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British Journal of Social Psychology (2011), 50, 501–518
C2010 The British Psychological Society
Prime and prejudice: Co-occurrence in the culture
as a source of automatic stereotype priming
Paul Verhaeghen1∗, Shelley N. Aikman2and Ana E. Van Gulick3
1Georgia Institute of Technology, Atlanta, USA
2Gainesville State College, Georgia, USA
3Vanderbilt University, Nashville, Tennessee, USA
It has been argued that stereotype priming (response times are faster for stereotypical
word pairs, such as black-poor, than for non-stereotypical word pairs, such as black-
balmy) is partially a function of biases in the belief system inherent in the culture.
In three priming experiments, we provide direct evidence for this position, showing
that stereotype priming effects associated with race, gender, and age can be very well
explained through objectively measured associative co-occurrence of prime and target
in the culture: (a) once objective associative strength between word pairs is taken into
account, stereotype priming effects disappear; (b) the relationship between response
time and associative strength is identical for social primes and non-social primes. The
correlation between associative-value-controlled stereotype priming and self-report
measures of racism, sexism, and ageism is near zero. The racist/sexist/ageist in all of us
appears to be (at least partially) a reﬂection of the surrounding culture.
Everyone, or so the song goes (Lopez & Marx, 2003), is a little bit racist. Or sexist.
Or ageist. Scientiﬁc evidence for this claim comes from, among other things, studies
that investigate stereotype priming. In such experiments, participants are ﬁrst presented
with a prime, typically a word denoting a social category, such as black, female, or old,
and then perform some latency task on a target, a second word presented immediately
after the prime. Reaction times (RT) are typically found to be faster for stereotypical
prime-target pairings (such as black-poor,female-caring,andold-forgetful) than for
non-stereotypical prime-target pairings (such as black-forgetful,female-poor,andold-
caring) – the stereotype priming effect. RT differences in priming effects are thought
to result from activation spreading through an individual’s semantic network; faster
RTs denote that the prime and the target are closely related in the individual’s mind.
The assumption in stereotype priming is that the RT difference between stereotypical
prime-target pairs and non-stereotypical prime-target pairs is a measure of an individual’s
∗Correspondence should be addressed to Dr Paul Verhaeghen, School of Psychology, Georgia Institute of Technology, 654
Cherry Street, Atlanta, GA 30332, USA (e-mail: email@example.com).
502 Paul Verhaeghen et al.
‘unconscious’ or ‘automatic’ or ‘implicit’ level of prejudice towards the group denoted
by the prime.
Implicit prejudice is quite dissociable from explicit prejudice as measured, for
instance, by questionnaires. A recent meta-analysis estimates the correlation between the
implicit association task (IAT), one implicit measure of prejudice, and explicit measures
to be .21 (Greenwald, Poehlman, Uhlmann, & Banaji, 2009). The modest size of the
correlation is typically explained by assuming that implicit measures tap an individual’s
‘true’ level of racism, whereas self-report data may not, due to self-presentation (e.g.,
Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Fazio, Jackson, Dunton, &
Williams, 1995), or by assuming that each stems from a different phase of social
processing: implicit measures tap a reﬂexive gut reaction, whereas explicit processes
tap a later, more reﬂective process of social evaluation (Gawronski & Bodenhausen,
2006). That does not imply that implicit stereotyping is innocuous; its effects might
be all the more insidious for (generally) not being accessible by conscious thought
(e.g., Asendorpf, Banse, & M¨
ucke, 2002; Devine, 1989; Dovidio et al., 1997; Egloff &
Schmukle, 2002; Fazio, 1990; Fazio et al., 1995; Wilson, Lindsey, & Schooler, 2000).
The Greenwald et al. meta-analysis obtained a correlation between implicit measures
of prejudice and prejudiced behaviour (such as voting preference, non-verbal aspects
of interactions, including touching, eye-contact, perceived friendliness, hostility, and
physical distance chosen) of .27; explicit measures yielded a correlation with prejudiced
behaviour of .33. Both types of measures possessed incremental validity, further
lending credence to the argument that they tap different systems or stages of social
What is the origin of the implicit prejudice observable in the stereotype priming
effect? Recent work in the ﬁeld (reviewed by Rudman, 2004) suggests at least four
routes: early experiences, affective experiences, cognitive consistency principles, and
cultural biases. The former three are idiosyncratic – every individual’s developmental
and learning history is unique – whereas the latter, cultural milieu, is much less so.
Direct evidence for the three idiosyncratic sources is relatively easy to obtain – one
can correlate early experiences with implicit attitudes in adulthood (e.g., Rudman &
Goodwin, 2004; Rudman & Heppen, 2003), one can manipulate affective experiences
experimentally and measure its effect on implicit attitudes (e.g., Blair, 2002), one
can measure (or manipulate) cognitive consistency and note its effect on implicit
attitudes (e.g., Greenwald et al., 2002). Measuring the effects of culture on implicit
attitudes is less self-evident. Cultural biases have been inferred from the frequent
(but not ubiquitous; see Rudman, 2004) ﬁnding that minority/disadvantaged groups
show priming for stereotypes concerning their own group (e.g., Greenwald & Krieger,
2006; Nosek, Banaji, & Greenwald, 2002) – a ﬁnding that is hard to explain through
any idiosyncratic mechanism. Devine, in her seminal 1989 paper, argues that cultural
inﬂuence can also be inferred from an individual’s mere knowledge of the content of
Our goal in the present set of experiments was to observe the impact of cultural biases
on implicit stereotypes more directly. We were inspired by recent developments in the
(cognitive) priming literature. The traditional view on priming, which we will label the
semantic view (see Lucas, 2000), holds that two items in memory get connected through
meaning, typically deﬁned as an overlapping set of features. Thus, the prime-target pair
dog–cat yields fast RTs because dogs and cats share many features (smallish carnivorous
mammals kept as pets). The other view on priming has been labelled the associative co-
occurrence view (e.g., Balota & Paul, 1996). This framework states that two items will
Prime and prejudice 503
become associated with another when they both co-occur in the natural environment.
Dog primes cat because cat and dog are often encountered in identical contexts, both
in the real world and in the world of media. Evidence for the co-occurrence position
comes from mediated priming (e.g., lion primes stripes, although there is no feature
overlap between the two entities; Balota & Lorch, 1986; Chwilla & Kolk, 2002) and from
priming effects obtained with ambiguous words, such as organ, as targets (Balota & Paul,
The possibility that priming might reﬂect associative co-occurrence rather than
semantic proximity opens a potential methodological route to study culture’s impact on
stereotype priming. If we take the co-occurrence view of (cognitive) priming seriously,
we can surmise that stereotype priming as observed in an individual might at least
partially reﬂect the semantic network that exists in the culture (see Arkes & Tetlock,
2004; Devine, 1989, for a related discussion). It is feasible, for instance, that stereotypical
pairings are associated with a higher degree of co-occurrence in the cultural discourse.
Perhaps, over a lifetime, we do see, hear, or read more instances where the word black is
coupled with the word poor,orthewordfemale with the descriptive weak,ortheword
old with the term fragile than instances where each of these words is coupled with, say,
the word courage. Stereotype priming effects should then at least partially reﬂect not
what is often termed an individual’s ‘personal’ (e.g., Gawronski, Peters, & LeBel, 2008)
biases, but rather how well the individual has internalized the co-occurrences presented
to her by her surrounding culture.
The associative co-occurrence model for stereotype priming is testable. Its basic
tenet – the weakest statement the model can make – is that stereotype priming effects
should covary with the associative strength between prime and target. A stronger
version of the model might state that stereotype priming is driven by associative co-
occurrence to the same extent as standard forms of semantic priming are, at least at the
group level. In other words, associative co-occurrence should have identical effects for
stereotypical pairings and non-stereotypical pairings – co-occurrence in the culture builds
co-occurrence in the mind, without distinguishing between the types of representations
that are being correlated. In that case, we would expect the following hypotheses to
hold true: (a) once objectively measured population-based associative strength between
prime and target is equated, stereotype priming effects should be identical to standard
lexical priming effects and (b) the assumed relationship between associative strength and
the priming effect (as tested by latency by associative-value curves) should be identical
for primes that are traditionally associated with stereotype priming and for primes that
fall outside this realm. The stronger version is quite parsimonious (it treats stereotype
priming like any other form of priming); therefore, this is the version we will test. We
note that the outcome is not self-evident: it has been argued that stereotypes serve
as a short-cut for social cognitive operations (e.g., Sherman, Conrey, & Groom, 2004;
Wigboldus, Sherman, Franzese, & van Knippenberg, 2004) to allow humans to navigate
easily through the many complexities of the social world by side-stepping intricate
decision processes, leading individuals to make faster judgments about the social than
the non-social world.
In our experiments, then, we will investigate priming effects using both social primes
able to elicit stereotypes (viz., black,white,female,male,old,young) and non-social
primes (viz., night,day,summer,winter,cat,dog). The social primes will be coupled
with both stereotypical targets and non-stereotypical targets of both positive and negative
valence; the non-social primes will be coupled with targets of different associative
strengths. Associative strength will be assessed objectively using a semantic similarity
504 Paul Verhaeghen et al.
data base compiled from a large variety of text sources assumed to reﬂect the lifetime
body of literature read by a typical college student, our population of choice. Participants
will be asked to complete one of three types of tasks, representative of the stereotype
(and standard) priming literature – lexical decision (is the target a word?), evaluative
judgments (does the target denote something positive or something negative?), and
relatedness judgments (are prime and target related?). The three tasks can be considered
as lying on a continuum of task difﬁculty (lexical decision implies access to the lexicon;
evaluative judgment implies access to the lexicon and retrieval of valence information;
relatedness judgment requires access to the lexicon for both stimuli, as well as judgment
of the associative tie between them – our response times for these tasks, monotonic with
assumed difﬁculty, bear out this expectation). One might also argue that this continuum
represents decreasing degrees of implicitness of the task: subjects who are motivated
to represent themselves in a non-prejudiced way might be more tempted to do so in
the evaluative task than in the lexical decision task, and even more so in the associative
judgment task. If we do ﬁnd the same pattern of results across these tasks, this would
represent strong conceptual replication.
In our three experiments, we hope to reproduce the stereotype priming effect, that
is, we hope to ﬁnd that within social targets RTs will be faster for stereotypical pairs.
At the same time, we hope to obtain evidence for the importance of associative co-
occurrence, as explicated in the previous paragraph. To test whether the stereotype
priming effects obtained here are indeed implicit, we will examine the correlations
between these effects and scores on standard measures of explicit stereotypes, the Age
Group Evaluation and Description (AGED) Inventory, The Modern Racism Scale, and the
Modern Sexism Scale.
Our design effectively consists of three between-subject experiments, differing only in
the tasks used: (a) lexical decision on the target; (b) evaluative judgments on the target;
and (c) relatedness judgments on the prime-target pairing. Prime-target pairs and general
aspects of the procedure are identical across experiments.
Participants were undergraduate students recruited from an introductory psychology
course. They received course credit in return for their participation. For the lexical
decision experiment, we tested 34 participants: 50% female, 79.4% self-reported
White/Caucasian ethnicity, ranging in age from 18 to 42 years (M=20.7). For the
evaluative judgment experiment, 35 participants signed up: 45.7% female, 82.9% self-
reported White/Caucasian ethnicity, ranging in age from 18 to 21 years (M=18.7).
For the relatedness judgment experiment, 35 participants signed up: 51.4% female,
74.3% self-reported White/Caucasian ethnicity, ranging in age from 18 to 49 years
We imposed no restrictions on the participants’ gender, age, or race/ethnicity, for
two reasons: ﬁrst, we did not want to sensitize participants to the theme of the study, and,
second, we wanted our sample to be as representative of the typical student population
as possible, given that the Touchstone Applied Science Associates (TASA) text corpus
(see the Stimuli section) is aimed at this population.
Prime and prejudice 505
We created 240 prime-target pairs out of 12 different primes and 240 different targets
(these are listed in Table 1). Six of our primes were social primes that could elicit
stereotypes (i.e., black, white, female, male, old, young) and six were non-social
(i.e., day, night, summer, winter, cat, dog). For each of the social primes, we used
10 stereotype targets, deﬁned as targets that have previously been used in stereotype
priming experiments. Half of these had a positive valence (e.g., black-charming), half
had a negative valence (e.g., black-poor). For each of the stereotype primes, we also used
10 non-stereotype targets (these were, in fact, associates generated for the non-social
primes), half of these positive (e.g., black-balmy), half negative (e.g., black-mundane).
For the non-stereotype primes, six members of our laboratory generated associates;
associates generated by two or more members were assumed to be of high associative
strength with the prime. For each of the non-stereotype primes, we used 10 high-
associate targets, 5 positive (e.g., cat-independent), 5 negative (e.g., cat-sly), and 10
low-associate targets, that is, words that were associated with the stereotype primes, 5
positive (e.g., cat-memorable) and 5 negative (e.g., cat-senile).1
Associative strength for each prime-target pair was generated by the Bound Encoding
of the AGgregate Language Environment model (BEAGLE; Jones & Mewhort, 2007), a
computational model to represent semantic space as a set of high-dimensional vectors.
BEAGLE is distinct from other such systems, like latent semantic analysis (LSA; e.g.,
Landauer, Foltz, & Laham, 1998) in that it takes word order into account when computing
similarity statistics. BEAGLE ‘reads’ a large-scale corpus of text data, one sentence at a
time, and records both context information and order information for each word in the
sentence. BEAGLE stores both types of information in a vector pattern; the associative
strength between two words is measured as the cosine of the vectors representing the
two words. The database is dense: dimensionality of the vector space in BEAGLE is
set at 1,024, and it contains 90,000 lexical entries. The corpus of text data used is the
TASA corpus (Landauer et al., 1998), designed to be approximately equivalent to what
the average college-level student has read in her lifetime. We note that, as expected,
within the social primes, associative strengths for stereotype prime-target pairs were
signiﬁcantly higher than those on non-stereotype pairs: .295 and .045, respectively,
t(118 df )=17.51, p<.001, a confound that is likely to contaminate many studies of
stereotype priming. Word pairs and their associative strength values are provided in
The 240 non-lexical stimuli used in the lexical decision task were pronounceable non-
words, downloaded from the English Lexicon Project Web Site (Balota et al., 2002; http:
//elexicon.wustl.edu/default.asp), between 6 and 12 letters long, with 3–10 orthographic
neighbours, a bigram frequency of 2,000 to 3,500 and a response accuracy equal to or
higher than .89. Examples include basner, tarren, carrow, pingers, chirling,anddrater.
The AGED Inventory (Knox, Gekoski, & Kelly, 1995) measures evaluative (i.e., goodness
and positiveness) and descriptive (i.e., vitality and maturity) dimensions of age group
1Our concern was to closely replicate experiments on stereotype priming as reported in the literature. Ideally, additional
constraints on word frequency, word length, and the number of orthographic neighbours should be imposed; this would,
however, have restricted the possible set of stimuli considerably. Because our generator of associative values, the BEAGLE
model, is not available as a look-up table (yet), we needed to resort to a priori selection of prime-target pairs, which were
then fed through the model by its originator, Michael Jones, to access their actual associative values.
506 Paul Verhaeghen et al.
Ta b l e 1 . All prime-target pairs, with the BEAGLE-derived associative strength values
Prime and prejudice 507
Ta b l e 1 . (Continued)
508 Paul Verhaeghen et al.
Ta b l e 1 . (Continued)
perceptions. In the present study, participants were asked to rate ‘the “average” or
“typical” person in their seventies’ along 28 bipolar adjective pairs (e.g., independent–
dependent, timid–assertive) using seven-point scales. We used the evaluative dimension
as our measure of ageism.
The Modern Racism Scale (McConahay, 1986) purports to measure a more subtle
form of racism (distinct from more overt racism) that has been described by Sears (1988)
to encompass denial of continued discrimination against, antagonism towards demands
of, and resentment about special favours for African-Americans.2
The Modern Sexism Scale (Swim, Aikin, Hall, & Hunter, 1995) purports to measure a
more subtle form of sexism (i.e., negative reactions towards women), distinct from overt
sexism. This scale encompasses three dimensions: denial of continued discrimination
2One item was omitted for being obsolete: ‘Blacks have more inﬂuence upon school desegregation plans than they ought to
Prime and prejudice 509
against, antagonism towards demands of, and resentment about special favours for
The lexical decision experiment consisted of 480 trials: 240 trials in which a prime-
target pairing was presented mixed randomly with 240 trials in which a prime-non-word
pairing was presented (primes were the same as used for the prime-target pairs). In
the evaluative judgment and relatedness judgment tasks, 240 prime-target trials were
presented. Presentation order of trials was randomized for each individual participant.
Participants were instructed to be as fast and accurate as possible, and to keep their
index ﬁngers on the response buttons at all times.
Each trial consisted of the following sequence of events. First, a ﬁxation cross
appeared at the centre of the screen for 500 ms. The prime was then presented (Courier
New font, size 24, bold) for 100 ms. In the lexical decision condition, primes were
presented in uppercase font; in the other two conditions primes were presented in
lowercase font; the discrepancy was due to a clerical error. Next, a blank screen was
presented for 200 ms, immediately followed by the target, presented in a lowercase font
(Courier New font, size 24, bold). The target remained onscreen until the participant
logged her response by pressing either the ‘z’ or ‘/’ key. A card was placed behind the
keys, identifying what each key signiﬁed: for the lexical decision task, the keys signiﬁed
‘nonword’ and ‘word’, respectively; for evaluative judgment, ‘negative’ and ‘positive’,
respectively; and for relatedness judgments ‘unrelated’ and ‘related’, respectively. After
a 700 ms delay, the next trial was presented.
Upon conclusion of the RT task, participants ﬁlled out the prejudice-related question-
naires. Finally, they were handed a debrieﬁng statement that explained the goals of the
study and given the opportunity to ask questions.
Prior to all analyses, we removed RTs lying outside three interquartile ranges from the
mean from the data set. This procedure removed 8.2% of the lexical decision data, 6.6%
of the evaluative judgment data, and 5.0% of the relatedness judgment data. Given that
the latter two tasks do not yield ‘correct’ answers – they reﬂect personal judgments – all
non-outlying RTs, regardless of the participant’s answer, were included in all analyses.
We report our ﬁndings in three sections. First, we report on the inﬂuence of
associative strength on stereotypical prime-target response times compared to response
times for non-stereotypical pairings – the main focus of interest. Second, we investigate
the relationship between response times and associative strength within stereotype
primes and non-stereotype primes. Third, we report on the relationship between
stereotype priming effects and our more explicit measures of prejudice.
Stereotype priming versus non-stereotype priming
To mimic the results of a typical stereotype priming experiment, we ﬁrst contrasted,
within the social prime-target pairs, average RT for the 60 stereotypical pairs with average
510 Paul Verhaeghen et al.
Stereotype vs. non-stereotype social
prime pairs, associative value not
Stereotype social pairs vs. all others,
overlapping associative values
Racist/sexist/ageist pairs vs. all
others, overlapping associative values
Stereotype vs. non-stereotype social
prime pairs, associative value controlled
Response time (ms)
Response time ln (ms)
Response time (ms)
Response time (ms)
Figure 1. Average response time for stereotype (white bars) and non-stereotype (hatched bars)
prime-target pairs for the three experiments (lexical decision, evaluative judgment, and relatedness
judgments, N=35 for each), using three different deﬁnitions. Panel A: 60 traditional stereotype prime-
target pairs contrasted with 60 non-stereotype prime-target pairs. Panel B: The same 60 traditional
stereotype prime-target pairs contrasted with the same 60 non-stereotype prime-target pairs, marginal
means of ln(response times) as derived from an analysis of covariance with ln(associative strength) as
the covariate. Panel C: 54 stereotype prime-target pairs contrasted with 59 non-stereotype prime-
target pairs with overlapping associative strength values as measured by BEAGLE, see text. Panel D:
11 negatively valenced stereotypical prime-target pairs contrasted with 102 other prime-target pairs
with overlapping associative strength values as measured by BEAGLE, see text. Only in Panel A is
the difference in latency between stereotypical and non-stereotypical prime-target pairs signiﬁcant,
indicating that control for semantic co-occurrence makes stereotype priming (statistically) disappear.
Error bars represent standard error of the mean.
RT for the 60 non-stereotypical pairs. The results are presented in Figure 1, Panel A. All
three ttests, one for each task, were highly signiﬁcant, t(118 df )=5.37, 3.66, and 2.70;
p<.001, .001, and .005; the average difference between stereotypical pairs and non-
stereotypical pairs, that is, the stereotype priming effect, over the three experiments,
was 102 ms (108 ms for lexical decision, 142 ms for evaluative judgments, and 56 ms for
In a second set of analyses, we implemented three separate univariate ANCOVA
(analysis of covariance) models, one for each task. In a ﬁrst model, we analysed response
time for stereotypical pairs versus non-stereotypical pairs, using associative strength as
Prime and prejudice 511
the covariate. All three Ftests for the main effect of type of pair were now reduced to
non-signiﬁcance, F(1, 117) =1.56, 3.38, and 1.63. This analysis, however, is suboptimal:
ANCOVA presupposes a linear relationship between dependent variable and covariate.
In the present case, this assumption is incorrect: as described in the next section,
the relationship between associative strength and response time is better described by
a power function. The net result is that the traditional linear analysis overestimates
the effect of pair type. Therefore, in a second model, we analysed the main effect
of pair type on the natural logarithm of response time with the natural logarithm of
associative strength as a covariate; this transformation provides a better approximation of
the data (see also Figure 2). Prior to the logarithmic transformation, associative strength
was multiplied by 1,000, and then 100 was added; this linear rescaling was done to
avoid negative numbers for the logarithmic transformation. The data for this model are
represented in Figure 1, Panel B. As expected, Fvalues for the main effect of type of pair
were even more reduced for this model, F(1, 117) =0.27, 1.10, and 1.30, respectively –
once associative strength was partialed out, there was no longer a signiﬁcant difference
in response times for stereotypical pairs and non-stereotypical pairs.
Our statistical control analysis thus yielded non-signiﬁcant results. It is, however,
also possible to control for associative value more directly by comparing stereotypical
pairs with non-stereotypical pairs with overlapping values for associative strength. In
a third analysis, therefore, designed to control for the fact that stereotypical prime-
target pairs tend to have higher associative strengths than other prime-target pairs,
we directly contrasted RTs for stereotype prime-target pairs (e.g., female-weak,male-
strong) with non-stereotype prime-target pairs, both social and non-social (e.g., female-
lucky, dog-smelly). We restricted the analysis to prime-target pairs with associative
strengths that yielded large overlap between the two categories. We chose associative
values between .15 and .50, contrasting 54 stereotypical prime-target pairs with 59
non-stereotype pairs (mean associative value for this sample of pairs was 0.30, SD =
0.08, for the stereotypical pairs and 0.29, SD =0.09, for the non-stereotypical pairs).
Results are depicted in Figure 1, Panel C; ttests revealed no signiﬁcant differences in
RT between the two categories for any of the three tasks, t(111 df )=1.30, 1.33, and
−0.39; the average difference between stereotypical pairs and non-stereotypical pairs,
over the three experiments, was 22 ms (32 ms for lexical decision, 42 ms for evaluative
judgments, and −8 ms for semantic relatedness judgments). (We note that this analysis
could not be performed on social primes only – the associative strengths for the non-
stereotype prime-target pairs within social primes were generally low, leaving only 2
social prime-target pairs in the non-stereotypical category.)
It could be argued that the previous analysis is not stringent enough, and that what
needs to be examined are negative stereotypes for the disadvantaged groups only –
truly racist, sexist, and ageist stereotypes. Therefore, in an ﬁnal analysis, we directly
contrasted RTs for negatively valenced stereotypical prime-target pairs for the three
primes black,female,andold with all other RTs (i.e., we contrasted RTs for stimulus
pairs of the type female-weak with RTs for stimulus pairs of the type female-gentle [non-
negative stereotype], female-lucky [non-stereotype], male-strong [prime not typically
associated with negative stereotype], and dog-smelly or dog-striped [non-stereotype
prime]). As in the previous set of analyses, the analyses were restricted to prime-target
pairs with associative strengths between .15 and .50, contrasting 11 negatively valenced
stereotypical prime-target pairs with 102 other pairs (mean associate values were 0.29
and 0.30, respectively, with SD =0.09 and 0.08, respectively). Results are depicted
in Figure 1, Panel D; ttests revealed no signiﬁcant differences in RT between the
512 Paul Verhaeghen et al.
Lexical decision task
0 100 200 300 400 500 600 700 800
0 100 200 300 400 500 600 700 800
0 100 200 300 400 500 600 700 800
Evaluative judgment task
Relatedness judgment task
Response time (ms)Response time (ms)Response time (ms)
Power (non-stereotype primes)
Power (stereotype primes)
Figure 2. The relationship between objectively measured semantic co-occurrence (associative strength
in a large text corpus, as computed by BEAGLE, see text) and response time for social and non-social
primes, for lexical decision (panel A), evaluative judgment (panel B), and relatedness judgment (panel
C), along with freely estimated power functions for each type of prime. Statistical analysis (see text)
shows that the two power functions in each display coincide, indicating that the co-occurrence–latency
relationship is identical for social and non-social primes.
Prime and prejudice 513
two categories for any of the three tasks, t(111 df )=0.67, 0.20, and −1.08; the
average difference between negative-stereotype pairs and other pairs, over the three
experiments, was −1 ms (16 ms for lexical decision, 8 ms for evaluative judgments, and
−28 ms for relatedness judgments).
The relationship between RT and associative strength in stereotype primes and
Figure 2 plots mean RT, averaged across all individuals, for each of the 240 prime-target
pairs as a function of prime-target associative strength as derived from BEAGLE, for each
of the three tasks separately (for these plots and the associated regression analyses,
associative strength was ﬁrst multiplied by 1,000, and then 100 was added; this linear
rescaling was done to facilitate regression analyses and avoid negative numbers for the
logarithmic transformation discussed below). The plots suggest a non-linear, negatively
accelerating relationship; we decided to ﬁt a power function to the data, mainly because
of its mathematical tractability. Overlaid on the plots are the freely estimated best-ﬁtting
power functions for each of the prime types, namely social (i.e., black, white, female,
male, old and young) and non-social (i.e., day, night, summer, winter, cat, dog).
To test for signiﬁcant differences between the curves for stereotype (i.e., social)
primes and non-stereotype (i.e., non-social) primes for each of the panels, we ﬁrst
linearized the data by performing a logarithmic transformation on both associative
strength and RT, and then conducted three hierarchical linear regressions, one for each
task. In a ﬁrst step, we entered the logarithm of the associative strength to predict
the logarithm of RT; in a second step, we entered a dummy variable, coding for type
of prime (to test for intercept differences between the two curves) as well as the
interaction between this dummy variable and the logarithm of the associative strength
(to test for rate differences between the two curves). In neither of the three tasks did
the second step lead to a signiﬁcant increase in R2; for lexical decision, R2=.000,
F(2, 236) =0.03; for evaluative judgment, R2=.009, F(2, 236) =1.16; for relatedness
judgment, R2=.008, F(2, 236) =1.04. This indicates that the two curves for each
plot coincide statistically. Calculating back to power functions in raw units, the three
lines are: for lexical decision, RT =1,222 (associative strength)−.083,non-linearR=
.40; for evaluative judgment, RT =1,417 (associative strength)−.072,non-linearR=.30;
and for relatedness judgment, RT =1,1,239 (associative strength)−.038,non-linearR=
.24. To check whether the shape of the RT by associative strength function inﬂuenced
the results, we repeated these analyses using linear hierarchical regression on the raw,
untransformed data. The conclusion remained the same: there is no signiﬁcant difference
between the lines for stereotype prime-target pairs and non-stereotype prime target pairs.
Put simply: the relationship between response time and associative strength is exactly
the same for stereotype prime-target pairs and non-stereotype prime-target pairs across
all three tasks. This strongly suggests there is nothing ‘special’ about stereotype pairs
over and beyond their higher associative value in the general culture.
Correlations between stereotype priming and self-reported racism, sexism,
In a ﬁnal analysis, we investigated the correlations between stereotype priming for
racist, sexist, and ageist pairs, and self-reported racism, sexism, and ageism, respectively.
Within each individual, we construed a racism priming scale by subtracting average RT
514 Paul Verhaeghen et al.
on the ﬁve negatively valenced stereotype targets associated with the prime black (mean
associative strength of 0.42) from average RT on the ﬁve negatively valenced targets on
non-stereotype primes selected to yield the same average associative strength (viz., cat-
wicked, dog-dirty, winter-dim, night-silent, night-gloomy; mean associative strength of
0.42). This score reﬂects how much faster (or slower) the subject responds to racist
prime-target pairs compared to non-stereotypical prime-target pairs. We constructed a
sexism (mean associative strength of 0.25) and ageism (mean associative strength of
0.22) scale in analogous fashion (baseline pairs had a mean associative strength of 0.25
and 0.22, respectively).
No signiﬁcant correlations were found between the priming measure of racism and
the Modern Racism Scale, between the priming measure of sexism and the Modern
Sexism Scale, and between the priming measure of ageism and the AGED inventory for
any of the three tasks (r=.04, .20, and .06 for racism; .19, .17, and −.02 for sexism, and
−.12, .03, and −.29 for ageism; the average correlation is .03).
Our experiments were designed to test the conjecture (e.g., Arkes & Tetlock, 2004;
Banaji, 2001; Devine, 1989; Rudman, 2004) that implicit stereotypes are at least partially
acquired through the umbilical cord we all share with the surrounding culture. Our
measure of implicit stereotype was stereotype priming, implemented in three types of
tasks – lexical decision, evaluative judgments, and relatedness judgments. Our measure
of cultural inﬂuence was associative co-occurrence between the prime-target pairs as
measured objectively in a large corpus of text data assumed to be representative of
the accumulated reading experience of the average undergraduate. The basic tenet of
a model using associative co-occurrence as the proxy for cultural inﬂuence would be
that stereotype priming effects are reduced when associative co-occurrence is taken
into account; the strongest version of such model would predict that associative co-
occurrence would eliminate all stereotype priming effects at the group level – that
is, it would expect that co-occurrence in the culture is as successful an explaining
mechanism for the implicit stereotype priming effect as it is for other, non-stereotype,
Our main results are unambiguous, and support the strong version of the co-
occurrence view: there is no special status for priming related either to social stimuli
or to stereotypical prime-target pairs. This suggests that at least part of the alleged
racist/sexist/ageist hiding inside us all is a monster not of our own making; it is built out
of memes borrowed from close contact with our environment – the books, newspapers,
and magazines we read, the television shows we watch, the radio programmes we tune
Two lines of evidence support this conclusion; each of these pieces of evidence
was obtained in three independent groups of participants, each performing a different
task on the same set of prime-target pairs. First, in all three groups and tasks, we
replicated the standard stereotypical priming effect: participants responded faster
to stereotypical pairs, such as black-poor than to non-stereotypical pairs, such as
black-goofy. Judged by this criterion, our participants are, indeed, racist, sexist,
and ageist. When, however, associative co-occurrence in the culture was taken into
account, a different picture emerged: our participants responded equally fast to either
general or negative stereotype prime-target pairs as to non-stereotype prime-target
Prime and prejudice 515
pairs. It is therefore not the stereotypical nature of the prime-target relation that
drives the stereotype priming effect, it is the associative strength between prime and
Second, the relationship between semantic co-occurrence and RT was statistically
identical for social prime-target pairs and non-social prime-target pairs. Thus, there is no
special status for primes associated with stereotypical responses – the mechanism that
links associative co-occurrence in the culture to priming effects as observed in groups
of individuals is a general one that does not make a distinction between the social or
non-social nature of the primes.
We note that in all three experiments, the correlations between individual stereotype
priming effects (with associative co-occurrence taken into account) and standard
measures of explicit prejudice were non-signiﬁcant and rather small, ranging from −.29
to .20, with a mean of .03. This might suggest – although more research is necessary
before this conclusion can be stated with conﬁdence – that our co-occurrence-controlled
stereotype priming measure has stronger divergent validity from explicit measures than
is typically observed in the literature (Greenwald et al., 2009).
One side effect of our study is that it provides further support for the associative
co-occurrence view on semantic priming (Balota & Paul, 1996), and extends this view to
social and stereotypical priming. We want to reiterate that our proposition that implicit
stereotypes at least partially reﬂect internalized cultural ‘knowledge’ of stereotypes is
not new. Devine (1989) demonstrated that even low-prejudice individuals (as determine
by self-report) show automatic stereotype activation effects, and research using the IAT
(Greenwald, McGhee, & Schwarzt, 1998) has examined the role of familiarity/strength of
associations in stereotype priming effects (e.g., Fazio, Sanbonmatsu, Powell, & Kardes,
1986; Rudman, Greenwald, Mellott, & Schwartz, 1999). However, to our knowledge,
no study has used objectively measured population-based associative strength values to
control for associative strength, examining both stereotypic and non-stereotypic primes
and stereotypic and non-stereotypic targets.
We also note that although our results indicate that, at the group level, stereotype
priming effects can be explained as completely as can be expected by the cultural biases
captured by co-occurrence in the language (i.e., to the same extent as priming with non-
social primes can be explained), they do not at all implicate complete explainability at
the level of the individual. Idiosyncratic inﬂuences, such as early experiences, affective
experiences, motivational dynamics, attentional biases, and cognitive consistency, also
play a role in the formation of implicit stereotypes (e.g., Rudman, 2004; Sherman et al.,
2009) – our results cannot speak to the mechanisms by which individuals internalize the
cultural dynamic (for recent review, see Fiedler & Walther, 2004). These experiences,
needless to say, would be inter-individually different, and would therefore show up
as inter-individual variability around the mean values derived here. Given that the
mean stereotype priming effect is zero (or, more precisely, −1 ms) after controlling
for associative co-occurrence, these individual experiences on average appear to neither
strengthen nor weaken the prejudice that is present in the culture, perhaps suggesting
that as different individuals move through life, what they individually and implicitly
experience as evidence for or against a stereotype ultimately balances out on the group
level. Of course, our results are based on a sample of college students; the results
may fall out differently when investigated within groups scoring higher (or perhaps
lower) on one of more of the different types of prejudice examined here. We also
note that although our experiment uses objectively derived measures of co-occurrence
in the language, the source of those co-occurrences cannot be traced (at least not
516 Paul Verhaeghen et al.
experimentally), and is unlikely to be objective. That is, associations between concepts
in the language do not necessarily reﬂect objective co-occurrences in the world – these
associations are themselves construed by the speakers of the language, thus forming
a cycle of prejudiced thought begetting prejudiced speech which begets prejudiced
thought and so on, without a clear beginning or end point. Our experiments capture
only a very speciﬁc time slice in this cultural transmission – the inﬂuence of exposure to
a broad corpus of information (acquired over the course of many years) on prejudiced
behaviour as measured by priming effects within a group of individuals.
Our study has other limitations as well. We did not achieve complete stimulus control:
stereotypical end non-stereotypical prime-target pairs should ideally all have the same
(or the same narrow range of) associative values. We were not able to construct such
stimuli, because stereotypical pairs have much higher associative values – one might even
argue that this is what makes them stereotypical. Likewise, the ideal experiment would
keep targets constant and vary primes while keeping the associative values constant; this
proved undoable, for the same reason.
On a ﬁnal note, we want to make it very clear that the results of this study should not
be used to condone prejudice, encourage intolerance, or excuse hate crimes. While it
appears reasonable to state that what the automatically activated prejudice at the level of
an individual may be determined to a potentially large part by the individual’s immersion
in the prevailing culture, society’s inﬂuence on its individual constituents does not
absolve these individuals from their own personal responsibilities and the consequences
of their actions. As a great deal of research has demonstrated, regardless of the origin
of stereotype priming effects, these effects have measurable real-world implications for
behaviours (e.g., Fazio & Olson, 2003; Greenwald et al., 2009). The consequences of
bias and prejudice are all too real, even if part of the origin of personal prejudice may be
situated within the culture.
We would like to thank Michael H. Jones for providing us with the BEAGLE associative
strengths for the prime-target pairs used in our experiments.
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Received 3 September 2009; revised version received 18 June 2010