and stimuli were identical to those of Experiment 1, save a change inthe experimental condition. In the experimental, or ‘low toner’ condition, category
members’ names were printed in the same Times New Roman 12 point font as in
the control condition, however the surveys were printed in such a way as to simulate
having been printed on a laser printer running out of toner (see Fig. 2).
5.2. Results and discussion
Ratings of typicality were higher in the low toner condition (mean = 6.27) than in
the control condition (mean = 6.12). A paired sample t-test compared mean typicality ratings for each exemplar in the low toner condition against mean typicality ratings for the corresponding exemplar in the control condition, and the diﬀerenceswere found to be reliable (t(59) = 2.00, p = .01, Cohen’s d = .52).
The results from Experiment 3 suggest that these eﬀects really are due to diﬀerential ﬂuency: since hearing or reading the name of a category primes its most typicalmembers, the ﬂuency associated with this priming is a reliable cue for typicality.
Based on Oppenheimer’s (2004a) results we know that people will tend to overcompensate for the perceived inﬂuence of ﬂuency when faced with a salient alternate
explanation for that ﬂuency. In other words, participants in Experiment 3 judge
low-toner category members to be more typical because they are over-correcting
for the (now salient) lowered ﬂuency of the stimuli. The alternative, aﬀective accountof these eﬀects predicts that typicality judgments would be decreased by the negativeaﬀect caused by lowered ﬂuency stimuli.

Category of Vehicles:___ C ar___ Truck___ Airplane___ Bicycle___ Hang Glider___ Unicycle___ Cart___ Carriage___ Submarine___ Tractor___ Train___ Scooter___ Skateboard___ Motorboat___ BusFig. 2. Example stimuli from Experiment 3 in which lowered ﬂuency was due to a printer running out of
toner.

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Up to this point, our studies have exclusively used a between-subject design.
While this design is standard for investigations of ﬂuency (for reviews, see Schwarz,
1998, 2004; Skurnik, Schwarz, & Winkielman, 2000) and has logistical advantages, italso suﬀers from certain drawbacks. Speciﬁcally, results from between-subject studies are often diﬃcult to interpret because they are vulnerable to statistical artifacts
caused by aggregating data across subjects (Monin & Oppenheimer, 2005; Nickerson, 1995). To remedy this shortcoming, in Experiment 4 we replicated the ﬁndings
from Experiment 1 using a within-subject design.

6. Experiment 4
6.1. Method
6.1.1. Participants
Fifteen Princeton University and 24 MIT undergraduates were recruited from anupper division cognitive psychology course and an introductory psychology course,
respectively. Each group participated in exchange for candy. Participants were tested
in large groups, but were seated at diﬀerent tables and as such were unable to see thematerials and responses of the other participants.
6.1.2. Design, stimuli and procedure
The stimuli were identical to those in Experiment 1. The experiment was conducted in two phases, which took place exactly one week apart. In phase one, theparticipants were randomly assigned to either the ﬂuent or disﬂuent condition andﬁlled out a survey in the corresponding font. In phase two, participants completed
an identical survey, only in a diﬀerent font; those participants who had initially ﬁlled
out the survey in a ﬂuent font were given the same survey in a disﬂuent font, while
those who had initially been given a disﬂuent font were now given the ﬂuent font.
6.1.3. Results and discussion
Because several of the participants failed to complete the entire survey, complete
data sets were only obtained for the ‘‘birds’’ and ‘‘mammals’’ categories, and so only
those categories were included in the analysis. Participants’ ratings of category membership at time 1 were strongly correlated with their ratings at time 2 (mean r = .78)
indicating that participants were fairly consistent in their ratings across time. We
conducted two analyses, averaging ﬁrst across items and then across participants.
We found that there was an average increase in typicality ratings of 0.46 points
on our scale, which was highly reliable across items (t(27) = 11.49, p = 6.67 12,
Cohen’s d = 2.17), replicating the results of our previous experiments. In the participants analysis, we also found a signiﬁcant eﬀect of ﬂuency (mean = 0.46), which was
also reliable (t(38) = 3.01, p = 0.005, Cohen’s d = 0.48). These results show a replication of the previous experimental results in a more sensitive, within-subjects paradigm. Because of the greater power of this within-subjects paradigm, we found avery large eﬀect for individual items. In addition, despite the large variability acrossPlease cite this article in press as: Oppenheimer, D. M., & Frank, M. C., A rose in any otherfont would not smell as sweet: ..., Cognition (2007), doi:10.1016/j.cognition.2007.05.010

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participants, the eﬀects of ﬂuency were still visible within individual participants,
providing evidence that the diﬀerences in typicality judgments we observed in Experiments 1–3 were not artifacts of averaging across participants.

7. General discussion
Four experiments have provided evidence that the perceptual ﬂuency of a stimulus aﬀects categorization judgments. Perceptual ﬂuency appears to be used as one ofseveral heuristic cues for categorization. The reason for the reliability of this cue is asfollows: every categorization decision involves both an exemplar and a category. The
more typical the exemplar is to the category, the stronger the representational link
between them, and the more the activation of one will prime the other. Since themain consequence of priming is an increase in ﬂuency (ease of processing and easeof production), there is a reliable relationship between ﬂuency of a category (giventhat the category member is activated) and that member’s typicality. Put diﬀerently,
if you want to judge what the best exemplars of a category are, start naming them oﬀ
the top of your head. The exemplars you name will be those most strongly linked tothe category representation. Why? They are the most ﬂuent exemplars, and this ﬂuency comes as a result of their being primed by the activation of the original category
representation. As such, while they are not foolproof indicators of category membership, heuristic cues such as ﬂuency provide useful and easily available sources of
information for decision-making.
Our proposal regarding the role of ﬂuency in typicality is consistent with the diﬀerential judgments reported. In Experiment 1, we demonstrated the basic phenomenon ofinterest, namely that when we lowered the perceptual ﬂuency of category exemplars by
reducing the font size they were written in, they were judged to be less typical. Since people often have diﬃculty determining the source of ﬂuency (Jacoby & Dallas, 1981; Schwarz, 2004), their ﬂuency experience is misinterpreted as being due to the conceptual
link between an exemplar and a category; as such, the diﬀerential ﬂuency is used as ametacognitive cue and inﬂuences category judgments. In Experiment 2, we demonstrated the robustness of this account by having participants make judgments offeatures instead of exemplars in relation to categories. Again, participants judged
the stimuli whose ﬂuency had been lowered as less typical, despite the fact that they
were identical in content to the control stimuli. In order to rule out aﬀective explanations, we tested the hypothesis that, like other heuristic cues used in judgments, ﬂuency
is subject to causal discounting. When presented with a salient alternative explanation
for the lowered ﬂuency of one stimulus set (namely, that the laser printer on which they
had been printed was running out of toner), participants overcompensated for the
now-salient lack of ﬂuency by rating category exemplars as more typical rather thanless typical, as they had before. Once participants became aware that the ﬂuency experience was not due to a conceptual link between an exemplar and a category, theyattempted to disregard ﬂuency as a cue, but overcompensated and erred in the otherdirection (as in Oppenheimer, 2004a, 2005; Schwarz, 2004). In Experiment 4, we demonstrated the eﬀect of ﬂuency using a within-subject design.
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7.1. Implications for theories of categorization
The results presented here, though small, are not typically predicted or accounted
for in current models of categorization. Exemplar and prototype models, which relyon shared semantic features of category exemplars to make decisions about category
membership, generally do not take into account experiential information in making
categorization decisions. Although a feature-based exemplar or prototype model
could account for ﬂuency eﬀects by including ﬂuency as a feature, this sort of adhoc addition fails to acknowledge that the eﬀect of ﬂuency may be heuristic andmeta-cognitive – as our evidence suggests, since it is subject to discounting – rather
than being a product of a feature based general categorization process.
Further, as categories and their exemplars are conceptually related for all categories, high ﬂuency would have to be a universal feature across all categories; a form ofrepresentation that seems fairly unparsimonious. One possible alternative would be atheory in which heuristic information modiﬁes traditional feature-based judgments.
In other words, the model would judge the typicality of ‘‘robin’’ before adjusting that
judgment according to heuristic input. However, this solution may beg the question:
why add heuristic information after a categorization judgment has already been
arrived at? Why not leave it out altogether and avoid the errors that heuristic cues
may sometimes introduce?
Likewise, causal and knowledge-based models of categorization are bound to a presentation-independent view of categorization unless they explicitly acknowledge presentation as part of a category’s causal model. Barring this modiﬁcation, theparticular stimulus presentation which engages conceptual knowledge should notaﬀect the overall outcome in categorization decisions, thus rendering them unable toaccount for heuristic categorization phenomena. One possible solution to this would
be a causal based framework in which features unrelated to category membership could
be represented (e.g., Oppenheimer, Tenenbaum, & Krynski, in preparation). In such amodel, metacognitive factors could be used as causal explanation of features, or even as
features themselves. Such models are still in the preliminary stages of development, butshow great promise for accounting for heuristic judgment in categorization.
Another class of promising models includes Gluck and Bower’s (1988) account of
categorization based on the informational value of diﬀerent cues. This model, an
extension of Rescorla and Wagner’s (1972) model of animal learning, focuses on their
participants’ use of the validity of a particular cue relative to other competitor cues
rather than the objective probability that a particular cue is associated with a stimulus. In general, models which are agnostic as to what constitutes a cue will be able tointegrate ﬂuency information with semantic features to account for the results
reported here. While a standard exemplar model might require the inclusion of ﬂuency as a feature for every stimulus to be categorized, a more ﬂexible model should
identify ﬂuency as just one of many competing cues that may oﬀer information about
typicality or feature likelihood. Even though ﬂuency may be robust as a cue fortypicality, the superior validity of semantic cues would predict that ﬂuency would
play a relatively small role in any given decision. Indeed, the eﬀects of ﬂuency in
our experiments, while reliable, are quite small in magnitude. Further, our studies
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asked participants to make their judgments based on a word or a description, not a
feature-laden representation – perhaps this lack of immediate link to the features ofa particular stimulus increased participants’ reliance on other perceptual cues, suchas ﬂuency.

8. Conclusion
The data presented here are indicative of a general cognitive trend. The patternobserved in our data – that of using ﬂuency for making judgments – is hardly uniqueto categorization. The work of Tversky and Kahneman and the extensive literaturein the heuristics and biases tradition indicates that a wide variety of judgment tasks
are carried out through a process of learning multiple, easily-identiﬁed metacognitive
patterns that substitute for more computationally intensive evaluation tasks. Indeed,taken in this context, categorization judgments can be seen simply as one type of
judgment, in which human beings use a variety of fast and easy shortcuts wheneverpossible, only resorting to slow and eﬀortful processing when the task requires it.
While category learning and category judgments in novel situations may rely primarily on featural and conceptual information, as we become more familiar with exemplars, features, and categories, we gain access to a new set of cues – heuristic cues –that can simplify the categorization process.

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