1. What is the question the authors are asking They asked abo.docx

1. What is the question the authors are asking?
They asked about the relation between self-focused attention
and interpersonal consequences of the social anxiety. Also, how
the interpersonal interaction will influence the social anxiety.
On top of that, they hypothesized in their study, the control
group will show increased uncomfortable sign while& after the
interpersonal activities,, like the less verbal speaking and more
protective body language. Also, they expected the control group
will show more negative effect and fairly low in positive effect
after the study.
2. Why do the authors believe this question is important?
Because they have found people have agreement on the positive
relation between the anxiety arousal and the shown anxiety
symptoms, also the interaction and social anxiety. But, the
relation and function of how the self- focused attention will
trigger and influence the social anxiety when interpersonal
activity happens.
3. How do they try to answer this question?
They conducted a study to testify whether the interpersonal
activity will influence or trigger the social anxiety. In order to
do that, they collected 120 participants after they have these
participants did SPAI, the ones who got highest and lowest 20
percentile scores people have been selected as the SA group,
which means they have shown the possibility of being
diagnosed as social phobia patients. And the rest of the 120p
people will consist of the NSA group, which names after the
people who do not have any social phobia symptom when
having interpersonal interaction. After having them grouped,
they will be paired as dyads automatically, the SA with NSA, or
the NSA with NSA. But, they are not informed their identity in
this study. Then, each group will have 5 minutes session to
interact with the other one, they will be video recorded at the
same time they started the session, and there will be people who

spectate their verbal code and nonverbal code when the
recording started. Each codes represent different “behavior” of
participant, for the verbal code, we have RS, ES,Q,ST AND GT
for information sharing. RS is for complaining and support
words said by participants, ES specifies the empathetic
comments, ST represents the information shared about
participant themselves, GT means the sharing information that
unrelated to participants themselves. Same to nonverbal codes,
we have polite smile, pleasurable smile, frown, the fidgeting
hand position. After the 5 minutes session with each other, the
researchers will have participants to do 2 questionnaires,
PANAS and QI, one for detecting the PA and NA level of
participants before and after the study, one for checking the
level of satisfaction of the just finished interaction.
4. What did they find?
They found out the results not exactly same as they predicted,
like for the level of NA in SA- NSA group, it did not have a
significantly rising after the session is ended, either to the
questions asked in the session, SA- NSA group almost having
same amount of question asked during the session. However,
they did found out that SA participants tend to be more silent
and was willing not be the leading questioner in the session.
Also they showed more self- talk while talking to their partner.
For the verbal codes, SA participants was shown fairly higher
level of RS than their NSA partners were, Same results to EC
level. On the contrary hand, for the nonverbal codes, SA
participants shown more polite smile, more fidgeting hands
positioning, and also more polite smile no matter what smile
their NSA partner was displaying.
5. How did the authors interpret what they found?
They concluding the even though SA participants show more
silent moment, more self-talk, more polite smile and more
fidgeting hand positioning, the level of their negative effect
were not increasing while they were interacting with NSA
partners, surprisingly the positive affect level increased
somehow., Which shows the interpersonal activities did have

influence to social anxiety. At the same time, all the symptom
of more time staying silent, more self- talk is more like a way to
stay self- focused, which the researcher suggest will influence
the interaction with the partner in future. Because the NSA
participants are more likely to have a pleasurable interaction
instead of the long time “one sided” or self- focused interaction.
Based on that, researcher ensured that the self- focused
attention and the interpersonal interaction did have impact on
social anxiety.
6. Briefly discuss two original critiques of the study and/or
relevant research questions or
First of all, in the verbal codes area, the researchers did not
cover the result of all the codes such as information sharing
codes, and the other problem is when the coders decide the
amount of question asked by each other, it made me wonder that
does that code eligible to be an “code” in this study? Because
somehow we all know the quality of a conversation does not
depend on how many questions they ask each other. The quality
of the question matter more.
The other critique is in this report, SA participant is the role we
studying, but the interpersonal words mean a two- sides
interaction, based on that, we might need to divide some
proportion to the NSA participants as well, maybe that will
bring us something interesting as well.
lable at ScienceDirect
Behaviour Research and Therapy 71 (2015) 139e149
Contents lists avai
Behaviour Research and Therapy
journal homepage: www.elsevier.com/locate/brat
Evaluating changes in judgmental biases as mechanisms of

cognitive-behavioral therapy for social anxiety disorder
Martha R. Calamaras a, b, Erin C. Tully a, Erin B. Tone a,
Matthew Price a, c,
Page L. Anderson a, *
a Georgia State University, Department of Psychology, P.O.
Box 5010, Atlanta, GA, 30302-5010, USA
b Emory University, Department of Psychiatry and Behavioral
Sciences, Grady Health System, 80 Jesse Hill Jr. Drive SE,
Atlanta, GA, 30303, USA
c University of Vermont, Department of Psychology, 2
Colchester Ave, Burlington, VT, 05405, USA
a r t i c l e i n f o
Article history:
Received 8 January 2015
Received in revised form
25 May 2015
Accepted 15 June 2015
Available online 16 June 2015
Keywords:
Social anxiety disorder
Judgmental biases
Threat reappraisal
Mechanisms of treatment response
* Corresponding author.
E-mail address: [email protected] (P.L. Anderson
http://dx.doi.org/10.1016/j.brat.2015.06.006
0005-7967/© 2015 Elsevier Ltd. All rights reserved.
a b s t r a c t
Reductions in judgmental biases concerning the cost and
probability of negative social events are pre-

sumed to be mechanisms of treatment for SAD. Methodological
limitations of extant studies, however,
leave open the possibility that, instead of causing symptom
relief, reductions in judgmental biases are
correlates or consequences of it. The present study evaluated
changes in judgmental biases as mecha-
nisms explaining the efficacy of CBT for SAD. Participants
were 86 individuals who met DSM-IV-TR
criteria for a primary diagnosis of SAD, participated in one of
two treatment outcome studies of CBT
for SAD, and completed measures of judgmental (i.e., cost and
probability) biases and social anxiety at
pre-, mid-, and posttreatment. Treated participants had
significantly greater reductions in judgmental
biases than not-treated participants; pre-to-post changes in cost
and probability biases statistically
mediated treatment outcome; and probability bias at
midtreatment was a significant predictor of
treatment outcome, even when modeled with a plausible rival
mediator, working alliance. Contrary to
hypotheses, cost bias at midtreatment was not a significant
predictor of treatment outcome. Results
suggest that reduction in probability bias is a mechanism by
which CBT for SAD exerts its effects.
© 2015 Elsevier Ltd. All rights reserved.
Theoretical models posit that social anxiety disorder (SAD) is
maintained in part by judgmental biases concerning the
probability
and cost of negative social events (e.g., Clark & Wells,1995;
Rapee &
Heimberg, 1997). Specifically, individuals with SAD tend to
believe
that negative social events are extremely likely to occur (i.e.,
probability bias), and that if such events were to happen, the
consequences would be awful or unbearable (i.e., cost bias). Foa

and
Kozak (1986) argued that one mechanism of action of cognitive-
behavioral therapy (CBT) for anxiety disorders is a reduction in
the exaggerated perception of probabilities and costs associated
with feared outcomes. This idea has been termed the threat
reap-
praisal mediation hypothesis. From a CBT perspective, cost and
probability biases are modified through challenges to distorted
cognitions about the cost and probability of negative social
events
and through exposure, in which a person learns that feared out-
comes are not as likely or as costly as anticipated. It is the shift
of
the distorted cognitions to more realistic appraisals and the new
).
learning that results from exposure that leads to reduced
anxiety.
Some researchers have questioned the causal role of cognitive
change in clinical improvement with CBT (e.g., Longmore &
Worrell, 2007), leading other scholars to call for tests of
existing
mediation models using more recently developed
methodological
guidelines (see Hofmann, 2008). In a recent review of the
literature
on the threat reappraisal mediation hypothesis in cognitive-
behavioral treatment of anxiety disorders, Smits, Julian,
Rosenfield, and Powers (2012) described the following criteria
as
critical to establishing that a variable is a mechanism of
treatment:
1) demonstration of statistical mediation; 2) demonstration that
CBT causes threat reappraisal; 3) demonstration that threat
reap-
praisal causes anxiety reduction; and 4) demonstration of speci-

ficity of the threat reappraisal-anxiety reduction relation.
Formal
tests of statistical mediation are useful in demonstrating signifi-
cance of the paths between treatment and the hypothesized
mechanism (path a) and between the mechanism and the
specified
outcome of interest (path b), and of the indirect mediated a � b
pathway (i.e, Criterion 1). Demonstration that CBT causes
threat
reappraisal (i.e., Criterion 2) is critical to establishing that
threat
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
Delta:1_given name
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M.R. Calamaras et al. / Behaviour Research and Therapy 71
(2015) 139e149140
reappraisal occurred as a result of treatment, versus some other
variable or as a function of time. Studies comparing CBT to
viable
alternative treatments are ideally suited to draw conclusions
that
CBTdversus some non-specific therapeutic factordcaused threat
reappraisal, although studies comparing CBT to a wait-list

control
can also permit researchers to make causal inferences. Demon-
stration that threat reappraisal temporally preceded anxiety
reduction (i.e., Criterion 3) is essential for demonstrating that
changes in the hypothesized mechanism caused changes in the
outcome. Finally, demonstration of specificity by ruling out
other
plausible mechanisms (i.e., Criterion 4) strengthens evidence
for
the causal relation between the mediator and the outcome.
In their review, Smits et al. (2012) identified eight studies that
examined the threat reappraisal hypothesis in relation to SAD.
No
single study both tested and established all four criteria. The
ma-
jority (n ¼ 5; 62.5%) demonstrated statistical mediation (Foa,
Franklin, Perry, & Herbert, 1996; Hoffart, Borge, Sexton, &
Clark,
2009; Hofmann, 2004; Rapee, Gaston, & Abbott, 2009; Smits,
Rosenfield, McDonald, & Telch, 2006). Fewer than half (n ¼ 3;
37.5%), however, established CBT as a cause of threat
reappraisal
(Hofmann, 2004; Rapee et al., 2009; Taylor & Alden, 2008) or
demonstrated specificity of the threat reappraisal-anxiety reduc-
tion relation (Hoffart et al., 2009; Rapee et al., 2009; Smits et
al.,
2006). According to Smits et al., four studies attempted to
estab-
lish causality of the mediator-to-outcome effects but did not
model
the data in ways that permitted strong causal inferences (Hoffart
et al., 2009; Hofmann, 2004; Taylor & Alden, 2008; Wilson &
Rapee, 2005). In these studies, threat reappraisal in earlier
phases
of treatment was correlated with symptom improvements later in

treatment, but testing of causality by controlling for earlier
levels of
social anxiety symptoms was absent. Only Smits et al. (2006)
demonstrated that threat reappraisal was associated with social
anxiety reduction after controlling for earlier levels of social
anxi-
ety, providing stronger support for the hypothesis that threat
reappraisal caused reduction in social anxiety. In this study,
cost
and probability biases independently accounted for variance in
fear
reduction within and between sessions, with change in
probability
bias accounting for a greater proportion of variance than change
in
cost bias. Within-session reductions in probability bias
predicted
within-session reductions in fear, which predicted further re-
ductions in probability bias (i.e., a reciprocal relation), whereas
within-session reduction in cost bias did not predict reduction
in
fear, but was a consequence of it. Although this study is the
most
methodologically rigorous examination of the threat reappraisal
mediation hypothesis to date, it has a major limitation in that
fear
ratings, but not social anxiety symptoms, were measured during
treatment. Furthermore, the cross-lagged panel analyses only
examined within-session change in a three-session treatment
protocol, so the conclusion regarding mediation is limited to
within-session processes using an abbreviated treatment period.
The authors encouraged research that applies their analytic
strat-
egy to a longer, more typical treatment protocol to provide
infor-
mation about change between treatment sessions e the aim of

the
present work.
The current project tests the threat reappraisal mediation hy-
pothesis in the context of an eight-week course of CBT for SAD
using the criteria outlined by Smits et al. (2012). Specifically
this
study examined whether or not 1) changes in judgmental biases
statistically mediated treatment outcome; 2) CBT caused threat
reappraisal, meaning individuals randomly assigned to receive
CBT
had lower threat appraisal following treatment than individuals
assigned to a waitlist control; 3) threat reappraisal caused social
anxiety symptom reduction, meaning earlier levels of
judgmental
bias predicted change in social anxiety; and 4) threat reappraisal
remained a significant mediator of treatment outcome when
modeled with a plausible rival mediator, working alliance.
Working
alliance was chosen as the rival mediator for the present study
because of its reliable, albeit modest, effect on treatment
outcome
in psychotherapy in general (approximately 8% of the total
variance
in therapy outcomes; see Horvath, Del Re, Flückiger, &
Symonds,
2011 for a review). Specifically, we hypothesize that changes in
both cost and probability estimates will mediate treatment
outcome and that judgmental biases will remain a significant
predictor of treatment outcome when modeled simultaneously
with the rival mediator, working alliance. These hypotheses
address each criterion proposed by Smits and colleagues to test
the
threat reappraisal mediation hypothesis.
We also explore the extent to which improvement in SAD is

better accounted for by changes in cost versus probability bias.
Foa
and Kozak (1985) originally theorized that inflated cost
estimates
are the primary variable mediating change in SAD because,
whereas other anxiety disorders are characterized by
overestimates
of the probability of objectively catastrophic outcomes (e.g.,
heart
attack, death of a loved one), the feared outcomes in SAD (e.g.,
appearing foolish, being embarrassed) are not objectively
dangerous. Empirical research with clinical samples has,
however,
yielded mixed findings: two studies found cost bias to be more
important (Foa et al., 1996; Rapee et al., 2009), two studies
found
probability bias to be more important (McManus, Clark, &
Hackmann, 2000; Smits et al., 2006), and two studies found
each
to be significant predictors and did not make inferences about
their
relative importance (Hoffart et al., 2009; Taylor & Alden,
2008).
Based on Foa and Kozak's original theory, we hypothesize that
re-
ductions in cost will be a stronger predictor of treatment
outcome
than reductions in probability when modeled simultaneously.
1. Method
The present study uses data from two treatment studies: a
randomized controlled trial comparing Exposure Group Therapy
(EGT; Hofmann, 2002) and Virtual Reality Exposure Therapy
(VRE;
Anderson, Zimand, Hodges, & Rothbaum, 2005) for SAD to

wait-list
controls (Anderson et al., 2013; Study 1) and an uncontrolled
trial
examining amygdala activity as a predictor of treatment
response
to VRE using functional magnetic resonance imaging (fMRI;
Study
2). For the purposes of this study, procedures in these two trials
were identical, with one exception: participants in Study 2 were
not
randomly assigned to treatment; they all received VRE.
1.1. Participants
Participants were 86 individuals who met DSM-IV-TR (APA,
2000) criteria for a primary diagnosis of generalized (n ¼ 40) or
non-generalized SAD (n ¼ 46), completed eight weeks of the
waitlist or treatment protocol, and identified public speaking as
their most feared social situation. Participants were included
only if
they identified public speaking as their most feared social
situation
because both the VRE and EGT protocols exclusively utilized
public
speaking exposures. Eligible participants on psychoactive
medica-
tion were required to be stabilized on their current
medication(s)
and dosage(s) for at least 3 months and to remain on the
stabilized
regimen throughout the course of the study. Exclusion criteria
included (a) history of mania, schizophrenia, or other
psychoses;
(b) recent prominent suicidal ideation; (c) current alcohol or
drug
abuse or dependence; (d) inability to wear the virtual reality

hel-
met; (e) history of seizures; and (f) inability to undergo an
fMRI
(e.g., claustrophobia, metallic implants) (Study 2 only).
Addition-
ally, participants were required to be literate in English.
Most participants (n ¼ 68; 79.1%) received a diagnosis of SAD
alone. The most common secondary diagnoses were specific
phobia
(2015) 139e149 141
(n ¼ 5), panic disorder without agoraphobia (n ¼ 3),
generalized
anxiety disorder, (n ¼ 3), and major depression (n ¼ 3). The
sample
consisted of 60.5% females (n ¼ 52) and 39.5% males (n ¼ 34).
Participants' ages ranged from 19 to 69 with a mean age of 39.8
(SD ¼ 11.3). Most participants self-identified as “Caucasian” (n
¼ 43;
50%) or “African American” (n ¼ 25; 29.1%). Four participants
(4.7%)
self-identified as “Hispanic,” three (3.5%), as “Asian
American,” nine
(10.5%) as “Other” (“African American/Indian/Caucasian” ¼ 1;
“Chinese” ¼ 1; “African” ¼ 1; “Biracial” ¼ 1; “Eritrean
American” ¼ 1; “Arabic” ¼ 1; “African American/Caucasian” ¼
1;
Unspecified ¼ 2), and two declined to answer. Sixty-five
percent
reported that they had completed college, 51% were married or
living with someone as though married, and 48% had an annual
income of $50,000 or greater.

1.2. Measures
1.2.1. Structured clinical interview for the DSM-IV (SCID;
First,
Gibbon, Spitzer, & Williams, 2002)
The SCID was used to determine eligibility and diagnostic
status
on Axis I conditions within the mood, substance use, and
anxiety
disorders modules. In both studies, all pretreatment diagnostic
assessments were videotaped, and a randomly selected subset
was
reviewed by a licensed psychologist to calculate the inter-rater
reliability of pretreatment assessments (100% agreement for pri-
mary diagnosis, with one disagreement on severity).
1.2.2. Brief Fear of Negative Evaluation (BFNE; Leary, 1983)
Social anxiety symptoms were measured using the BFNE, a 12-
item self-report questionnaire that measures the degree to which
individuals fear being negatively evaluated by others across a
number of social settings (e.g., “I often worry that I will say or
do
wrong things.”). Only the eight straightforwardly-worded items
were included in the scoring algorithm for the present study,
given
concerns noted in prior studies about the psychometric
properties
of the reverse-scored items (Rodebaugh et al., 2004; Weeks et
al.,
2005). Items are rated on a 5-point Likert-type scale, and scores
range from 8 to 40, with higher scores representing greater eval-
uative concerns. The BFNE has demonstrated excellent internal
consistency (Cronbach's a ¼ .97) and one-month test-retest reli-

ability (r ¼ .94) (Collins, Westra, Dozois, & Stewart, 2005).
The in-
ternal consistencies for the current study were excellent for
pretreatment (a ¼ .94), midtreatment (a ¼ .93), and
posttreatment
(a ¼ .95).
1.2.3. Outcome Probability Questionnaire (OPQ; Uren, Szab�o,
&
Lovibond, 2004)
The OPQ is a 12-item self-report questionnaire that assesses an
individual's estimate of the probability that negative socially
threatening events will occur (e.g., “You will sound dumb while
talking to others.”). As recommended by the original scale
devel-
opment paper, the 10-item version was used in the present
study.
Items are scored on a 9-point Likert-type scale with summary
scores ranging from 0 to 80. Internal consistency for the
measure
has been found to range from good to excellent (Cronbach's a ¼
.89
e .90; Uren et al., 2004). The internal consistencies for the
current
study were as follows: good for pretreatment (a ¼ .85) and
excel-
lent for midtreatment (a ¼ .92) and posttreatment (a ¼ .91).
1.2.4. Outcome Cost Questionnaire (OCQ; Uren et al., 2004)
The OCQ is a 12-item self-report questionnaire that assesses an
individual's estimate of the cost of negative social events (e.g.,
“You
sounded dumb to others.”). As recommended by the original
scale

development paper, the 10-item version was used in the present
study. Items are scored on a 9-point Likert-type scale with
summary scores ranging from 0 to 80. Internal consistency for
the
measure has been found to be consistently in the excellent range
(Cronbach's a ¼ .92 e .94; Uren et al., 2004). The internal
consis-
tencies for the current study were as follows: good for pretreat-
ment (a ¼ .85) and excellent for midtreatment (a ¼ .92) and
posttreatment (a ¼ .93).
1.2.5. Working Alliance Inventory e Short form (WAI-S; Tracey
&
Kokotovic, 1989)
The WAI-S is a 12-item instrument used to evaluate the thera-
peutic alliance. Like the original WAI (Horvath & Greenberg,
1989),
the WAI-S assesses working alliance regardless of therapeutic
orientation. Participants are asked to rate items (e.g., “My
therapist
and I are working towards mutually agreed upon goals.”) on a 7-
point Likert-type scale to best represent their feelings, with an-
swers ranging from 1 (Not at All) to 7 (Very Much). Total
scores
range from 7 to 84, with higher scores indicating a stronger alli-
ance. The WAI-S demonstrates good psychometric properties,
including content validity and internal consistency (�a ¼ .93)
(Tracey
& Kokotovic, 1989), and shows similar properties as the
original
WAI (Busseri & Tyler, 2003). The WAI was administered
following
each treatment session. The internal consistencies for the
current
study were as follows: excellent for Session 1 (a ¼ .90),

acceptable
for Session 4 (midtreatment; a ¼ .79), and good for Session 8
(posttreatment; a ¼ .88).
1.3. Procedure
Both studies were approved by a university Institutional Review
Board. Participants were self-referred or recruited through area
professionals, newspaper advertising, posted flyers, and public
service announcements. Study eligibility was determined
through a
two-part process consisting of a brief telephone screening and a
subsequent in-person pretreatment assessment. After expressing
interest and verbally consenting to complete a telephone
screening,
study candidates completed a short phone interview with a
doctoral student to determine if they met obvious exclusion
criteria
(e.g., current substance abuse in both studies, metallic implants
in
Study 2 only). Those who were not excluded during the
telephone
screening were given the opportunity to participate in an in-
person
pretreatment assessment. Written informed consent for study
procedures was obtained at the pretreatment assessment.
In Study 1, the pretreatment assessment included a structured
diagnostic clinical interview (SCID) administered by a doctoral
student, video-recorded behavioral avoidance task (10-
min speech), and completion of self-report measures. Eligible
participants were then randomly assigned to the VRE, EGT, or
WL
condition. The VRE and EGT treatment groups were designed to
be
as similar as possible. Both treatments specifically targeted

public
speaking fears via exposure therapy. Furthermore, both
treatments
sought to address specific aspects of SAD identified in the psy-
chopathology literature, including self-focused attention,
percep-
tions of self and others, perceptions of emotional control,
rumination, and realistic goal setting for social situations. In
both
treatments, probability and cost biases were specifically
targeted
and challenged via a combination of psychoeducation, cognitive
restructuring, cognitive preparation, exposure, and/or social
mishap exercises. The mechanism and setting through which
exposure was delivered varied for the two treatment groups. In-
dividual study therapists relied on the virtual environment to
facilitate exposure to public speaking fears (VRE), whereas
group
therapists relied on other group members to help facilitate expo-
sure (EGT). For participants in both treatments, elements of
expo-
sure were present as early as the pretreatment assessment,
during
which participants gave a video-recorded speech that they
(2015) 139e149142
subsequently viewed during their second treatment session.
Structured exposure for the EGT treatment condition began in
Session 2, when participants gave a speech in front of the
group,
whereas structured exposure for the VRE treatment condition
began in Session 4 or 5, when participants gave a speech in
front of

the virtual audience. Though structured exposures began in
different sessions for the two treatment conditions, the study
was
designed so that all participants, regardless of treatment
condition,
received the same total amount of exposure by the completion
of
treatment. The number of therapists and session length also
varied
across treatment conditions. In the VRE condition there was one
individual therapist, and sessions lasted for 60 min; in the EGT
condition, there were two group co-therapists, and sessions
lasted
for 120 min. See Anderson et al. (2013) for a detailed
description of
the two treatments. The WL lasted eight weeks, after which par-
ticipants completed a battery of questionnaires similar to the
bat-
tery that was administered after both the EGT and VRE
treatments.
WL participants were then re-randomized to VRE or EGT
following
the waiting period.
In Study 2, the pretreatment assessment was identical to that of
Study 1 except that it included an additional “mock” fMRI to
ensure
that participants could tolerate an actual fMRI. Following the
pre-
treatment assessment, eligible participants then underwent an
fMRI at a nearby hospital. These participants were not randomly
assigned to treatment groups; all received VRE.
Participants in both studies completed study measures at pre-,
mid-, and posttreatment. The same therapists were used in Study
1

and Study 2, and each therapist delivered both types of
treatment.
Figs. 1 and 2 were prepared in accordance with guidelines
outlined in the CONSORT (Consolidated Standards of Reporting
Trials; Altman et al., 2001) and TREND (Transparent Reporting
of
Fig. 1. CONSORT participant flow chart for study 1. EGT ¼
Exposure Grou
Evaluations with Nonrandomized Designs; Des Jarlais, Lyles, &
Crepaz, 2004) statements. The figures show the flow of partici-
pants through the two treatment studies. In Study 1, following
the
initial randomization, 26 individuals completed EGT, 25
completed
VRE, and 25 completed the WL. Following the re-
randomization of
the WL participants, an additional eight participants completed
EGT, and an additional seven completed VRE, for a total of 34
EGT
completers and 32 VRE completers in Study 1. In Study 2, all
10
participants who completed the study received VRE. Thus
combining participants from Study 1 and Study 2 who
completed
an active treatment, a total of 34 participants completed EGT,
and a
total of 42 completed VRE.
1.4. Data analytic plan
Preliminary analyses determined whether or not participants
could be collapsed across studies and, within Study 1, across
treatment conditions. Hypotheses were then evaluated in accor-
dance with the guidelines outlined by Smits et al. (2012). First,
a

multiple mediators path model assessed the indirect effect of
treatment on change in social anxiety symptoms through change
in
cost and probability biases. This analysis determined whether or
not symptom improvement was statistically mediated by re-
ductions in cost bias and probability bias (Criterion 1). This
model
was also used to examine the effect of treatment on cost bias
and
probability bias to test whether CBT caused threat reappraisal
(Criterion 2). Participants who were initially assigned to a treat-
ment condition (NTreated ¼ 61) or to the waitlist (NNot Treated
¼ 25)
were included in the analyses. Data were modeled using path
analysis, and the significance of the mediated pathway was
tested
using bootstrapping.
p Therapy; WL ¼ Wait List; VRE ¼ Virtual Reality Exposure
Therapy.
Fig. 2. TREND participant flow chart for study 2. VRE ¼
Virtual Reality Exposure Therapy.
(2015) 139e149 143
According to Smits et al. (2012), “… testing the causal effects
of
threat reappraisal on anxiety reduction requires (at a minimum)
relating previous levels of the threat appraisal to later levels of
anxiety (and vice versa; i.e., bidirectional effects)” (p. 626).
Other
scholars have written about the limitations of simple pre-post
de-
signs and the importance of establishing temporal precedence,

i.e.,
demonstrating that changes in the proposed mediator occurred
prior to changes in the dependent variable (Kazdin & Nock,
2003;
Kraemer, Wilson, Fairburn, & Agras, 2002). Therefore,
additional
analyses were conducted using treated participants only that
included the midtreatment data (See Table 1 for descriptive sta-
tistics). Fifteen participants who were re-randomized to and
completed treatment following the WL period were included in
these analyses to increase power (NTotal ¼ 76; See Fig. 1).
Associa-
tions between midtreatment levels of cost and probability biases
and posttreatment levels of social anxiety, while controlling for
previous levels of social anxiety, were examined to test the
Table 1
Descriptive statistics for the outcome and candidate and rival
mediator variables at
all time points.
Time 1
M (SD)
n
Time 2
M (SD)
n
Time 3
M (SD)
n
BFNE 27.42 (7.89) 25.34 (7.68) 21.74 (7.06)
n ¼ 76 n ¼ 74 n ¼ 73
OPQ 47.48 (16.56) 34.30 (16.78) 25.57 (15.51)

n ¼ 76 n ¼ 74 n ¼ 75
OCQ 55.88 (10.84) 43.58 (17.21) 33.24 (19.23)
n ¼ 73 n ¼ 73 n ¼ 74
WAI 74.03 (8.63) 78.01 (6.49) 79.01 (6.75)
n ¼ 65 n ¼ 62 n ¼ 64
Note: BFNE ¼ Brief Fear of Negative Evaluation; OPQ ¼
Outcome Probability Ques-
tionnaire; OCQ ¼ Outcome Cost Questionnaire; WAI ¼
Working Alliance Inventory.
BFNE, OPQ, and OCQ were collected at pretreatment,
midtreatment, and post-
treatment. WAI was collected after Session 1, Session 4, and
Session 8.
hypothesis that threat reappraisal caused social anxiety
reduction
(Criterion 3). A cross-lagged panel design path model was
employed to investigate the presumed causal interplay among
so-
cial anxiety symptoms (BFNE) and cost and probability biases
(OCQ,
OPQ) at three time points: pretreatment, midtreatment, and
post-
treatment. Cross-lagged panel designs allow for examination of
the
direct effects of one variable on another over time and of
reciprocal
relations among variables (Kessler & Greenberg, 1981; Menard,
1991). They examine the predictive association of two variables
over time, each controlling for the effects at earlier time points,
such that the effect of X1 on Y2 controlling for Y1 represents
the
effect of X1 on changes in Y over time (Finkel, 1995).

Lastly, because this study aimed to establish threat reappraisal
as a specific cognitive mediator of CBT, a plausible rival
mediator
(the nonspecific factor working alliance) was modeled simulta-
neously with threat appraisal to demonstrate specificity of the
relation between threat reappraisal and social anxiety reduction
(Criterion 4). A second cross-lagged panel design path model
was
utilized to analyze the relation between social anxiety
symptoms
and the candidate and rival mediators at three time points
throughout treatment. As working alliance was necessarily not
measured at pretreatment, the model includes measures of social
anxiety symptoms and judgmental biases at pretreatment, mid-
treatment, and posttreatment, and measures of working alliance
after Session 1, Session 4 (i.e., midtreatment), and Session 8
(i.e.,
posttreatment).
For all analyses, SEM software Mplus 7 (Muth�en & Muth�en,
1998e2012) was used to model the data. Maximum likelihood
estimation with robust standard errors (MLR) was used to test
the
fit of the hypothesized models to the observed variance-
covariance
matrix. MLR provides robust estimates of standard errors and
uses
Full Information Maximum Likelihood (FIML) to handle
missing
data. The MLR estimator was used because the assumption of
multivariate normality was not met. Specifically, the BFNE and
OPQ
at posttreatment were significant positively skewed, and the
OCQ

(2015) 139e149144
at pretreatment was significantly negatively skewed and signifi-
cantly positively kurtotic. Results of Little's MCAR test
revealed data
were missing completely at random (c2(127) ¼ 132.037, p ¼
.362),
further supporting the use of FIML.
2. Results
For Study 1, a series of ANOVAs and chi-square tests showed
no
differences in the variables of interest at pretreatment (BFNE,
OPQ,
OCQ) across the VRE, EGT, and WL conditions (p's ¼ .213 to
.830) or
demographic characteristics (SAD subtype, gender, ethnicity,
educational achievement, income, relationship status; p's ¼ .402
to
.841). Thus, random assignment successfully created three
condi-
tions that were comparable at pretreatment with regard to symp-
tom severity, judgmental biases, and demographic factors.
Participants receiving EGT reported slightly higher first-
exposure
SUDS ratings than participants receiving VRE, but this
difference
was not statistically significant (MEGT ¼ 7.4; MVRE ¼ 6.2;
t(40) ¼ �1.817, p ¼ .077). At posttreatment, there were no
differ-
ences between the EGT and VRE groups on any measure (p's ¼
.348
to .802). Thus participants in the EGT and VRE groups were
com-

bined, forming a total of two experimental groups (Treated
[EGT þ VRE], Not Treated [WL]). Independent samples t-tests
and
chi-square tests were then conducted to determine whether par-
ticipants from the uncontrolled trial (Study 2) were significantly
different in terms of symptom severity, judgmental biases, or
de-
mographics at the pretreatment assessment from participants in
the controlled trial (Study 1). There were no significant
differences
between Study 1 and Study 2 on any of the metrics listed above
(p's ¼ .254 to .969); as such, participants from Study 2 were
added
to the Treated group from Study 1 to increase sample size and
statistical power. With regard to SAD subtype, there were no
sig-
nificant between-group differences in cost bias or probability
bias
at pretreatment or working alliance at Session 1 (p's ¼ .064 to
.691);
however, participants with generalized SAD reported
significantly
higher levels of social-evaluative fears at pretreatment than par-
ticipants with non-generalized SAD (Mgeneralized ¼ 44.69;
Mnon-
generalized ¼ 36.80; t(74) ¼ �3.362, p ¼ .001).
To determine whether or not symptom improvement was sta-
tistically mediated by reductions in cost bias and probability
bias
(Criterion 1) and whether CBT caused threat reappraisal
(Criterion
2), a multiple mediators path model was tested. Residualized
gain
scores were first computed using data from pretreatment and
posttreatment to represent a measure of change in social anxiety

symptoms (BFNE) and threat appraisal (OPQ, OCQ) during
treat-
ment. Residualized gain scores control for initial symptom
severity
and measurement error associated with repeated assessment and
thus have advantages over other measures of change (Steketee
&
Chambless, 1992). Residualized gain scores were calculated by
subtracting the standardized pretreatment scores, which were
multiplied by the correlation between the standardized scores at
pretreatment and posttreatment, from the posttreatment scores.
Using this formula, lower residualized gain scores reflect
greater
reductions in symptoms. Next, a model was tested that included
paths for 1) the effect of treatment on the mediators (pre-to-post
changes in probability bias and cost bias) (i.e., Criterion 2); 2)
the
effect of the mediators on treatment outcome (pre-to-post
changes
in social anxiety symptoms), 3) correlations between the two
me-
diators, and 4) the indirect effect of treatment on treatment
outcome through pre-to-post changes in probability bias and
cost
bias (i.e., Criterion 1). The multiple mediators path model is
pre-
sented in Fig. 3. Overall, the model fit the data well: Model
c2(1) ¼ 1.652, p ¼ .199; RMSEA ¼ .081 [.000, .294]; CFI ¼
.995;
TLI ¼ .967; SRMR ¼ .024, with the exception of the upper
limit of
the RMSEA confidence interval. First, as predicted, treatment
had a
significant effect on threat appraisals; receiving treatment
compared to not receiving treatment predicted significantly
greater

reductions in both probability bias (bStdYX ¼ �.330, z ¼
�3.645,
p < .001) and cost bias (bStdYX ¼ �.320, z ¼ �3.495, p <
.001).
Second, treatment outcome was predicted by threat appraisals;
higher residualized gain scores for social anxiety were predicted
by
both higher residualized gain scores for probability bias
(bStdYX ¼ .432, z ¼ 4.433, p < .001) and cost bias (bStdYX ¼
.285,
z ¼ 2.815, p ¼ .005). Third, the mediators were significantly
posi-
tively correlated (bStdYX ¼ .625, z ¼ 10.015, p < .001); and
fourth, the
effect of treatment on treatment outcome was statistically medi-
ated by pre-to-post changes in both cost bias and probability
bias.
That is, the indirect a � b pathway was significant for the OPQ
(bStdYX ¼ �.143, z ¼ �2.780, p ¼ .005) and OCQ (bStdYX ¼
�.091,
z ¼ �2.182, p ¼ .029). Next 5000 bootstrap samples were
generated
to obtain the most accurate confidence intervals for indirect
effects
in mediation (MacKinnon, Lockwood, & Williams, 2004).
Neither
the confidence interval for the OPQ [95% CI �.490, �.081] nor
that
for the OCQ [95% CI �.381, �.027] overlapped with zero,
further
supporting the finding of statistically significant mediation.
To test whether or not threat reappraisal causes anxiety
reduction (Criterion 3), a cross-lagged panel design path model
was
employed to investigate the presumed causal interplay among

so-
cial anxiety symptoms (BFNE) and cost and probability biases
(OCQ,
OPQ) at three time points: pretreatment, midtreatment, and
post-
treatment. The model incorporates autoregressive effects that
control for temporal stability within threat appraisal and social
anxiety scores across time, synchronous correlations between
variables at each time point that account for covariances
between
variables not already explained by the influences of the
variables
from earlier time points, and cross-lagged direct effects. Thus
any
cross-lagged effects can be considered effects that add
predictive
power over and above that which can simply be obtained from
within-construct stability over time and synchronous and other
IV
effects. The cross lags between social anxiety and judgmental
bia-
ses at mid- and posttreatment are of primary importance to our
study hypothesis, as they allow for evaluation of three potential
scenarios: 1) whether earlier levels of judgmental biases
predicted
later changes in social anxiety, 2) whether earlier levels of
social
anxiety predicted later changes in judgmental biases, and 3)
whether any relations were reciprocal. Social anxiety and judg-
mental biases at pretreatment are also important for this study,
because their inclusion serves as a control for pretreatment
symptom severity, thereby providing an indicator of change
(Finkel,
1995; Rieckmann et al., 2006). For example, social anxiety at
mid-
treatment represents residualized change in social anxiety from

pretreatment to midtreatment. The cross-lagged panel design
model is presented in Fig. 4. Results indicated the fully cross-
lagged
model had acceptable fit according to all indices (Model
c2(13) ¼ 22.053, p ¼ .055; CFI ¼ .974; TLI ¼ .933; SRMR ¼
.053),
with the exception of the RMSEA (RMSEA ¼ .096 [.000, .163],
which
is slightly above conventional standards for good fit
(MacCallum,
Browne, & Sugawara, 1996). It should be noted, however, that
the
RMSEA tends to reject acceptable models when sample sizes
are
small. For this reason, some scholars argue against computing
the
RMSEA for models with low degrees of freedom (Kenny,
Kaniskan,
& McCoach, 2014). Fig. 4 also shows the standardized path co-
efficients for the model. As predicted, examination of
individual
paths revealed significant autoregressive effects and in-
tercorrelations between variables at each time point. The cross
lag
model revealed a significant effect of midtreatment OPQ on
post-
treatment BFNE (bStdYX ¼ .350, z ¼ 4.473, p < .001),
specifically
lower probability bias predicting lower social anxiety
symptoms.
However, the cross lag from midtreatment OCQ to
posttreatment
BFNE was not significant (bStdYX ¼ �.059, z ¼ �.719; p ¼
.472); nor

Fig. 3. Multiple mediators path model. BFNE ¼ Brief Fear of
Negative; OCQ ¼ Outcome Cost Questionnaire; OPQ ¼
Outcome Probability Questionnaire. Parameter estimates are
reported with standard errors in parentheses. * ¼ Parameter
estimate is significant at the .05 level; ** ¼ Parameter estimate
is significant at the .01 level; *** ¼ Parameter estimate is
significant at the .001 level.
(2015) 139e149 145
was the cross lag from midtreatment BFNE to posttreatment
OPQ
(bStdYX ¼ .038, z ¼ .419, p ¼ .675) or from midtreatment
BFNE to
posttreatment OCQ (bStdYX ¼ .049, z ¼ .364, p ¼ .716).
Findings
suggest that lower midtreatment levels of probability bias, but
not
cost bias, predicted greater reduction in social anxiety
symptoms.
That the inverse is not supported (i.e., that midtreatment BFNE
does
not predict change in OPQ) suggests the relation is not
reciprocal
and provides further evidence supporting probability bias as a
specific cognitive reappraisal mediator of CBT for SAD.1
To test the specificity of the relation between threat reappraisal
and social anxiety reduction (Criterion 4), a second cross-
lagged
panel design path model was utilized to analyze the relation be-
tween social anxiety symptoms (BFNE), probability bias (OPQ),
and
working alliance (WAI), a plausible rival mediator, at three
time

points throughout treatment. Fit indices, again with the
exception
of the upper limit of the RMSEA confidence interval, indicated
good
model fit (Model c2(14) ¼ 18.705, p ¼ .177; RMSEA ¼ .066
[.000,
.138]; CFI ¼ .984; TLI ¼ .963; SRMR ¼ .037). Fig. 5 shows the
standardized path coefficients for the model. Examination of
indi-
vidual paths revealed significant autoregressive effects between
variables at each time point. However, working alliance was not
significantly correlated with probability bias or with social
anxiety
symptoms at any time point. The cross lag model again revealed
a
significant effect of midtreatment OPQ on posttreatment BFNE
(bStdYX ¼ .352, z ¼ 3.480, p ¼ .001), whereas the cross lag
from
midtreatment WAI to posttreatment BFNE was not significant
(bStdYX ¼ �.050, z ¼ �.657; p ¼ .511). Findings suggest that
earlier
levels of probability bias, but not working alliance, predicted
later
change in social anxiety symptoms. These findings provide
further
support for threat reappraisal, specifically probability bias, as a
mediator of CBT for SAD by demonstrating specificity of the
threat
reappraisal-social anxiety reduction relation.
1 Models with parameters allowed to vary across treatment
group (VRE or EGT)
showed that the path from probability bias at midtreatment to
social anxiety at
posttreatment was significant for both the VRE and EGT
groups, the path from cost
bias at midtreatment to social anxiety at posttreatment was not

significant for
either the VRE or the EGT groups, and the model with these
paths constrained to be
equal across treatment groups fit the data better than models
with these paths free
to vary across treatment groups. These findings indicate that,
irrespective of
treatment group, the OPQ at midtreatment was a significant
predictor of treatment
outcome, but the OCQ at midtreatment was not. Full results of
these analyses are
available upon request.
3. Discussion
CBT is theorized to exert its therapeutic effect on SAD by
reducing judgmental biases concerning the probability and cost
of
negative social events (i.e., the threat reappraisal mediation hy-
pothesis; Foa & Kozak, 1986; Clark & Wells, 1995; Rapee &
Heimberg, 1997). Many empirical studies have yielded findings
consistent with the threat reappraisal mediation hypothesis but
failed to evaluate and/or demonstrate criteria critical to
establish-
ing mediation, leading to calls for research to test threat
reappraisal
as a mechanism of CBT using more modern statistical methods
(Hofmann, 2008; Smits et al., 2012). The present study tested
the
threat reappraisal hypothesis by evaluating cost and probability
biases as mediators of CBT for SAD in accordance with the
recent
recommendations of Smits et al. (2012). As hypothesized, the
effect
of CBT on pre-to-post changes in social anxiety symptoms was
statistically mediated by pre-to-post changes in judgmental
biases,

and participants who received CBT had significantly greater re-
ductions in judgmental biases than participants who did not
receive treatment. Tests aimed at examining the extent to which
threat reappraisal caused social anxiety reduction support
proba-
bility bias as a significant predictor of treatment outcome when
it is
modeled simultaneously with cost bias, as well as when it is
modeled simultaneously with a plausible rival mediator. Cost
bias,
however, was not a significant predictor of treatment outcome
when modeled simultaneously with probability bias. Taken
together, the results of this study broadly support the threat
reappraisal mediation hypothesis: reductions in probability bias
met all the criteria critical to establishing mediation.
Our methodological approach has a number of strengths.
Examining cost and probability biases and their relation to
social
anxiety symptoms at pre-, mid-, and posttreatment allowed for a
finer analysis than was possible in prior studies that assessed
relevant variables only before and after treatment. Our approach
also addressed the issue of temporal precedence by allowing for
tests of whether earlier levels of probability and cost biases pre-
dicted later change in social anxiety (or vice versa) or whether
the
relation was reciprocal. The cross-lagged panel design
permitted a
more conservative examination of the specific contributions of
judgmental biases to social anxiety symptom reduction by con-
trolling for autoregressive effects and isolating the effect from
cross-variable correlations, which could have inflated effect
size
estimates. Furthermore, examining both cost and probability as
predictors simultaneously allowed us to address a key topic of

Fig. 4. Cross-lagged panel design path diagram relating the
BFNE, OCQ, and OPQ. BFNE ¼ Brief Fear of Negative; OCQ ¼
Outcome Cost Questionnaire; OPQ ¼ Outcome Probability
Questionnaire. Standardized parameter estimates are reported
with standard errors in parentheses. * ¼ Parameter estimate is
significant at the .05 level; ** ¼ Parameter estimate is
significant at the .01 level; *** ¼ Parameter estimate is
(2015) 139e149146
debate in the literature by comparing the contributions of each
to
treatment outcome and ascertaining that one (probability bias)
was
more influential than the other. Finally, this study is the first to
examine working alliance as a candidate mediator in the threat
reappraisal-social anxiety reduction relation and the only study
to
test the threat reappraisal mediation hypothesis using all the
criteria recommended by Smits et al. (2012).
Fig. 5. Cross-lagged panel design path diagram relating the
BFNE, OPQ and WAI. BFNE ¼ Brie
Inventory. Standardized parameter estimates are reported with
standard errors in parenth
significant at the .01 level; *** ¼ Parameter estimate is
Our finding that cost bias at midtreatment was not a significant
predictor of treatment outcome is consistent with two of the six
previous empirical investigations that examined both cost and
probability biases as predictors or mediators of treatment
outcome
(McManus et al., 2000; Smits et al., 2006). The consistency
with the

Smits et al. (2006) findings is especially noteworthy, given that
the
authors used similarly robust mediation analyses and different
f Fear of Negative; OPQ ¼ Outcome Probability Questionnaire;
WAI ¼ Working Alliance
eses. * ¼ Parameter estimate is significant at the .05 level; ** ¼
Parameter estimate is
(2015) 139e149 147
measures of anxiety and judgmental biases. As ours is the first
and
only study to test the threat reappraisal mediation hypothesis
using
the most recently developed guidelines, it is premature to
conclude
that probability bias is a treatment mechanism and cost bias is
not.
Clearly additional research is needed to replicate these findings.
However, the difference between probability and cost bias as a
predictor of treatment outcome in the present study was quite
pronounced. One might speculate that reductions in probability
bias may be especially important for successful treatment of
public
speaking fears (as compared to other types of social fears),
because
both the present study and the Smits et al. study specifically tar-
geted public speaking fears. However, McManus et al. (2000)
sample was not restricted to participants with substantial public
speaking fears. Thus it seems unlikely that this finding is an
artifact
of the type of social fear targeted by treatment.
Alternate explanations for this finding raise questions about

previously held assumptions about the primacy of cost bias over
probability bias in SAD. First, this finding is inconsistent with
the
idea that inflated estimates of the likelihood of negative social
events should only cause anxiety if the anticipated negative
social
events are also considered to be aversive or to have bad conse-
quences (Foa & Kozak, 1985). Instead, it suggests that simply
anticipating negative social events may be sufficient to provoke
anxiety, regardless of how severe the events' negative conse-
quences are expected to be. This interpretation suggests that
peo-
ple with social anxiety may have low thresholds for tolerating
negative social events that they judge as relatively benigndin
other
words, even events that carry the mildest negative consequences
may be unacceptably anxiety-provoking.
Second, cost judgments may only be relevant for events that are
deemed probable. In other words, if an event is perceived as
costly
but highly unlikely, it may not be appraised as anxiety-
provoking.
Even individuals with strong convictions that negative social
events are costly may not experience anxiety unless they
perceive
those events as imminent. Research efforts to clarify the
boundaries
of probability and cost biases may be helpful for testing this
pos-
sibility; for example, it could be useful to establish whether
each
type of bias has a different threshold at which it becomes likely
to
trigger anxiety.

Both of these explanations suggest, at a minimum, that a closer
look at distinctions and overlaps between probability and cost
biases is warranted. Further, the results of the present study
should
encourage an increase in the amount of research attention dedi-
cated specifically to understanding probability bias as a
treatment
mechanism and to developing probability-specific interventions.
A
randomized controlled trial in which a treatment enhanced with
probability-focused interventions is shown to be more
efficacious
than the original non-enhanced treatment would be the next step
toward confirming probability bias as a mechanism of treatment
of
CBT for SAD. In the meantime, practicing clinicians should
continue
to routinely assess and target both probability and cost biases in
treatment, as the literature suggests that both may function as
treatment mechanisms and elements of both are needed to create
threat appraisals. That is, without the perception of negative
con-
sequences, an extremely likely event would not create fear, and
without the possibility (likelihood) of an event occurring, a
cata-
strophic consequence would not create fear.
This recommendation is in line that of researchers (e.g., Antony
& Swinson, 2008; Wells, 1997) who advocate for the use of
behavioral experiments in which, following exposures that test
the
probability of negative social events (in which patients ideally
learn
that feared outcomes are not as probable as they had
anticipated),
patients should complete exercises in which they purposely

commit social errors. For example, if an individual fears her
hand
will tremble during a presentation, she could first complete an
exposure in which she attempts to give her presentation as
competently as possible, followed by an additional exposure in
which she purposely allows her hand to tremble. The dual
nature of
this approach allows individuals to learn that, not only are
feared
outcomes unlikely, even if such outcomes do occur, the conse-
quences are not as catastrophic as imagined.
Though our findings support the centrality of threat reappraisal
to treatment outcome, our study design does not allow for con-
clusions about which specific components of treatment led to
re-
ductions in cost and probability biases. As such, our findings do
not
directly contradict dismantling studies that have challenged the
utility of explicit cognitive intervention (e.g., Fedoroff &
Taylor,
2001; Feske & Chambless, 1995; Gould, Buckminster, Pollack,
Otto, & Yap, 1997; Powers, Sigmarsson, & Emmelkamp, 2008).
Hofmann and Otto (2008) argued that cost exposures are “the
single most effective strategy to target probability and cost esti-
mates” (p. 110), an assertion supported by the only study that
experimentally compared cost- and probability-focused
treatment
interventions (Nelson, Deacon, Lickel, & Sy, 2010). Thus a goal
for
future research is to determine how cost- and probability-
focused
interventions exert their effects. Research comparing
probability-
and cost-focused cognitive interventions (e.g., cognitive
restruc-

turing, Socratic questioning, ABC worksheets) and probability-
and
cost-focused exposure exercises (e.g., traditional exposure,
social
mishap exercises) may be illuminative.
There are several limitations to the present study, first and
foremost of which is the use of pre-to-post residualized change
scores in the statistical mediation analyses. Much has been
written
about the limitations of cross-sectional mediational analyses in
treatment outcome studies (Kazdin & Nock, 2003; Kraemer et
al.,
2002). In the present study, the formal test of statistical media-
tion utilized a simple pre/post design because midtreatment data
were only available from participants who completed either of
the
two treatments in the study (i.e., not from the waitlist control
group). As such, the analyses of the present study deviate from
Kraemer et al.'s (2002) recommendations for testing
mechanisms
of action in randomized controlled trials. Our findings were,
how-
ever, consistent with statistical mediation, and the midtreatment
data used in a later analytic step allowed us to assess the tempo-
rality of the relations between judgmental biases and social
anxiety.
Second, because CBT was compared to a waitlist control and
not
an active treatment control, it is not possible to conclude that
the
reductions in threat appraisal and social anxiety found in the
Treated group were caused by components of CBT. Rather, it is
possible that something that is part of CBT but not its putative
active ingredient (e.g., a non-specific factor, such as therapist

con-
tact) caused the changes seen in the present study. However, our
choice for the plausible rival mediator was strategic in that it
allowed us to rule out the nonspecific factor of working
alliance.
Additionally, our primary outcome measure, the BFNE, tests a
relatively specific, albeit core, feature of SAD (fear of negative
evaluation), and thus may not capture the full range of social
anxiety symptoms (e.g., avoidance). Use of a social anxiety
com-
posite score would rectify this situation, but it was not possible
to
calculate such a composite in the present study due to the
schedule
on which measures were administered.
Lastly, all participants identified public speaking as their most
feared social situation. Thus, future research is needed to
ascertain
whether the relations between threat reappraisal and social anxi-
ety observed in the present study generalize across participants
with a wider range of social performance and interaction fears.
For
example, perceived likelihood and/or costs of negative social
events
associated with public speaking may be higher across the board
than are those associated with social events that involve fewer
(2015) 139e149148
participants or that are more private. Such differences in
variability
could, in turn, drive differences in patterns of association.

In conclusion, this study responds to the call for increased
methodological rigor in investigation of cognitive mediators of
CBT
by adding to our knowledge about how changes in judgmental
biases and social anxiety symptoms unfold over time. Our
design
and analytic approach used a more conservative test of changes
in
judgmental biases as mechanisms by which CBT results in
reduced
social anxiety symptoms. Our findings contribute to our under-
standing of why treatment works and encourage research into
enhanced probability-focused interventions for improving the
ef-
ficacy of CBT for SAD.
Acknowledgments
This project was supported by a National Institutes of Mental
Health grant (P50 MH 68036) awarded to the last author and an
Anxiety Disorders Association of American grant (H07532)
awar-
ded to the third and last authors.
References
Altman, D. G., Schulz, K. F., Moher, D., Egger, M., Davidoff,
F., Elbourne, D., et al.
(2001). The revised CONSORT statement for reporting
randomized trials:
explanation and elaboration. Annals of Internal Medicine, 134,
663e694. http://
dx.doi.org/10.7326/0003-4819-134-8-200104170-00012.
Anderson, P. L., Price, M., Edwards, S. M., Obasaju, M. A.,

Schmertz, S. K., Zimand, E.,
et al. (2013). Virtual reality exposure therapy for social anxiety
disorder: a
randomized clinical trial. Journal of Consulting and Clinical
Psychology, 81,
751e760. http://dx.doi.org/10.1037/a0033559.
Anderson, P. L., Zimand, E., Hodges, L. F., & Rothbaum, B. O.
(2005). Cognitive
behavioral therapy for public-speaking anxiety using virtual
reality for expo-
sure. Depression and Anxiety, 22, 156e158.
http://dx.doi.org/10.1002/da.20090.
Antony, M. M., & Swinson, R. P. (2008). The shyness and
social anxiety workbook:
Proven, step-by-step techniques for overcoming your fear (2nd
ed.). Oakland, CA:
New Harbinger Publications.
APA. (2000). Diagnostic and statistical manual of mental
disorders: DSM-IV-TR.
Washington, DC: American Psychiatric Publishing, Inc.
Busseri, M. A., & Tyler, J. D. (2003). Interchangeability of the
working alliance in-
ventory and working alliance inventory, short form.
Psychological Assessment,
15, 193e197. http://dx.doi.org/10.1037/1040-3590.15.2.193.
Clark, D. M., & Wells, A. (1995). A cognitive model of social
phobia. In
R. G. Heimberg, M. R. Liebowitz, D. A. Hope, & F. R. Schneier
(Eds.), Social phobia:
Diagnosis, assessment, and treatment (pp. 69e93). New York:
Guilford Press.

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, 345e359. http://dx.doi.org/10.1016/j.janxdis.2004.02.003.
Des Jarlais, D. C., Lyles, C., & Crepaz, N. (2004). Improving
the reporting quality of
nonrandomized evaluations of behavioral and public health
interventions: the
TREND statement. American Journal of Public Health, 94,
361e366. http://
dx.doi.org/10.2105/ajph.94.3.361.
Fedoroff, I. C., & Taylor, S. (2001). Psychological and
pharmacological treatments of
social phobia: a meta-analysis. Journal of Clinical
Psychopharmacology, 21,
311e324. http://dx.doi.org/10.1097/00004714-200106000-
00011.
Feske, U., & Chambless, D. L. (1995). Cognitive behavioral
versus exposure only
treatment for social phobia: a meta-analysis. Behavior Therapy,
26, 695e720.
http://dx.doi.org/10.1016/s0005-7894(05)80040-1.
Finkel, S. E. (1995). Causal analysis with panel data. Thousand
Oaks, CA, US: Sage
Publications, Inc.
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W.
(2002). Structured clinical
interview for DSM-IV-TR axis I disorders, research version,
patient edition (SCID-I/

P). New York, NY: American Psychiatric Publishing
Incorporated.
Foa, E. B., Franklin, M. E., Perry, K. J., & Herbert, J. D.
(1996). Cognitive biases in
generalized social phobia. Journal of Abnormal Psychology,
105, 433e439. http://
dx.doi.org/10.1037/0021-843x.105.3.433.
Foa, E. B., & Kozak, M. J. (1985). Treatment of anxiety
disorders: Implications for
psychopathology. In A. H. Tuma, & J. D. Maser (Eds.), Anxiety
and the anxiety
disorders (pp. 421e452). Hillsdale, NJ: Erlbaum.
Foa, E. B., & Kozak, M. J. (1986). Emotional processing of
fear: exposure to corrective
information. Psychological Bulletin, 99, 20e35.
http://dx.doi.org/10.1037/0033-
2909.99.1.20.
Gould, R. A., Buckminster, S., Pollack, M. H., Otto, M. W., &
Yap, L. (1997). Cognitive-
behavioral and pharmacological treatment for social phobia: a
meta-analysis.
Clinical Psychology: Science and Practice, 4, 291e306.
http://dx.doi.org/10.1111/
j.1468-2850.1997.tb00123.x.
Hoffart, A., Borge, F., Sexton, H., & Clark, D. M. (2009).
Change processes in resi-
dential cognitive and interpersonal psychotherapy for social
phobia: a process-
outcome study. Behavior Therapy, 40, 10e22.
j.beth.2007.12.003.

Hofmann, S. G. (2002). Exposure group therapy treatment
manual. Boston, MA:
Center for Anxiety and Related Disorders, Boston University.
Unpublished
manuscript.
Hofmann, S. G. (2004). Cognitive mediation of treatment
change in social phobia.
Journal of Consulting and Clinical Psychology, 72, 392e399.
http://dx.doi.org/
10.1037/0022-006x.72.3.392.
Hofmann, S. G. (2008). Common misconceptions about
cognitive mediation of
treatment change: a commentary to Longmore and Worrell
(2007). Clinical
Psychology Review, 28, 67e70.
http://dx.doi.org/10.1016/j.cpr.2007.03.003.
Hofmann, S. G., & Otto, M. W. (2008). Cognitive behavioral
therapy for social anxiety
disorder. New York: Routledge.
Horvath, A. O., Del Re, A. C., Flückiger, C., & Symonds, D.
(2011). Alliance in indi-
vidual psychotherapy. Psychotherapy, 48, 9e16.
a0022186.
Horvath, A. O., & Greenberg, L. S. (1989). Development and
validation of the
working alliance Inventory. Journal of Counseling Psychology,
36, 223e233.
http://dx.doi.org/10.1037/0022-0167.36.2.223.

Kazdin, A. E., & Nock, M. K. (2003). Delineating mechanisms
of change in child and
adolescent therapy: methodological issues and research
recommendations.
Journal of Child Psychology and Psychiatry, 44, 1116e1129.
10.1111/1469-7610.00195.
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2014). The
performance of RMSEA in
models with small degrees of freedom. Sociological Methods &
Research. http://
dx.doi.org/10.1177/0049124114543236. Advance online
publication.
Kessler, R. C., & Greenberg, D. F. (1981). Linear panel
analysis: Models of quantitative
change. Academic Press.
Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S.
(2002). Mediators and
moderators of treatment effects in randomized clinical trials.
Archives of General
Psychiatry, 59, 877e883.
http://dx.doi.org/10.1001/archpsyc.59.10.877.
Leary, M. R. (1983). A brief version of the Fear of Negative
Evaluation Scale. Per-
sonality and Social Psychology Bulletin, 9, 371e375.
0146167283093007.
Longmore, R. J., & Worrell, M. (2007). Do we need to
challenge thoughts in cognitive
behavior therapy? Clinical Psychology Review, 27, 173e187.

10.1016/j.cpr.2006.08.001.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996).
Power analysis and
determination of sample size for covariance structure modeling.
Psychological
Methods, 1, 130e149. http://dx.doi.org/10.1037/1082-
989x.1.2.130.
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004).
Confidence limits for the
indirect effect: distribution of the product and resampling
methods. Multivar-
iate Behavioral Research, 39, 99e128. http://dx.doi.org/10.1207/
s15327906mbr3901_4.
McManus, F., Clark, D. M., & Hackmann, A. (2000).
Specificity of cognitive biases in
social phobia and their role in recovery. Behavioural and
Cognitive Psychother-
apy, 28, 201e209.
http://dx.doi.org/10.1017/s1352465800003015.
Menard, S. (1991). Longitudinal research. Newbury Park, CA
US: Sage Publications.
Muth�en, L. K., & Muth�en, B. O. (1998e2012). Mplus user's
guide (7th ed.). Los
Angeles, CA: Muth�en & Muth�en.
Nelson, E. A., Deacon, B. J., Lickel, J. J., & Sy, J. T. (2010).
Targeting the probability
versus cost of feared outcomes in public speaking anxiety.
Behaviour Research
and Therapy, 48, 282e289.
http://dx.doi.org/10.1016/j.brat.2009.11.007.

Powers, M. B., Sigmarsson, S. R., & Emmelkamp, P. M. G.
(2008). A meta-analytic
review of psychological treatments for social anxiety disorder.
International
Journal of Cognitive Therapy, 1, 94e113.
http://dx.doi.org/10.1521/ijct.2008.1.2.94.
Rapee, R. M., Gaston, J. E., & Abbott, M. J. (2009). Testing the
efficacy of theoretically
derived improvements in the treatment of social phobia. Journal
of Consulting
and Clinical Psychology, 77, 317e327.
http://dx.doi.org/10.1037/a0014800.
Rapee, R. M., & Heimberg, R. G. (1997). A cognitive-
behavioral model of anxiety in
social phobia. Behaviour Research and Therapy, 35, 741e756.
10.1016/s0005-7967(97)00022-3.
Rieckmann, N., Gerin, W., Kronish, I. M., Burg, M. M.,
Chaplin, W. F., Kong, G., et al.
(2006). Course of depressive symptoms and medication
adherence after acute
coronary syndromes: an electronic medication monitoring study.
Journal of the
American College of Cardiology, 48, 2218e2222.
j.jacc2006.07.063.
Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R.
G., Chambless, D. L., &
Rapee, R. M. (2004). More information from fewer questions:
the factor
structure and item properties of the original and brief fear of

negative evalu-
ation scale. Psychological Assessment, 16, 169e181.
1040-3590.16.2.169.
Smits, J. A. J., Julian, K., Rosenfield, D., & Powers, M. B.
(2012). Threat reappraisal as a
mediator of symptom change in cognitive-behavioral treatment
of anxiety
disorders: a systematic review. Journal of Consulting and
Clinical Psychology, 80,
624e635. http://dx.doi.org/10.1037/a0028957.
Smits, J. A. J., Rosenfield, D., McDonald, R., & Telch, M. J.
(2006). Cognitive mecha-
nisms of social anxiety reduction: an examination of specificity
and tempo-
rality. Journal of Consulting and Clinical Psychology, 74,
1203e1212. http://
dx.doi.org/10.1037/0022-006x.74.6.1203.
Steketee, G., & Chambless, D. L. (1992). Methodological issues
in prediction of
treatment outcome. Clinical Psychology Review, 12, 387e400.
10.1016/0272-7358(92)90123-P.
Taylor, C. T., & Alden, L. E. (2008). Self-related and
interpersonal judgment biases in
social anxiety disorder: changes during treatment and
relationship to outcome.
International Journal of Cognitive Therapy, 1, 125e137.
http://dx.doi.org/10.7326/0003-4819-134-8-200104170-00012
http://dx.doi.org/10.7326/0003-4819-134-8-200104170-00012

http://dx.doi.org/10.1037/a0033559
http://dx.doi.org/10.1002/da.20090
http://refhub.elsevier.com/S0005-7967(15)00105-9/sref4
http://dx.doi.org/10.1037/1040-3590.15.2.193
http://dx.doi.org/10.1016/j.janxdis.2004.02.003
http://dx.doi.org/10.2105/ajph.94.3.361
http://dx.doi.org/10.2105/ajph.94.3.361
http://dx.doi.org/10.1097/00004714-200106000-00011
http://dx.doi.org/10.1016/s0005-7894(05)80040-1
http://dx.doi.org/10.1037/0021-843x.105.3.433
http://dx.doi.org/10.1111/j.1468-2850.1997.tb00123.x
http://dx.doi.org/10.1111/j.1468-2850.1997.tb00123.x
http://dx.doi.org/10.1016/j.beth.2007.12.003
http://dx.doi.org/10.1016/j.beth.2007.12.003

http://dx.doi.org/10.1016/j.cpr.2007.03.003
http://dx.doi.org/10.1111/1469-7610.00195
http://dx.doi.org/10.1111/1469-7610.00195
http://dx.doi.org/10.1177/0049124114543236
http://dx.doi.org/10.1001/archpsyc.59.10.877
http://dx.doi.org/10.1207/s15327906mbr3901_4
http://dx.doi.org/10.1207/s15327906mbr3901_4
http://dx.doi.org/10.1017/s1352465800003015
http://dx.doi.org/10.1521/ijct.2008.1.2.94

http://dx.doi.org/10.1016/j.jacc2006.07.063
http://dx.doi.org/10.1016/j.jacc2006.07.063
http://dx.doi.org/10.1016/0272-7358(92)90123-P
http://dx.doi.org/10.1016/0272-7358(92)90123-P
(2015) 139e149 149
ijct.2008.1.2.125.
Tracey, T. J., & Kokotovic, A. M. (1989). Factor structure of
the working alliance in-
ventory. Psychological Assessment: A Journal of Consulting
and Clinical Psychology,
1, 207e210. http://dx.doi.org/10.1037/1040-3590.1.3.207.
Uren, T. H., Szab�o, M., & Lovibond, P. F. (2004). Probability
and cost estimates for
social and physical outcomes in social phobia and panic
disorder. Journal of
Anxiety Disorders, 18, 481e498.
http://dx.doi.org/10.1016/s0887-6185(03)
00028-8.
Weeks, J. W., Heimberg, R. G., Fresco, D. M., Hart, T. A.,
Turk, C. L., Schneier, F. R., et al.
(2005). Empirical validation and psychometric evaluation of the
brief fear of

negative evaluation scale in patients with social anxiety
disorder. Psychological
Assessment, 17, 179e190. http://dx.doi.org/10.1037/1040-
3590.17.2.179.
Wells, A. (1997). Cognitive therapy of anxiety disorders: A
practical guide. West Sus-
sex, England: John Wiley & Sons, Limited.
Wilson, J. K., & Rapee, R. M. (2005). The interpretation of
negative social events in
social phobia: changes during treatment and relationship to
outcome. Behav-
iour Research and Therapy, 43, 373e389.
j.brat.2004.02.006.
http://dx.doi.org/10.1016/j.brat.2004.02.006Evaluating changes
in judgmental biases as mechanisms of cognitive-behavioral
therapy for social anxiety disorder1. Method1.1.
Participants1.2. Measures1.2.1. Structured clinical interview for
the DSM-IV (SCID; First, Gibbon, Spitzer, & Williams,
2002)1.2.2. Brief Fear of Negative Evaluation (BFNE; Leary,
1983)1.2.3. Outcome Probability Questionnaire (OPQ; Uren,
Szabó, & Lovibond, 2004)1.2.4. Outcome Cost Questionnaire
(OCQ; Uren et al., 2004)1.2.5. Working Alliance Inventory –
Short form (WAI-S; Tracey & Kokotovic, 1989)1.3.

Procedure1.4. Data analytic plan2. Results3.
DiscussionAcknowledgmentsReferences

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