Behaviour Research and Therapy, in press
Predicting Return of Fear Following Exposure Therapy
With an Implicit Measure of Attitudes
Michael W. Vasey*, Casaundra N. Harbaugh, Adam G. Buffington,
Christopher R. Jones, & Russell H. Fazio
Department of Psychology, The Ohio State University,
1835 Neil Avenue, Columbus, OH 43210-1222, USA
Running head: Predicting Return of Fear with an Implicit Measure of Attitudes
Key words: Exposure; Return of fear; Social phobia; Fear of public speaking; Implicit measures;
* Corresponding author. Department of Psychology, The Ohio State University, 1835 Neil
Avenue, Columbus, OH 43210-1222, USA. Tel.: +1 614 292 2951; fax: +1 614 688 8261
Phone: (614) 292-2951; email: firstname.lastname@example.org
We sought to advance understanding of the processes underlying the efficacy of exposure
therapy and particularly the phenomenon of return of fear (ROF) following treatment by drawing
on a social psychological view of phobias as attitudes. Specifically, a dual process theory of
attitude-related behavior predicts that a positive response to exposure therapy may reflect change
in either the automatic (the attitude representation itself) or controlled (skills and confidence at
coping with the fear) responses to the phobic stimulus, or both. However, if the attitude
representation remains negative following treatment, ROF should be more likely. We tested this
hypothesis in a clinical sample of individuals with public speaking phobia using a single-session
exposure therapy protocol previously shown to be efficacious but also associated with some
ROF. Consistent with predictions, a post-treatment implicit measure of attitudes toward public
speaking (the Personalized Implicit Association Test [PIAT]) predicted ROF at 1-month follow-
up. These results suggest that change in the automatically activated attitude toward the phobic
stimulus is an important goal of exposure therapy and that an implicit measure like the PIAT can
provide a useful measure of such change by which to gauge the adequacy of exposure treatment
and predict its long-term efficacy.
Predicting Return of Fear Following Exposure Therapy
With an Implicit Measure of Attitudes
After decades of research documenting the efficacy of exposure therapy for phobic
anxiety disorders (Choy, Fyer, & Lipsitz, 2007), much about the technique remains inadequately
understood (Moscovitch, Antony, & Swinson, 2009). This is perhaps no more clearly
demonstrated than by the fact that it remains difficult to determine when gains achieved during
treatment will persist or fade over time (Craske et al., 2008). Despite showing excellent
reductions in fear in the context of therapy, many treatment responders will, eventually,
experience some degree of “return of fear” (ROF; Rachman, 1989). ROF is defined simply as the
reappearance of a fear that was previously present but had undergone a decline. Although ROF
typically does not amount to full clinical relapse, it is a significant problem in a substantial
percentage of cases and the phenomenon is not well understood (Craske & Mystikowski, 2006).
In particular, predictors of ROF’s occurrence have proven elusive (e.g., see Craske et al., 2008).
Translational research drawing on advances in learning theory (see Craske, Hermans, &
Vansteenwegen, 2006) and neuroscience (see McNally, 2007) has led to significant progress in
understanding of ROF. In our view, work drawing on advances in social psychology has similar
promise. From a social cognitive perspective on attitudes (see Fazio, 2007), phobias are
essentially inappropriately negative and highly accessible attitudes toward objects (e.g., spiders)
or situations (e.g., public speaking). Because the basic science of attitudes and attitude-related
behavior is well developed, it has considerable potential to advance understanding of the
processes underlying the efficacy of exposure therapy and the durability of its effects.
Specifically, we believe conceptualizing phobias from the perspective of a dual process theory of
attitude-related behavior, such as the MODE model (Fazio, 1990; Olson & Fazio, 2009), can
help to explain ROF’s occurrence.
The MODE model distinguishes two classes of attitude-to-behavior processes. The
difference centers on the extent to which pursuing a particular course of action involves a
spontaneous reaction to one's perception of the immediate situation versus deliberation regarding
the behavioral alternatives. Thus, MODE is an acronym for Motivation and Opportunity as
DEterminants – determinants of whether attitude-related behavior is primarily spontaneous
versus controlled in nature. The model postulates that attitudes can guide behavior in a
spontaneous manner, without the individual actively considering the relevant attitude and
without the individual's necessary awareness of its influence. Instead, the attitude may be
activated from memory automatically upon the individual’s encountering the attitude object and
influence how the person construes the object in the immediate situation. Ultimately, this
construal will affect the person's behavioral response. Alternatively, attitudes may guide
behavior through a more deliberative process. Individuals analyze the costs and benefits of a
particular behavior and deliberately reflect upon the attitudes relevant to the behavioral decision
so as to arrive at a behavior plan, which then may be enacted (see Ajzen, 1991). Given the
effortful reflection required by the deliberative alternative, some motivating force is necessary to
induce individuals to engage in such reasoning. The time and the resources to deliberate (i.e., the
opportunity) also must exist.
In addition to delineating two distinct classes of attitude-behavior processes, the MODE
model explicitly postulates the possibility of "mixed" processes that involve both automatic and
controlled components. Any controlled component within a mixed sequence requires, once
again, that the individual be both motivated to engage in the necessary effort and have the
opportunity to do so. Thus, some individuals might be motivated to check on the
appropriateness, or even counter the influence, of an automatically-activated attitude. That
motivation might stem from an enhanced desire for accuracy, a sense of accountability, a
concern with social desirability, or, as in research concerning racial prejudice, a motivation to
control for prejudiced reactions. Within the MODE model, opportunity is essentially a gating
mechanism. One must have the self-regulatory resources and the time for such motivated
processing. Provided the opportunity exists, an individual can counter or even correct for the
influence of the automatically-activated attitude. So, for example, in the domain of racial
attitudes, evidence shows that some individuals are troubled by the activation of a negative racial
attitude and, provided the opportunity exists, will work very hard to correct for that unwanted
influence. However, if the individual is fatigued or cognitively taxed in some way, or if the
situation demands an immediate response, there will be little opportunity to engage in motivated
deliberation. In that case, judgments or behavior tend to be influenced by the automatically
activated attitude, regardless of any motivational concerns (see Olson & Fazio, 2009, for a
In our view, the MODE model has important implications for understanding exposure
therapy. As noted earlier, many individuals who exhibit improvement following treatment
nonetheless experience ROF. The MODE model leads us to raise the question: what changed
during treatment in such individuals – the automatic or the controlled component, or both? Did
the individual’s attitudinal representation of the feared object change, or just the individual’s
skills at controlling the automatically-activated fear? Although progress of the latter sort is
certainly desirable, if the attitude remains intact, the negativity will continue to be activated
whenever the feared object or situation is encountered. This may happen at a time when the
individual is fatigued or in a context in which he/she believes him/herself incapable of effective
control, in other words, when the opportunity for successful effortful control is lacking.
Moreover, the very experience of anxiety and the efforts to regulate it have the potential to
exacerbate any such fatigue and interfere with performance (Jones, Fazio, & Vasey, in press).
This may erode the individual’s confidence regarding the management of the fear and foster
avoidance and safety behaviors that promote relapse.
Interestingly, a similar implication can be derived from cognitive models that distinguish
between the automatic and strategic processes that play a role in anxiety (e.g., Beck & Clark,
1997; Ouimet, Gawronski, and Dozois, 2009). Like the MODE model, these information
processing models point to the possibility of the automatic component exerting an especially
dominant role when the opportunity for strategic processing is lacking and, hence, imply that
treatment must foster change in the automatic processes. As we have argued, however, the
MODE model’s grounding in the attitude literature highlights the significance of the attitudinal
representation in memory and offers a clear prediction regarding the value of promoting change
in the representation for limiting the potential for ROF and, importantly, of measuring the extent of
such attitude change to predict its occurrence.
To summarize, the MODE model predicts that successful exposure therapy requires, not
just the development of fear-management skills and the confidence that one can control the fear,
but attitude change. Thus, what is needed is a way to measure the attitude representation of the
phobic object following treatment. Drawing on findings from social psychology, a number of
scholars have suggested that implicit measures of attitudes may prove useful in this regard (e.g.,
Hermans et al., 2002; Teachman & Woody, 2003).
Insofar as implicit measures tap the underlying attitude representation regarding the
phobic stimulus, ROF should be likely when negative automatically activated attitudes toward
the phobic stimulus persist following exposure therapy. In addition to such well-documented
problems with self-reported assessments as their sensitivity to demand characteristics and social
desirability concerns, people who have just undergone some treatment for an anxiety disorder
may not be very good at reporting any success that they have achieved. They simply may not be
very well-calibrated at estimating their improvement across time or the extent of their fear
relative to what is normative. Consistent with this view, Craske et al. (2008) note that client
judgments of their own competence during exposure therapy are not good predictors of actual
competence at a later re-testing. Indeed, they suggest that the typical approach to exposure
therapy (i.e., one involving massed training and constancy of training conditions) fosters
illusions of competence. For all these reasons, the estimates of automatically-activated attitudes
provided by an appropriately designed implicit measure should serve as valuable benchmarks of
A variety of evidence shows that implicit measures do indeed differentiate those with
phobias from non-phobic controls (see Roefs et al., 2011). Importantly, such measures typically
account for significant variance in behavioral approach task (BAT) performance above and
beyond that accounted for by explicit measures (e.g., Asendorpf, Banse, & Mucke, 2002;
Ellwart, Rinck, & Baker, 2006; Teachman & Woody, 2003; but see Huijding & de Jong, 2007).
However, although some evidence suggests that implicit measures may be sensitive to change
following exposure treatment (Gamer et al., 2008; Teachman & Woody, 2003), such effects have
not always been found (Huijding & de Jong, 2007; 2009; Teachman & Woody, 2003) and when
found the effect is typically small and may reflect practice rather than treatment effects (see
Roefs et al., 2011). Thus, it has been difficult to discern whether the treatment did not produce
attitude change or the implicit measure was insensitive to such change. Moreover, the only study
to test an implicit measure as a predictor of ROF failed to find a significant effect (Huijding & de
Jong, 2009). In that case, however, it should be noted that the implicit measure also did not
reveal significant change following treatment, likely limiting its ability to predict ROF.
Although the failure to find consistent evidence of exposure effects using implicit
measures may simply reflect their absence, it may also reflect limitations of the specific
measures used. For example, the attribute response labels (e.g., pleasant/unpleasant) used in the
traditional form of the most widely employed implicit measure, the Implicit Association Test
(IAT), have been shown to be problematic when the IAT is intended to measure attitudes. The
traditional IAT involves sorting four categories of stimuli (e.g., a race IAT may involve pictures
of pleasant and unpleasant stimuli with pictures of White and Black faces) using only two keys.
Performance depends on how easily the participant can cope with the dual meanings of the
response keys (e.g., Black/pleasant and White/unpleasant in some blocks versus
Black/unpleasant and White/pleasant in other blocks). For example, a negative attitude toward
Blacks would be demonstrated by faster responses when Black and unpleasant are mapped onto
the same key than when Black and pleasant are represented by the same key. However, because
the typical evaluative labels are inherently ambiguous with respect to perspective (pleasant or
unpleasant to whom?), IAT performance is easily affected by how the labels are interpreted (see
Han, Czellar, Olson, & Fazio, 2010). Although the respondent’s attitudes can affect the ease of
accommodating to a particular response mapping, contextually salient information that does not
form the basis for the individual’s attitude also can do so, for example, by encouraging adoption
of the perspective of society as whole (Olson & Fazio, 2004) or of some specific individual with
an attitude that differs from one’s own (Han, Olson, & Fazio, 2006). Therefore, traditional
implementations of the IAT can be influenced by factors other than the attitudinal associations
that the procedure aims to measure, resulting in mis-estimation of an individual’s attitude.
Recent research has demonstrated that this malleability of the IAT labels can lead to the
inference that attitude change has occurred even when there is very good reason to believe it has
not, and conversely, can obscure attitude change when such change is indeed likely to have
occurred (Han et al., 2010).
Recent research also suggests that focusing the IAT on personal attitudes by using more
specific evaluative labels can obviate these concerns. Simply modifying the response labels from
“pleasant/unpleasant” to “I like/I don’t like,” and thus personalizing the IAT, provides a more
precise measure of individuals’ personal evaluations. This personalized IAT (PIAT) has proven
more sensitive to the presence or absence of attitude change (Han et al., 2010) and has yielded
stronger correspondence with past behavior and behavioral intentions (Olson & Fazio, 2004)
than the traditional implementation.
On this basis, we developed and refined a PIAT aimed at assessing attitudes toward
public speaking and used it to test for change in the attitudinal representation following exposure
therapy and to predict ROF in individuals with a phobia of public speaking. Specifically, this test
was conducted in the context of a single session exposure treatment developed by Tsao and
Craske (2000), who found it to produce significant reductions in fear of public speaking but also
to be associated with significant ROF over a follow-up interval of 1-month. Our focus is on
whether the snapshot of the attitudinal representation provided by a PIAT administered
immediately following treatment proves predictive of any ROF evident at the 1-month follow-up
Participants were 40 adults (60% female) ranging in age from 18-46 years (M = 22.4, SD
= 5.7). Most members of the sample self-identified as Caucasian (70%), with 17.5% Asian-
American, 7.5% Hispanic, and 5% Bi- or Multi-Racial. Based on administration of the
Structured Clinical Interview for DSM Disorders (SCID; First, Spitzer, Gibbon, & Williams,
1995), all participants met diagnostic criteria at minimum for Social Anxiety Disorder (SAD) in
the context of public speaking, with 20% also meeting criteria for SAD–Generalized Type.
Exclusion criteria were: 1) Current major depressive disorder or suicidal ideation; 2) Current or
past diagnosis of bipolar disorder, schizophrenia, other psychosis, or organic mental syndrome;
or 3) Current psychosocial treatment. Participants could be taking anxiolytic or antidepressant
drugs, provided dose was stable during the study.1
Participants were recruited in two ways. First, students in introductory psychology
classes completed the Personal Report of Confidence as a Speaker (PRCS; Paul, 1966) as part of
a screening battery. Those with scores ≥ 26 (94th percentile; Phillips, Jones, Rieger, & Snell,
1997) were telephoned and offered the opportunity to participate in a study in which they would
receive treatment for their fear of public speaking. Those indicating interest completed a
screening interview to assess likely eligibility. Second, fliers describing the study were posted
around the campus of a large Midwestern university. Potential participants responded by phone
and completed a screening interview designed to assess likely eligibility.
Personal Report of Confidence as a Speaker (PRCS; Paul, 1966). The PRCS is a widely
used measure of public speaking anxiety, which consists of 30 true/false items, some of which
1 Three participants (7%) were doing so.
are reverse-scored. Scores can range from zero (no fear) to 30 (extreme fear). It includes such
face-valid items as “I prefer to have notes on the platform in case I forget my speech,” “I feel
relaxed and comfortable while speaking,” and “I look forward to the opportunity to speak in
public.” A large body of evidence supports the PRCS’s reliability and validity (for a review see
Phillips et al., 1997). In the current study, Cronbach’s alpha ranged from .91-.94 across time
Brief Fear of Negative Evaluation Scale (BFNE; Leary, 1983). The BFNE is a 12-item
scale measuring anxiety concerning social evaluation. Items are rated on a 5-point Likert scale
ranging from 1 (not at all characteristic of me) to 5 (extremely characteristic of me). The scale
has been shown to have good reliability and validity (Collins, Westra, Dozois, & Stewart, 2005;
Leary, 1983). In the current study, Cronbach’s alpha ranged from .94-.96 across time points.
State-Trait Anxiety Inventory – State Version (STAI [Form Y]; Spielberge, Gorsuch,
Lushene, Vagg, & Jacobs, 1983). The STAI – State Version is a 20-item scale designed to
measure anxiety at the present moment. Items are rated on a 4-point Likert scale ranging from 1
(not at all) to 4 (very much so). The scale has been shown to have excellent reliability and
validity (Spielberger, 1989). In the current study, Cronbach’s alpha ranged from .92-.97 across
Behaviours Checklist (BCL; Mansell & Clark, 1999). The BCL is an 18-item self-report
scale on which participants rate how anxious they think they appeared during a speech and the
extent to which they would be judged by others to have shown various positive (e.g., confident,
self-assured) and negative (e.g., awkward, voice quivering) characteristics. Ratings on each
characteristic are made on a 9-point Likert scale ranging from 0 (not at all) to (extremely).
Positive items are reversed and a total score is computed, which has been shown to have good
internal consistency and validity (e.g., Wild, Cark, Ehlers, & McManus, 2008). Cronbach’s alpha
ranged from .93-.94 across time points.
Heart rate (HR) was recorded continuously during each BAT using a Polar ambulatory
heart-rate monitor attached to a standard chest belt. Data were transmitted to a model RS800
Polar watch. Mean HR was computed for each BAT. Due to equipment malfunction, HR data
were lost for two participants at pre- and post-treatment and 4 participants at follow-up.
In each BAT, participants were given 3-minutes to prepare a 5-minute speech on two
topics selected at random without replacement from a set of 16 topics modeled after those used
by Tsao and Craske (2000). All speeches were delivered without notes in the presence of the
experimenter and while facing a video camera. The second of the two BATs at follow-up also
included a live audience of three research assistants. Immediately before and at 1-minute
intervals during each BAT, participants provided Subjective Units of Distress (SUDS; Wolpe,
1969) ratings on a scale ranging from 0 (no anxiety) to 100 (extreme anxiety).
Five undergraduate research assistants independently rated the overall quality and
effectiveness of the speeches. A video recording of each speech was viewed by a random set of
three of these judges. Raters were kept blind to participants’ scores on other measures and the
point in the sequence of sessions when a given speech occurred. Each speech was rated on two
questions: (1) “How would you rate the overall quality of the speech?” and (2) “How would you
rate the presenter’s effectiveness as a speaker?” Ratings were made on an 11-point scale ranging
from 0 (very poor) to 10 (very high). Because the correlation between the two ratings was very
high (r = .89), they were averaged to create a single index of speech quality. ICCs for these
average ratings ranged from .82 to .91. Jones, Fazio, and Vasey (in press) found this score to
correlate significantly with speakers’ self-ratings of fear of public speaking and speech
performance. Similarly, in the current study, for the pre-treatment BAT, the average speech
quality rating correlated significantly with participants’ self-ratings on the BCL (r = -.63) and
their maximum SUDS ratings during each BAT (r = -.48).
PIAT. The PIAT focused on the strength of personal associations between “public
speaking” and “I like” versus “I don’t like.” Because there is no obvious contrast category to
public speaking, the implementation involved the single category version of the IAT (Karpinski
& Steinman, 2006; Wigboldus, Holland, & van Knippenberg, 2005), as well as personalization.
During the critical blocks, participants were presented with three classes of images – public
speaking (e.g., a lectern, a microphone, an audience), positively-valued objects (e.g., an ice
cream sundae, a gift box, picturesque scenery), and negatively-valued (e.g., polluting
smokestacks, burnt toast) and classified them using two response keys. In some blocks, one key
was labeled “I Like” and “Public Speaking” and the other key “I Don’t Like” whereas other
blocks involved “I Don’t Like” and “Public Speaking” sharing a response key. The task
consisted of 10 blocks of 30 trials each. The first two blocks were for practice. In the remaining
eight blocks, public speaking was mapped onto the “I like” key in four blocks and onto the “I
don’t like” key in the remainder. The two types of blocks were administered in counterbalanced
order. Instructions regarding the meaning of the two keys were presented at the beginning of
each block. The critical comparison involves the difference in latencies for the two response
mappings, with higher scores indicating a more negative attitude, as shown by faster latencies
when “Public Speaking” and “I Don’t Like” were mapped on to the same response key than
when “Public Speaking” and “I Like” were mapped on to the same key. The eight blocks of
critical trials thus produced four scores representing this critical difference, which were averaged
to yield a total PIAT score. Reliability of this score was evaluated by computing Cronbach’s
alpha for the four difference scores. Alpha averaged .66 across the three PIAT tasks.
The study involved three sessions. In Session 1 potentially eligible individuals provided
informed consent and completed the PIAT followed by the SCID, which was administered by
two doctoral students in clinical psychology trained to a minimum of 80% agreement. Inter-rater
agreement in this sample was very good (Kappa = .82). Those meeting eligibility requirements
were invited to attend Session 2 (typically within 2-weeks of Session 1), in which the exposure
treatment was administered by two clinical psychology doctoral students. Session 2 typically
lasted about 2-hours. During that session, participants completed the PRCS and BFNE, and
immediately before completing the pre-treatment speech BAT, the STAI. Immediately following
the speech BAT, they completed the BCL. They then completed the exposure treatment.
Following treatment, participants again completed the PRCS, BFNE, the STAI, the post-
treatment speech BAT, the BCL, and finally, the PIAT. After 1-month, participants returned for
Session 3, at which they again completed the PIAT, followed by the PRCS, BFNE, and two
BATs, each preceded by the STAI and each followed by the BCL. The 1-month follow-up
interval was chosen because Tsao and Craske (2000) found significant ROF over that interval
using the same treatment.
Exposure Treatment: The exposure treatment was conducted according to a detailed
manual based on the massed exposure condition used by Tsao and Craske (2000). After
receiving didactic information about public speaking anxiety and a treatment rationale
emphasizing the importance of exposure, participants completed four exposure trials during
which they were given 2-minutes to prepare prior to delivering 5-minute speeches. For each
speech, participants drew two topics randomly and without replacement from the same set of
topics used for the BATs. SUDs ratings were obtained pre-speech and at 1-minute intervals
thereafter. Each speech was presented without the use of notes before the therapist and an
audience of three research assistants (trained to remain impassive and refrain from offering
encouragement [e.g., nodding], or discouragement [e.g., frowning]). Therapists intervened only
to obtain SUDs ratings and to encourage the participant to continue speaking if he/she stopped
prematurely. All treatment was supervised by a licensed clinical psychologist and treatment
sessions were observed either live or via video recording by the first author to monitor treatment
Following Session 3, participants were offered an opportunity to receive further exposure
therapy if desired. Those who did not complete the study (e.g., due to being deemed ineligible or
declining treatment) were offered assistance in finding appropriate services.
Did Fear Decline Significantly Following Treatment?
Paired t-tests revealed significant improvement from pre- to post-treatment on all
measures (Table 1). Most notably, PIAT scores were significantly reduced, indicating a less
negative attitude representation following treatment.
Did ROF Occur at Follow-up?
ROF was assessed at both the average and the individual levels. As shown in Table 1, on
average, significant increases from post-treatment to follow-up were seen in anticipatory STAI
scores for BAT 2 but not BAT 1. A similar pattern emerged for pre-speech SUDs ratings
although only at a trend level. Similarly, ROF on average was seen for maximum SUDs rating
during BAT 2 but not BAT 1. Finally, ROF was seen for mean heart rate during both BATs. In
contrast, gains were maintained on average from post-treatment to follow-up on the PIAT and
observer ratings of speech quality, while further improvement was seen in BFNE scores.
At the individual level, the percentage of participants whose scores at follow-up reflected
an increase in fear (or a decrease in speech quality) from post-treatment levels ranged from 25%
to 66%, with an average of 49.2% across measures. Thus, there was sufficient variability in ROF
to support regression analyses designed to test the PIAT as a predictor of ROF.
Predictors of ROF at Follow-Up
We computed ROF scores for each measure at follow-up by subtracting the post-
treatment score from the follow-up score. Thus, positive values on these ROF scores indicated an
increase from post-treatment to follow-up. To determine if post-treatment PIAT scores predicted
outcomes at follow-up above and beyond explicit measures of public speaking anxiety at post-
treatment, each of the ROF scores at follow-up served as the dependent variable (DV) in a
regression analysis in which the predictors were the PIAT, the pre- and post-treatment versions
of the DV and the post-treatment PRCS and BFNE scores. The latter two measures were
included to clarify if the implicit measure accounted for variance above and beyond explicit
measures of social anxiety. Each of the predictor variables was standardized to ease
interpretation of the results.
Because the pattern of results for these regression analyses did not differ for the two
BATs at follow-up, to simplify presentation, all variables were averaged across those BATs
when computing the ROF scores. Similarly, because the anticipatory STAI state anxiety scores
and SUDs ratings were strongly correlated across BATs (average r = .67), we created an
anticipatory anxiety index by combining those scores for each BAT. For pre- and post-treatment
these scores were simply standardized and averaged. However, to create an interpretable ROF
score at follow-up, scores were standardized using the post-treatment mean and standard
deviation. Thus, a positive score on this index at follow-up indicates an increase from post-
treatment to follow-up in post-treatment SD units.
Results of the regression analyses are summarized in Table 2. Unsurprisingly, in most
cases the best predictor of a given variable at follow-up was the same variable at either pre- or
post-treatment. However, as shown in Table 2, post-treatment PIAT scores accounted for
significant variance in the case of three DVs: Anticipatory Anxiety, BCL, and Mean HR. In each
case, the regression coefficient makes it clear that, for an individual scoring at the average on the
other predictor variables (i.e., z-scores equaling zero) PIAT scores that are above average (i.e.,
more negative) predict ROF scores greater than zero. Because the intercept in each case is also
above zero, these positive ROF scores indicate increases in fear from post-treatment to follow-
up. For example, in the case of Mean HR, the predicted ROF score for an individual scoring at
the average for all predictors, including the PIAT is simply the intercept (i.e., 4.23 beats per
minute [BPM]). Thus, for Mean HR, the average individual is predicted to show significant ROF
because the intercept is significantly greater than zero. But for the same individual with a PIAT
score of +1 SD (i.e., +55.4 ms) the predicted ROF score would be 4.81 BPM higher. Similarly,
for the BCL a PIAT score of +1 SD predicts an increase of 5.32 points beyond the average level
of 3.62 indexed by the intercept. For the Anticipatory Anxiety Index, a PIAT score of +1 SD
predicts an increase of .20 SDs beyond an average ROF of .14 SDs.
The PIAT also approached significance for the BFNE score, with a PIAT score of +1 SD
predicting an increase of 1.76 points but on average ROF was -2.28. Thus, whereas the average
individual’s BFNE score declined significantly from post-treatment to follow-up, there was a
trend for that to be less likely for those with higher PIAT scores.
Correlates of Post-Treatment PIAT Scores
To investigate how change on the PIAT following treatment related to change on other
outcome measures, following Teachman and Woody (2003), we examined correlations among
residualized change scores on the PIAT and other dependent measures. Change on the PIAT was
not significantly predicted by change on any other measure following treatment (rs ranged from -
.11 to .14, ps > .39).
In this study we sought to advance understanding of the processes underlying the efficacy
of exposure therapy and particularly the phenomenon of ROF by drawing on a social
psychological view of phobias as attitudes. Specifically, based on the MODE model (Fazio,
1990), we predicted that individuals completing exposure therapy would vary in the degree to
which their attitude representation of the phobic situation was successfully modified as a result
of the learning opportunities afforded by exposure. To the extent that the attitude representation
remains predominantly negative following treatment, ROF should be more likely. We tested this
hypothesis in a sample of individuals with public speaking phobia using an implicit measure of
attitudes toward public speaking (i.e., the PIAT) designed to minimize the influence of
extrapersonal factors and thereby enhance its sensitivity to attitude change.
Consistent with the findings of Tsao and Craske (2000) using the same treatment that we
employed, participants showed significant improvement following treatment on a wide range of
measures. It is notable that participants, on average, showed improvement not only on self-report
measures, but also on a measure of heart rate during a speech, observer ratings of speech quality,
and especially on the PIAT. Furthermore, whereas the reduction in PIAT scores was maintained
at follow-up, ROF was evident on a variety of other measures. Thus, this study provided an
excellent context for testing our hypotheses regarding the predictive value of the PIAT.
Consistent with expectations, in the case of three DVs, the post-treatment PIAT accounted for
significant variance in ROF even after controlling for pre- and post-treatment scores on the DV
as well as two explicit measures of public speaking fear. Specifically, this was true for pre-
speech anticipatory anxiety, post-speech beliefs about observers’ evaluations (i.e., BCL scores)
and mean HR during the speeches at follow-up. That this was true of an objective physiological
measure of anxiety is particularly noteworthy. In contrast, scores on explicit outcome measures
at follow-up were generally not significantly related to post-treatment scores on explicit
measures of fear of public speaking (i.e., the PRCS and BFNE).
In sum, these results provide clear support for the predictions derived from the MODE
model. In the month following successful exposure treatment some individuals experienced ROF
and a “snapshot” of the relevant attitudinal representation (the PIAT) taken immediately after
treatment was able to predict significant variance in its occurrence. This implies that
improvement during treatment is accompanied by attitude change for some individuals. Others
may have learned to control their fear but not have experienced a change in the attitudinal
representation and, hence, continue to have negativity automatically activated when encountering
the fearful situation.
It is important to note that these results can also be understood within the context of a
model of exposure therapy that emphasizes the centrality of inhibitory learning to the process of
extinction (Bouton, Woods, Moody, Sunsay, & Garcia-Gutierrez, 2006). However, as noted by
Craske et al. (2008), the strength of such inhibitory associations acquired is not well indexed by
fear levels expressed during or immediately following extinction. Consequently, a mismatch is
often seen between the level of fear immediately following exposure and that seen in a different
context or at a later time. From this perspective, the PIAT appears to provide a better measure of
the strength and/or breadth of the inhibitory associations acquired through exposure than do
explicit measures. In keeping with past research seeking to predict ROF (see Craske et al., 2008),
measures of fear reduction following treatment were unrelated to change on the PIAT.
Whereas a variety of processes may weaken inhibitory learning over time, it is important
to note that the PIAT predicted the occurrence of ROF before any time had passed. Thus, ROF in
this study appears to have been a function of inadequate inhibitory learning during exposure that
was not detected by explicit outcome measures or by a measure of physiological arousal or
objective ratings of speech quality. It will be important for future research to identify predictors
of, and more generally strategies for promoting, attitude change (i.e., inhibitory learning). For
example, inhibitory learning is likely to be enhanced when the exposure treatment context
maximizes client expectancies for negative outcomes so as to create the greatest possible
mismatch between expected and actual outcomes (Craske et al., 2008). This is most likely when
exposure occurs in the presence of as many predictors of the expected aversive outcome as
possible. It may be that those showing the greatest attitude change were those for whom the
standardized exposure context made the best contact with the complex of stimuli associated with
their expectancy of negative outcomes. Similarly, those showing the least attitude change may be
those for whom the standard exposure context may have included one or more safety signals or
those whose safety behaviors were insufficiently limited during the standardized exposure format
(Craske et al., 2008). Thus, the PIAT appears to provide a useful metric by which to gauge the
adequacy of inhibitory learning achieved through exposure and to predict long-term treatment
It is noteworthy that the PIAT proved to be most predictive of ROF in variables related to
anticipatory anxiety (i.e., pre-speech SUDS and STAI State Anxiety) and post-event processing
(i.e., beliefs endorsed on the BCL that one will be judged to have appeared very nervous and to
have delivered a poor quality speech). As emphasized by authors like Wells and Clark (1997)
and Hofmann (2007), anticipatory anxiety and post-event processing are central contributors to
the maintenance of social anxiety. Anticipatory anxiety drives avoidance and, if avoidance is
impossible, safety behaviors are used to minimize the threat posed by the situation. This suggests
the possibility that incomplete attitude change (i.e., inadequate inhibitory learning) may lead to
ROF because anticipatory anxiety leads to avoidance of and/or failure to seek out further
exposure experiences during the follow-up interval. This and similar mediators of the link
between post-treatment PIAT scores and ROF should be considered in future research.
It is important to consider several limitations of the study. First, the sample was relatively
small and therefore caution is warranted in generalizing to the broader population. Second,
although participants met DSM-IV criteria for social anxiety disorder, severity in this sample
was generally moderate. Only about 20% met the more stringent criteria for generalized social
anxiety disorder. Obviously, a more severe group would likely show less response to a single
session of treatment. However, we see no reason to believe that the PIAT would have less value
as an index of attitude change in such a sample.
In conclusion, results of this study suggest not only that change in the attitude
representation regarding the phobic stimulus is an important goal of exposure therapy but that the
PIAT appears to provide an efficient means of measuring such change. However, it should be
noted that this does not imply that change on explicit measures is unimportant. Rather in our
view the MODE model implies that such change is likely to be insufficient by itself. That view is
confirmed by the current study.
This research was supported by Grant MH38832 from the National Institute of Mental Health to
the first and last authors. The authors thank Dr. Joseph P. DeCola for providing clinical
supervision for the study, Deborah Sharp for her assistance with the PIAT, and Andrew Brush,
Greg Hilbert, Kaitlyn Fieseler, Marissa Mishne, and Jennifer Varela for their help with speech
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50, 179-211.
Asendorpf, J.B., Banse, R., & Mucke, D. (2002). Double dissociation between implicit and
explicit personality self-concept: The case of shy behavior. Journal of Personality and
Social Psychology, 83, 380-393.
Beck, A. T., & Clark, D. A. (1997). An information processing model of anxiety: Automatic and
strategic processes. Behaviour Research and Therapy, 3, 49-58.
Bouton, M. E., Woods, A. M., Moody, E. W., Sunsay, C., & Garcia-Gutierrez, A. (2006).
Counteracting the context-dependence of extinction: Relapse and tests of some relapse
prevention methods. In M. G. Craske, D. Hermans, & D. Vansteenwegen (Eds.) Fear and
learning: From basic processes to clinical implications. Washington, DC: American
Choy, Y., Fyer, A. J., & Lipsitz, J. D. (2007). Treatment of specific phobia in adults. Clinical
Psychology Review, 27, 266-286.
Collins, K. A., Westra, H. A., Dozois, D. J. A., & Stewart, S. H. (2005). The validity of the brief
version of the Fear of Negative Evaluation Scale. Journal of Anxiety Disorders, 19, 345-
Craske, M. G., Hermans, D., & Vansteenwegen, D. (2006). Fear and learning: From basic
processes to clinical implications. Washington, DC: American Psychological
Craske, M. G., Kircanski, K., Zelikowsky, M., Mystikowski, J., Chowdhury, N., & Baker, A.
(2008). Optimizing inhibitory learning during exposure therapy. Behaviour Research and
Therapy, 46, 5-27.
Craske, M. G., & Mystkowski, J. (2006). Exposure therapy and extinction: Clinical studies. In
M. G. Craaske, D. Hermans, & D. Vansteenwegen (Eds.), Fear and learning: From basic
processes to clinical implications (pp. 217-233). Washington, DC: American
Ellwart, T., Rinck, M., & Baker, E. S. (2006). From fear to love: Individual differences in
implicit spider associations. Emotion, 6, 18-27.
Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as
an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social
psychology (Vol. 23, pp. 75-109). New York: Academic Press.
Fazio, R. H. (2007). Attitudes as object-evaluation associations of varying strength. Social
Cognition, 25, 603-637.
First, M. B., Spitzer, R. L., Gibbon, M., Williams, J. B. W. (1995). Structured Clinical Interview
for DSM-IV Axis I Disorders—Patient Edition. New York: Biometrics Research
Department, New York Psychiatric Institute.
Gamer, J., Schmukle, S. C., Luka-Krausgrill, U., & Egloff, B. (2008). Examining the dynamics
of the implicit and the explicit self-concept in social anxiety: changes in the implicit
association test–anxiety and the social phobia anxiety inventory following treatment.
Journal of Personality Assessment, 90, 476-480.
Han, H. A., Czellar, S., Olson, M. A., & Fazio, R. H. (2010). Malleability of attitudes or
malleability of the IAT? Journal of Experimental Social Psychology, 46, 286-298.
Han, H. A., Olson, M. A., & Fazio, R. H. (2006). The influence of experimentally-created
extrapersonal associations on the Implicit Association Test. Journal of Experimental
Social Psychology, 42, 259-272.
Hermans, D., Vansteenwegen, D., Crombez, G., Baeyens, F., & Eelen, P. (2002). Expectancy-
learning and evaluative learning in human classical conditioning: Affective priming as an
indirect and unobtrusive measure of conditioned valence. Behaviour Research and
Therapy, 40, 217-234.
Hofmann, S. G. (2007). Cognitive factors that maintain social anxiety disorder: A comprehensive
model and its treatment implications. Cognitive Behaviour Therapy, 36, 193-209.
Huijding, J., & de Jong, P. J. (2007). Beyond fear and disgust: The role of (automatic)
contamination-related associations in spider phobia. Journal of Behavior Therapy and
Experimental Psychiatry, 38, 200-211.
Huijding, J., & de Jong, P. J. (2009). Implicit and explicit attitudes toward spiders: Sensitivity to
treatment and predictive value for generalization of treatment effects. Cognitive Therapy
and Research, 33, 211-220.
Jones, C. R., Fazio, R. H., Vasey, M. W. (in press). Attentional control buffers the effect of
public-speaking anxiety on performance. Social Psychological and Personality Science.
Karpinski, A., & Steinman, R. B. (2006). The single category implicit association test (SC-IAT)
as a measure of implicit social cognition. Journal of Personality and Social Psychology,
91, 16- 32.
Leary, M. R. (1983a). A brief version of the Fear of Negative Evaluation Scale. Personality and
Social Psychology Bulletin, 9, 371–375.
Mansell, W., & Clark, D. M. (1999). How do I appear to others? Social anxiety and processing
of the observable self. Behaviour Research and Therapy, 37, 419-434.
McNally, R. J. (2007). Mechanisms of exposure therapy: How neuroscience can improve
psychological treatments for anxiety disorders. Clinical Psychology Review, 27, 750-759.
Moscovitch, D. A., Antony, M. M., & Swinson, R. P. (2009). Exposure-based treatments for
anxiety disorders: Theory and process. In M. M. Antony & M. B. Stein (Eds.), Oxford
handbook of anxiety and related disorders (pp. 461-475). New York: Oxford University
Olson, M. A., & Fazio, R. H. (2004). Reducing the influence of extrapersonal associations on the
Implicit Association Test: Personalizing the IAT. Journal of Personality and Social
Psychology, 86, 653-667.
Olson, M. A., & Fazio, R. H. (2009). Implicit and explicit measures of attitudes: The perspective
of the MODE model. In R. E. Petty, R. H. Fazio, & P. Briñol (Eds.), Attitudes: Insights
from the new implicit measures (pp. 19-63). New York, NY: Psychology Press.
Ouimet, A. J., Gawronski, B., & Dozois, D. J. A. (2009). Cognitive vulnerability to anxiety: A
review and an integrative model. Clinical Psychology Review, 29, 459-470.
Paul. G. L. (1966). Insight versus desensitization in psychotherapy. Stanford, CA: Stanford
Philips, G. C., Jones, G. E., Rieger, E. J., & Snell, J. B. (1997). Normative data for the Personal
Report of Confidence as a Speaker. Journal of Anxiety Disorders, 11, 215-220.
Rachman, S. J. (1989). The return of fear: Review and prospect. Clinical Psychology Review, 9,
Roefs, A., Huijding, J., Smulders, F. T. Y., MacLeod, C. M., de Jong, P., Wiers, R. W., &
Jansen, A. T. M. (2011). Implicit measures of association in psychopathology research.
Psychological Bulletin, 137, 149-193.
Spielberger, C. D. (1989). State-Trait Anxiety Inventory: Bibliography (2nd ed.). Palo Alto, CA:
Consulting Psychologists Press.
Spielberger, C. D., Gorsuch, R. L., Lushene, R. E., Vagg, P. R., & Jacobs, G. A. (1983). Manual
for the State-Trait Anxiety Inventory. Palo Alto: Consulting Psychologists Press.
Teachman, B. A., & Woody, S. R. (2003). Automatic processing in spider phobia: Implicit fear
associations over the course of treatment. Journal of Abnormal Psychology, 112, 100 –
Tsao, J. C. I., & Craske, M. G. (2000). Timing of treatment and return of fear: Effects of massed,
uniform, and expanding-spaced exposure schedules. Behavior Therapy, 31, 479-497.
Wells, A. & Clark, D. M. (1997). Social phobia: A cognitive approach. In G. C. L. Davey (Ed.),
Phobias: A handbook of description, treatment and theory (pp. 3–26). Chichester,
Wigboldus, D.H.J., Holland, R.W., & van Knippenberg, A. (2005). Single target implicit
associations. Unpublished manuscript, Radboud University Nijmegen.
Wild, J., Clark, D. M., Ehlers, A., & McManus, F. (2008). Perception of arousal in social
anxiety: Effects of false feedback during social interaction. Journal of Behavior Therapy
and Experimental Psychiatry, 39, 102-116.
Wolpe, J. (1969). The practice of behavior therapy. New York: Pergamon Press.
Table 1. Means and (SDs) of outcome measures at pre-treatment, post-treatment, and follow-up.
Speech to Audience
Speech to Camera
Pre-BAT STAI-State Anxiety
SUDs - Anticipatory
SUDs - Maximum
Mean Heart Rate During BAT
Observer Rating of Speech
Note: Means with different subscripts differ at p < .05. However, the two speeches at follow-up were not compared to one another.
N = 40 except where noted. 1 Difference from post-treatment: p = .062; 2 Difference from post-treatment: p = .073; 3 N = 38; 4 N = 36
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Table 2. Summary of regression analyses for PIAT scores predicting change from post-treatment to 1-month follow-up.
Intercept Treatment Treatment.
up minus Post)
PRCS Change .15 .81† (.45) 1.29† (.74) .27† -1.71* (.77)
BFNE Change .27* -2.28* (.89) 5.31* (2.09) .37* -5.98* (.2.09)
BCL Change .44* 3.62† (2.04) 11.06* (3.7) .39* -14.01* (3.3)
Mean Heart Rate
of Speech Quality
Note: All predictors z-transformed; B = unstandardized regression coefficient; sr = semi-partial correlation coefficient. Change scores
reflect follow-up minus post-treatment (i.e., positive change scores indicate an increase from post-treatment to follow-up [i.e., ROF]).
The Anticipatory Anxiety Composite score was the average of the standardized anticipatory SUDS and State Anxiety scores for each
follow-up BAT, each standardized based on the post-treatment mean and SD. Follow-up scores were standardized based on the post-
treatment mean and SD so that a positive change score indicates an increase in SD units from post-treatment to follow-up).
* p < .05; † p < .10
DV at Pre-DV at Post- PRCS at Post-
BFNE at Post-
PIAT at Post-
-.22 (.53) -.06
.43* .14† (.08) .30* (.12) .32* -.16 (.13) .06 (.09)
.32* 3.35* (1.56) 4.37†(2.29) .27† -4.01† (2.37) -.24† .04 (2.16) .00 3.12 (1.89) .23 2.53 (1.89) .20
.35* .26† 4.23* (1.60) 4.36 (3.67) .19 -6.73† (3.73)
.07 (.08) .49* (.21) .32* -.63* (.21) -.42* -.24* (.10) -.33* .00 (.10) .00 .06 (.10) .08