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Interpreting Neutral Faces as Threatening Is a Default Mode for Socially Anxious Individuals


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The authors of the present study used an incidental learning paradigm to investigate the interpretation of neutral facial expressions in socially anxious individuals. Participants were asked to detect the location of a target following the presentation of a facial picture (i.e., cue). Unbeknownst to participants, the target location was contingent on the valence of the cue, and participants thus learned to associate different target locations with either positive or negative facial expressions. The authors subsequently used this learned association to assess interpretive biases. If socially anxious individuals interpret neutral faces in a negative manner, they should be faster to detect a target that appears in the location that is associated with negative face cues when the target is presented after a neutral face cue. The authors also assessed whether the anticipation of a feared situation influenced interpretive biases by comparing participants with and without a speech threat on this task. Results indicate that socially anxious individuals are characterized by an interpretive bias regardless of the threat manipulation. In contrast, nonanxious individuals interpreted neutral faces in a negative manner only when they were in the threat condition.
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Interpreting Neutral Faces as Threatening Is a Default Mode for Socially
Anxious Individuals
K. Lira Yoon and Richard E. Zinbarg
Northwestern University
The authors of the present study used an incidental learning paradigm to investigate the interpretation of
neutral facial expressions in socially anxious individuals. Participants were asked to detect the location
of a target following the presentation of a facial picture (i.e., cue). Unbeknownst to participants, the target
location was contingent on the valence of the cue, and participants thus learned to associate different
target locations with either positive or negative facial expressions. The authors subsequently used this
learned association to assess interpretive biases. If socially anxious individuals interpret neutral faces in
a negative manner, they should be faster to detect a target that appears in the location that is associated
with negative face cues when the target is presented after a neutral face cue. The authors also assessed
whether the anticipation of a feared situation influenced interpretive biases by comparing participants
with and without a speech threat on this task. Results indicate that socially anxious individuals are
characterized by an interpretive bias regardless of the threat manipulation. In contrast, nonanxious
individuals interpreted neutral faces in a negative manner only when they were in the threat condition.
Keywords: social anxiety; interpretive bias; face
As the basic fear in social anxiety (SA) is that of receiving
negative evaluation, which is often conveyed by and inferred
from facial expressions, interpretations of ambiguous facial
expressions might be particularly relevant for understanding SA
(Philippot & Douilliez, 2005). Evidence for a facial interpretive
bias in socially anxious individuals, however, is weak at best.
Although a few studies have reported that socially anxious
individuals interpret neutral faces in a negative manner (e.g.,
Lundh & O
¨st, 1996a), others have failed to do so (e.g., Lundh
¨st, 1996b). Even more problematic, the studies reporting
significant findings are mostly limited by their sole reliance on
self-reports. It is, thus, possible that the results from previous
studies might reflect socially anxious individuals’ tendency to
select negative response options (i.e., response bias).
We employed incidental learning methodology (e.g., Buchner
& Wippich, 1998; Lewicki, 1986) to compare high-SA individ-
uals with low-SA individuals in their interpretations of pictures
of ambiguous facial expressions. As participants were not asked
to direct their attention to the valence of the stimuli or to engage
in conscious categorization of stimuli in terms of valence, we
were able to study interpretive bias that could not be readily
explained by response bias or demand.
The participants’ task was to locate a target and then to press
a button corresponding to its location as soon as possible.
Unbeknownst to the participants, there was a relationship be-
tween the valence of a face stimulus (i.e., cue) and the target
location that participants were exposed to during the learning
phase. In the learning phase, negative and positive faces served
as cues. If learning occurred, participants should more rapidly
detect the target appearing at the expected location according to
the pattern presented in the learning phase. For the critical trials
in the testing phase, neutral faces—inherently ambiguous as to
the emotional state of the actor—appeared as cues.
It is important to note that threat manipulations often interact
with individual differences in trait anxiety in regard to cognitive
biases (e.g., Mathews & MacLeod, 1994). The results are
inconsistent with some studies that have suggested threat ma-
nipulations enhance biases (e.g., Calvo & Castillo, 1997;
Mansell, Clark, & Ehlers, 2003), whereas other studies have
suggested that threat manipulations suppress biases in the anx-
ious populations (e.g., Amir et al., 1996; Mathews & Sebastian,
1993). It thus seems important to examine the effects of threat
manipulation on interpretive bias. Therefore, half the partici-
pants were assigned to a speech condition, and half were
assigned to a no-speech condition.
On the basis of cognitive models of social phobia (Clark &
Wells, 1995; Rapee & Heimberg, 1997), we hypothesized that
socially anxious individuals would interpret neutral faces in a
negative manner (i.e., they would show relative facilitation in
target detection when the target appears at the location that they
learned to associate with the negative faces following a neutral
face cue).
K. Lira Yoon, Department of Psychology, Northwestern University;
Richard E. Zinbarg, Department of Psychology, Northwestern University;
and The Family Institute, Northwestern University.
Correspondence concerning this article should be addressed to K. Lira
Yoon, who is now at the Department of Psychology, University of Miami,
P.O. Box 248185, Coral Gables, FL 33124-0751. E-mail:
Journal of Abnormal Psychology Copyright 2008 by the American Psychological Association
2008, Vol. 117, No. 3, 680– 685 0021-843X/08/$12.00 DOI: 10.1037/0021-843X.117.3.680
One hundred sixteen introductory psychology students com-
pleted the Social Phobia Scale (SPS; Mattick & Clarke, 1998)
during an initial group testing session. Sixty students who scored
in the top (SPS 18) or bottom (SPS 7) 25th percentile on the
SPS were then chosen to participate in the study as part of a course
requirement. Due to technical failure, 11 participants were unable
to complete the incidental learning task. In addition, 2 participants
had at least one missing cell and thus were excluded from analyses.
The loss of data was spread evenly across SA level (i.e., 7
participants in the low-SA group and 6 in the high-SA group). The
final sample consisted of 47 participants (35 female), with 23 (17
female) low-SA (SPS, M5.91, SD 3.73) and 24 (18 female)
high-SA participants (SPS, M28.75, SD 8.36). About two
thirds of our high-SA participants met or exceeded the score of 26
suggested by Peters (2000) to identify those with social anxiety
Symptom measures. The SPS assesses a participant’s typical
levels of fear of scrutiny when performing a task or being observed
by others and has adequate reliability and concurrent validity.
Participants also completed the Beck Depression Inventory (BDI;
Beck, Ward, Mendelson, Mock, & Erbaugh, 1961), which has
adequate reliability and has been extensively validated (e.g., Beck,
Steer, & Garbin, 1988). Alpha in the current study equaled .93 for
the SPS and .87 for the BDI. Participants’ state anxiety level was
measured by a single item (“How anxious do you feel right now?”)
ona0(not at all)to9(extremely) scale on the computer.
Face stimuli. Angry, disgusted, happy, and neutral faces were
obtained from Ekman and Friesen’s (1976) Pictures of Facial
Affect. Fourteen models (8 female, 6 male) posed for each facial
affect, resulting in a total of 56 different facial stimuli. Eight
neutral pictures appeared once, and the other 6 appeared twice; 6
happy pictures appeared eight times, and the other 8 appeared nine
times; 10 angry pictures and 10 pictures showing disgust appeared
four times, and the remaining 4 angry and 4 disgusted pictures
appeared five times.
Incidental Learning Task
The task consisted of a learning phase and a testing phase. Each
trial consisted of a 500-ms fixation point, a facial picture (cue)
presented for 675 ms that was followed immediately by a target
(letter L), and a question that asked the participant whether the
person in the picture was a man or a woman to ensure attention to
the stimuli.
During the learning phase, the cue was either negative (i.e., an
angry or a disgusted face) or positive (i.e., a happy face). Once the
cue disappeared, the target appeared at any of four premarked
positions on the computer screen (see Figure 1). When the cue was
negative, the target appeared at either the top or the bottom two
locations (i.e., negative location) on 80% of the trials. The side
opposite to the negative location was the positive location. For
example, for some participants when the cue was negative, the
target appeared at the upper-left location 40% of the time, at the
upper-right location 40% of the time, and at each of the two lower
locations 10% of the time. For these participants, when the cue was
positive, the target appeared at the bottom-left 40% of the time and
at the bottom-right 40% of the time. Whether the top or the bottom
was designated as the negative location was determined randomly
for each of the participants. For the final sample, there was no
difference in the number of participants from each group who
received the top as the negative location.
Participants were instructed to respond to the appearance of the
target as quickly as possible by pressing one of four keys that
corresponded to the locations of the target. We measured response
latency as the time interval between the onset of the target display
and the response. Each participant was presented with 10 blocks of
learning trials, with each block consisting of 20 trials (i.e., 10 trials
with negative cues—five angry and five disgusted—and 10 trials
with positive cues) in a different randomized order.
During the testing phase, neutral face cues appeared in addition
to positive and negative face cues. The relationship between the
valence of the cue and the target location during the testing phase
remained identical for the positive and the negative cues. For the
neutral cue trials, the target appeared at any of the four possible
positions with equal chances. There were 20 trials each with
neutral, positive, and negative cues. We used the same positive and
negative face cues as we did in the learning phase.
Participants were tested in groups of three to four. After signing
the consent form, participants in the speech condition were asked
to prepare for a speech for 2 min, whereas participants in the
no-speech condition were asked to write an essay. Participants in
the no-speech condition were assured that their essays would not
a) b) c) d)
Male Female
Figure 1. Illustration of the sequence of events on a single trial. The trial began with a fixation point (a), which
stayed on the screen for 500 ms. The fixation point was then replaced with a face stimulus (i.e., cue). During
the learning phase, the cue was either a negative or a positive expression. During the testing phase, a negative,
positive, or neutral cue appeared. The target (Letter L) appeared at any of four possible locations and stayed on
the screen until the participant made a response (c). Finally, a question appeared on the screen asking the
participant whether the picture was of a man or a woman (d).
be read by anyone but that the experimenter would check whether
they wrote an essay. Participants in the speech condition were told
that one of them would be randomly selected to give a videotaped
speech toward the end of the session. Next, participants were asked
to complete the computer task. Participants were told that their task
was to identify the location of the Lby pressing one of the four
keys that corresponded to the location of the target. After respond-
ing to the target, participants indicated whether the person in the
picture was a man or a woman.
Participants began the incidental learning task by completing the
single-item state anxiety measure. One of the participants in each
speech group was asked to give a 2-min videotaped speech once
everyone in the group completed the incidental learning task. After
the speech (or after the computer task, if they were in the no-
speech group), participants were given a questionnaire that as-
sessed awareness of the contingencies.
Participant Characteristics
A series of SA Speech analyses on age, gender, BDI, and SPS
scores were conducted. Socially anxious individuals scored higher
on the BDI, F(1, 43) 12.16, p.001, and on the SPS, F(1,
43) 139.46, p.001, than their counterparts. No other effects
were significant, indicating that randomization was successful.
Manipulation Check
To test whether the speech manipulation had its intended effect,
we examined participants’ anxiety levels right before they began
the computer task (i.e., after they learned their experimental con-
dition). The SA Speech analysis of variance (ANOVA)
vealed significant main effects for SA, F(1, 43) 12.08, p.001,
.05, and for speech, F(1, 43) 4.30, p.05,
.02. The
pattern confirmed that groups differed in the manner that we
intended by the manipulation (speech, M3.83, SD 1.81;
no-speech, M2.87, SD 1.87). Importantly, the interaction
between SA and speech was not significant, F(1, 43) 2.06,
.007. Means and standard deviations of state anxiety level for each
group was as follows: low-SA, no-speech (M2.36, SD 1.86);
low-SA, speech (M2.67, SD 1.30); high-SA, no-speech (M
3.33, SD 1.83); high-SA, speech (M5.00, SD 1.48).
Data Reduction
Data from trials with errors (i.e., incorrect identification of the
target location or incorrect identification of the gender of a cue)
were discarded. In addition, response times (RTs) that were less
than 200 ms or greater than 1,500 ms were excluded on the basis
of the boxplot (cf. Barnett & Lewis, 1994). For the final sample,
the mean percentages of data lost due to errors and outliers were
2.9% and 1.5%, respectively. Errors and outliers did not differ by
group, speech condition, or cue type (all Fs1.1). The overall
mean RT was 609.1 ms (SD 451.4) before and 563.9 ms (SD
122.5) after the data cleanup. Because our RT data were not
normally distributed (Shapiro–Wilk .89, p.001), we reana-
lyzed the data with log-transformed RTs, which successfully nor-
malized the data (Shapiro–Wilk .96, ns). The results remained
the same, therefore we report the analyses based on raw RTs for
the simplicity of interpretation of results.
Learning of the Cue Valence and Target Location
To examine whether participants learned the relationship be-
tween cue valence and the target location and to rule out the
possibility that socially anxious individuals learned the association
involving the negative cues more strongly than did their less
anxious counterparts, we carried out an SA (high vs. low) Cue
Type (negative vs. positive) Target Location (expected vs.
unexpected) Block mixed ANOVA with polynomial trend anal-
ysis on the RTs for the training trials.
As expected, the Target
Location Linear Trend of Block interaction was significant, F(1,
45) 4.53, p.04,
.01; the decrease in RT was steeper over
blocks for the expected target location (slope ⫽⫺.007) compared
with the unexpected target location (slope ⫽⫺.003). The Cue
Type Target Location Linear Trend of Block interaction was
not significant (F1). Importantly, the four-way interaction,
which might indicate differential acquisition of the associations
between the groups, was not significant (F1). In addition,
neither the SA Target Location Block interaction nor the
SA Target Location was significant (both Fs1). Therefore,
these results provide no support for a stronger expectation for the
threat location among the socially anxious participants.
Interpretive Bias and SA
Although the results above suggest that participants learned the
contingencies to the same extent regardless of their SA level, it is
still possible for socially anxious individuals to become faster at
detecting the target that appears at the negative location regardless
of the valence of the cue on the testing trials. Similarly, partici-
pants in the speech condition might have become better at detect-
ing the target that appeared at the negative location due to their
heightened levels of anxiety. To rule out these possibilities and to
test the main hypothesis that socially anxious individuals interpret
neutral faces negatively, a SA Speech Cue Type Target
Location ANOVA was conducted on the RTs during the testing
phase. There was a significant main effect of target location, F(1,
43) 7.65, p.01,
.15, which was qualified by a significant
four-way interaction, F(2, 42) 3.69, p.04,
.15. The
interaction between SA and target location, however, was not
significant, suggesting that socially anxious individuals were not
significantly different from their less-anxious counterparts in their
readiness to detect targets appearing at locations associated with
negative cues. Likewise, the participants in the speech condition
were not significantly different from those in the no-speech con-
dition in their tendency to detect targets that appear at locations
associated with negative cues as indicated by a nonsignificant
Speech Target Location interaction.
Because the size of a group that a participant was in could affect the
level of state anxiety, an additional ANOVA with SA, speech condition,
and group size was conducted. The results remained the same, and none of
the effects involving the group size was significant.
Mean RTs for each block are available upon request from K. Lira
To follow up on the significant four-way interaction, we tested
the simple three-way interaction between SA, speech, and target
location separately for neutral, positive, and negative cues. The
simple three-way interaction was significant for neutral cues, F(1,
43) 10.56, p.01,
.17, but not for positive or negative
cues (both Fs1), indicating that the group differences in
responses to the neutral cues were responsible for the four-way
interaction. Table 1 presents the means in each condition.
To break down the simple three-way interaction for neutral cues,
we calculated a speeding index by subtracting the RT when the
target appeared at the positive location from the RT when the
target appeared at the negative location. Thus, negative speeding
index scores reflect a tendency to respond more rapidly when the
target appeared at the negative location, which is consistent with
interpreting neutral faces in a more negative way. Simple effects
analyses revealed that the high-SA group showed significantly
more negative bias than the low-SA group in the no-speech con-
dition, F(1, 21) 8.16, p.01,
.28, but not in the speech
condition, F(1, 22) 2.20, ns. In addition, the effect of speech
condition was significant for the low-SA group, F(1, 21) 9.81,
.32, but not for the high-SA group, F(1, 22) 2.69,
ns. The mean speeding index for the speech condition, collapsed
across the anxiety levels, was significantly different from 0,
t(23) ⫽⫺2.66, p.01.
For those in the no-speech condition, the
speeding index of high-SA participants was significantly different
from 0, t(11) ⫽⫺2.26, p.05, whereas the speeding index of
low-SA participants was not, t(10) 1.82, ns. These results
suggest that everyone exhibited negative interpretive bias when
they were in the speech condition (see Figure 2). However, only
the high-SA individuals exhibited negative interpretive bias in the
no-speech condition.
To further provide anchors against which to determine whether
observed patterns signify negative or positive interpretive bias, we
computed speeding indexes for negative and positive cues in the
same manner as we created the speeding index for the neutral cue.
That is, the RTs to detect targets that appeared at positive locations
were subtracted from the RTs of detected targets that appeared at
negative locations. When the speeding indexes for the neutral cues
were statistically contrasted to the speeding indexes for the unam-
biguous cues, the speeding index for the neutral cues was signif-
icantly lower than it was for positive cues in the no-speech,
high-SA group, t(11) 2.78, p.05. No other comparisons were
Through our use of an incidental learning paradigm, the results
suggest an interpretive bias in socially anxious individuals that
cannot be readily explained by either response bias or experi-
menter demand. Thus, the present results suggest that highly
socially anxious individuals treat neutral social interaction cues as
conveying anger or disgust and/or contempt, thereby extending the
results of earlier studies in which researchers used verbal stimuli
(e.g., Hirsch & Mathews, 2000). These findings also replicate and
extend results obtained in a previous study in which researchers
used a priming paradigm (Yoon & Zinbarg, 2007). Although
demand characteristics cannot be conclusively refuted, it seems
implausible that the participants would have figured out (a) our
hypothesis that SA would be related to interpretive bias and (b)
how this hypothesis translated into predictions about their RTs.
Another purpose of the current study was to examine whether
the interpretive bias would be affected by a threat manipulation. In
the current study, the group differences were significantly smaller
when there was an explicit threat (i.e., speech condition). In fact,
the group differences were only significant when there was no
apparent threat, such that high-SA individuals exhibited a negative
interpretive bias, whereas the low-SA individuals did not exhibit
such a bias. In contrast, participants, regardless of anxiety levels,
exhibited negative interpretive biases in the threat condition. Thus,
it could be argued that interpreting neutral faces in a negative
manner is the default mode for high-SA individuals, whereas
low-SA individuals interpret neutral faces negatively only when
anticipating threat.
One explanation for the effect of the speech threat draws on the
model proposed by Mathews, Mackintosh, and Fulcher (1997).
Because there was no significant group difference in the speech con-
dition, we tested only one mean collapsed across the anxiety levels to
maximize power.
Table 1
Mean Response Latencies to Targets (in ms) for Neutral, Negative, and Positive Cue Trials
Target location
Low-SA group High-SA group
Speech (n12) No-speech (n11) Speech (n12) No-speech (n12)
Neutral cues
Negative 467.71 57.77 593.16 201.21 509.85 107.51 527.44 73.14
Positive 514.13 96.38 553.55 169.24 523.26 103.74 594.43 131.66
Negative cues
Negative 501.66 140.77 585.64 186.26 520.87 74.55 557.95 111.73
Positive 557.49 143.85 636.69 297.85 524.14 81.58 660.25 169.65
Positive cues
Negative 549.17 240.51 625.68 212.66 546.73 97.20 644.25 135.04
Positive 507.23 95.60 594.61 163.71 517.28 142.94 580.06 99.11
Note.SAsocial anxiety.
According to Mathews et al., interpretive biases arise from selec-
tive attention to threat, which enhances the activation of the
threatening interpretation of an ambiguous stimulus. More specif-
ically, we assume that when faced with the threat of public
speaking, most people experience enhanced selective attention to
potentially threatening faces. For the low-SA group, this resulted
in a boost of the activation of the negative interpretation of an
ambiguous face such that it tended to win the mutually inhibitory
competition among the activated interpretations. For the high-SA
group, the activation of the negative interpretation was already
boosted to the point at which the negative interpretation was
already tending to win even in the absence of the speech threat.
What remains unclear are the conditions in which a threat
manipulation (a) enhances group differences in bias (e.g., Calvo &
Castillo, 1997), (b) masks group differences by causing the
low-SA group to demonstrate the bias otherwise characteristic of
the high-SA group (as presented here), or (c) masks group differ-
ences by suppressing the bias otherwise demonstrated by the
high-SA group (e.g., Mathews & Sebastian, 1993). Studies differ
in several ways, including the type of bias assessed (i.e., atten-
tional vs. interpretive), the type of stimuli presented (e.g., verbal,
pictorial), the nature of the populations studied (i.e., clinical vs.
high-trait anxious), and the temporal location of a threat. Further
research needs to be done to sort out which methodological dif-
ferences account for the differences in the results.
Limitations of this study are worth mentioning. First, we did not
administer diagnostic interviews. Thus, future studies are neces-
sary to test whether the obtained results can be generalized to a
clinical population. Second, we do not have information about
whether participants in the speech condition believed that they had
to give a speech. However, considering the higher levels of anxiety
in the speech condition compared with the no-speech condition,
the speech manipulation seemed to have its intended effect. It is
also possible that our low-SA group might have included individ-
uals with high public speaking anxiety who are otherwise not
socially anxious. This could have contributed to the findings of
low-anxiety individuals in the speech condition who showed neg-
ative biases. Third, we used a single-item measure to assess the
level of state anxiety. The item has good face validity and dem-
onstrated that the speech manipulation had its intended effects on
the level of state anxiety. It is still possible, however, that the
single item might not have adequately assessed the group differ-
ences due to the threat manipulation. Relatedly, our results might
represent a levels effect; that is, an increase from low to moderate
state anxiety may serve to elicit a negative interpretive bias, which
is unaffected by further increases in state anxiety. Despite the
nonsignificant interaction between the group and the speech con-
dition, the pattern of means suggests that our threat manipulation
moved individuals with different levels of SA through different
parts of the state anxiety continuum. Thus, it remains for future
researchers to determine whether the difference between the anx-
iety groups that are not undergoing a social challenge in interpre-
tive response is due to the inherent state anxiety difference be-
tween the groups or to a different impact of state anxiety on
interpretive bias in the two groups.
One might argue that neutral faces are not ambiguous. We note
that one can draw different inferences about the actor’s emotional
state from a neutral expression, because there is considerable
ambiguity in this process when the actor displays a neutral expres-
sion. Relatedly, Ekman and Friesen’s (1976) norms indicate that
the neutral faces tend to be more ambiguous than other facial
expressions, as there is far more variance in responses to the
Low SA High SA
Bias Score
no speech
Figure 2. Bias score for neutral cue trials as a function of social anxiety (SA) and speech condition. Error bars
represent one standard error.
neutral faces. It is possible that the present results might reflect
socially anxious individuals’ negative emotional reactions to faces
that they and the low-SA group interpret identically. Differential
emotional appraisal alone, however, cannot explain the whole
story. If the groups are making different emotional appraisals of
the neutral faces, and socially anxious individuals find all faces
more aversive than their less-anxious counterparts, we are still left
with a continuum of relative aversiveness within each group. That
is, differential emotional appraisal cannot fully account for why
the socially anxious individuals tend to respond to neutral faces
more as they do the truly aversive faces than they do the happy
faces. Another important extension for future studies would be to
use nonsocial stimuli in the learning phase to even more defini-
tively rule out that the current results are due to group differences
in initially learning about the positive and negative social stimuli.
These limitations notwithstanding, the incidental learning para-
digm renders response bias implausible, as participants were never
asked to indicate what expression they thought was represented in
each of the pictures. Thus, the present study provides strong
support for the hypothesis that SA is associated with a bias toward
interpreting neutral facial expressions in a negative manner. More
specifically, our results suggest that interpreting neutral faces in a
negative manner is the default mode for socially anxious individ-
uals, whereas people with low levels of anxiety interpret neutral
faces negatively only when they are under threat.
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Received December 11, 2006
Revision received January 18, 2008
Accepted March 3, 2008
... Since evaluations from others are often conveyed through and inferred from facial expressions, how individuals with social anxiety respond to others' faces could provide insight into social information processing among these individuals. In previous studies wherein participants were presented with a picture of an emotional face and asked to identify its emotional category or rate its emotional magnitude, people with high social anxiety perceived neutral or ambiguous facial expressions more negatively than those in the control group [6][7][8][9][10] . ...
... Why do people with high social anxiety tend to perceive the number of emotional expressions differently from their counterparts? People with high social anxiety negatively evaluate the emotional value of facial expressions even when these expressions are presented individually [6][7][8][9][10] . Thus, the negative bias for an individual face could result in the overestimation of the number of negative faces among a group of emotional expressions. ...
... The number of angry faces in the stimulus display varied across nine levels (with 0, 4,6,7,8,9,10,12, and 16 angry faces) with the remainder being happy faces. All negativity conditions were randomly interleaved throughout the study for each participant. ...
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The cognitive model of social anxiety suggests an association between social anxiety and cognitive bias toward negative social information. This study investigated the numerosity perception of emotional faces among individuals with high social anxiety. Seventy-five college students completed self-reported questionnaires—assessing social anxiety symptoms—and a numerosity comparison experiment. In each trial of the experiment, participants were presented with a group of 16 emotional faces, varying in the number of faces expressing positive and negative emotions. They were asked to judge which emotion—positive or negative—was more numerous in the crowd. Bias and sensitivity in numerosity perception of emotions were estimated by fitting a psychometric function to participants’ responses. Individuals with low social anxiety showed a bias toward positive faces (t(17) = 2.44, p = 0.026), while those with high social anxiety did not (t(17) = 1.87, p = 0.079). Correlation analyses indicated that social anxiety was negatively associated with the parameters of the function (mean for bias and standard deviation for sensitivity; r = − 0.34, p = 0.003 for mean; r = − 0.23, p = 0.047 for standard deviation). Thus, our results suggest that socially anxious individuals lack the bias toward positive emotion and are more sensitive to negative emotion than nonanxious individuals in perceiving the numerosity of facial expressions.
... Some studies support that socially anxious individuals prioritize threatening stimuli by paying attention to them 5,17 and overattribute the threat or anger to neutral stimuli. 4,[18][19][20] Other studies have reported that there is an attentional bias away from the threat, that is, avoiding the negative emotions reduces the threat for socially anxious individuals, [21][22][23] and no evidence for overattribution of the threat to neutral emotion stimuli was found. [24][25] Schofield et al. 26 hypothesized that the symptoms of SA may be associated with difficulty in disengaging attention from threat signals than from neutral signals. ...
... However, some limitations of the previous studies question the generalizability of the findings to the clinical population, including recruitment of the participants without a detailed psychiatric evaluation and a sample consisting of nonclinical population and use of single-item measures for anxiety. 4,17,23 Overall, evidence suggests that people with SAD demonstrate deficits in neurocognitive performance on attention and enhanced threat perception to emotional stimuli. However, there is limited literature examining the relationship between emotional threat perception (ETP) and attentional control and working memory in SAD. ...
... The findings are supported by similar observations made previously. 4,18,25,[44][45][46] According to Mathew and Mackintosh, 47 interpretive biases arise from selective attention to threat in their model of the threat evaluation system, leading to enhanced attribution of the threat to ambiguous stimuli. Thus, when presented with socially relevant ambiguous stimuli, an individual may be more likely to interpret them negatively compared to other individuals who are not socially anxious. ...
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Background The negative appraisal of emotional stimuli is a feature of social anxiety disorder (SAD). People with SAD demonstrate deficits in neurocognitive performance while performing tasks of attention. However, the relationship between attentional control, working memory, and threat perception in SAD has not been studied well. The present study aimed to identify patterns of threat perception in relation to performance on attention and visuospatial working memory tasks in individuals with SAD. Methods Subjects with SAD ( n = 27) and a healthy comparative (HC) group ( n = 26) completed tasks of sustained and focused attention, visuospatial working memory, computerized emotion identification, and pictorial emotional Stroop. Results The SAD group had decreased performance in the domains of sustained (P = 0.001) and focused attention (P = 0.04). They also had an enhanced threat perception as demonstrated by greater reaction time to anger (P = 0.03), lower emotion recognition accuracy (P = 0.05), and higher over-identification of the threat to neutral and nonthreatening faces. However, the Stroop effect was not demonstrated across the groups. No group difference was seen in the performance on the visuospatial working memory tasks. Lower focused attention was significantly correlated with higher emotional threat perception (ETP; P = 0.001) in the SAD group. Conclusion People with SAD have greater deficits in attention processing and ETP. The attention deficits were associated with enhanced ETP in social anxiety. The link between threat perception and cognitive functions would aid in a better understanding of SAD and in planning appropriate intervention.
... Moreover, certain types of "errors" or biases in emotion recognition have been linked to psychopathology or resiliencepromoting capacities. Biases towards labeling emotionally neutral stimuli as negatively-valenced has been associated with negative affect and symptoms of psychopathology (Lepp€ anen et al., 2004;Pinkham et al., 2011;Yoon & Zinbarg, 2008), whereas a bias towards labeling neutral stimuli as positively-valenced has been associated with positive affect and optimism (Beadel et al., 2016). Psychological interventions such as cognitive behavioral therapies aim to modify such biases, and this bias modification is thought to represent an underlying mechanism that accounts for these interventions' positive effects on mental health (Reiter et al., 2021). ...
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Background Environmental adversity and subclinical symptoms of psychopathology in adolescents increase their risk for developing a future psychiatric disorder, yet interventions that may prevent poor outcomes in these vulnerable adolescents are not widely available. Aims To develop and test the feasibility and acceptability of a prevention-focused program to enhance resilience in high-risk adolescents. Method Adolescents with subclinical psychopathology living in a predominantly low-income, Latinx immigrant community were identified during pediatrician visits. A group-based intervention focused on teaching emotion recognition and regulation skills was piloted in three cohorts of adolescents (n = 11, 10, and 7, respectively), using a single arm design. The second and third iterations included sessions with parents. Results Eighty-eight percent of participants completed the program, which was rated as beneficial. Also, from baseline to end of treatment, there was a significant decrease in subclinical symptoms and a significant increase in the adolescents’ positive social attribution bias (all p < 0.05). Conclusions A resilience-focused intervention administered to high-risk adolescents was found to be feasible and acceptable to participants. Future work is needed to determine whether such a program can reduce the incidence of negative outcomes, such as the development of psychiatric disorders and related disability, in this population.
... That participants in our study did not extinguish on Day 1 was unexpected given prior studies using the same paradigm in healthy individuals (Dunsmoor et al., 2015;Lucas et al., 2018). One explanation is that the CS used in our study (angry faces) may have been particularly anxiety-provoking for our sample, given that 70.6% of our sample met the criteria for SAD and individuals with social anxiety tend to interpret facial expressions in a more threatening way than non-anxious individuals (Mohlman et al., 2007;Yoon and Zinbarg, 2008). Another explanation could be a general abnormality in fear extinction in those with pathological anxiety. ...
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Studies with rodents and healthy humans suggest that replacing the expected threat with a novel outcome improves extinction and reduces the return of conditioned fear more effectively than threat omission alone. Because of the potential clinical implications of this finding for exposure-based anxiety treatments, this study tested whether the same was true in individuals with pathological anxiety (i.e., met DSM-5 diagnostic criteria for an anxiety disorder and/or obsessive-compulsive disorder (OCD). In this preliminary test of novelty-facilitated extinction, 51 unmedicated individuals with pathological anxiety were randomized to standard extinction (n = 27) or novelty-facilitated extinction (n = 24). Participants returned 24 h later to test extinction recall and fear reinstatement. Skin conductance responses (SCR) were the dependent measure of conditioned fear. Participants in both groups learned the fear association but variably extinguished it. Novelty did not facilitate extinction in this preliminary trial. Findings underscore the importance of translating paradigms from healthy humans to clinical samples, to ensure that new treatment ideas based on advances in basic neuroscience are relevant to patients.
... Furthermore, the early follicular phase was largely underrepresented in the follicular groups of the previous samples, making a comparison of the previous studies with the present study difficult. Negativity biases in neutral or ambiguous face recognition have been repeatedly implicated in individuals with affective disorders, (social) anxiety and other mental problems (Richards et al., 2002;Leppänen et al., 2004;Yoon and Zinbarg, 2008;Mier et al., 2014;Münkler et al., 2015;Gutiérrez-García and Calvo, 2017;Peschard and Philippot, 2017). These biases or overinterpretations could contribute to the difficulties in social interactions and relations in these individuals. ...
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Accuracy in facial emotion recognition has shown to vary with ovarian hormones, both in naturally cycling women, as well as in women taking oral contraceptives. It remains uncertain however, if specific – endogenous and exogenous – hormonal levels selectively impact recognition of certain basic emotions (or neutral faces) and if this relationship coincides with certain affective states. Therefore, we investigated 86 women under different hormonal conditions and compared their performance in an emotion recognition task as well as self-reported measures of affective states. Based on self-reported cycle days and ovulation testing, the participants have been split into groups of naturally cycling women during their early follicular phase (fNC, n = 30), naturally cycling women during their peri-ovulatory phase (oNC, n = 26), and women taking oral contraceptives (OC, n = 30). Participants were matched for age and did not differ in education or neuropsychological abilities. Self-reported anxiety and depressive affective state scores were similar across groups, but current affective state turned out to be significantly more negative in fNC women. Independent of negative affective state, fNC women showed a significantly higher negativity bias in recognizing neutral faces, resulting in a lower recognition accuracy of neutral faces compared to oNC and OC women. In the OC group only, negative affective state was associated with lower recognition accuracy and longer response times for neutral faces. Furthermore, there was a significant, positive association between disgust recognition accuracy and negative affective state in the fNC group. Low progesterone levels during the early follicular phase were linked to higher negative affective state, whereas in the peri-ovulatory phase they were linked to elevated positive affective state. Overall, previous findings regarding impaired emotion recognition during OC-use were not confirmed. Synthetic hormones did not show a correlation with emotion recognition performance and affective state. Considering the important role of emotion recognition in social communication, the elevated negativity bias in neutral face recognition found for fNC women may adversely impact social interactions in this hormonal phase.
... Although prior literature has predominantly used 'neutral' facial expressions as control stimuli, this practice is considered problematic as expressions intended to portray emotional neutrality can be interpreted differently by the viewer (Filkowski & Haas, 2017). This is a particular concern when examining processes related to anxiety and depression, conditions that are characterised by 'negative interpretation bias' of neutral or ambiguous stimuli (Yoon & Zinbarg, 2008). Stimuli were presented in blocks of 12 s, each containing six image presentations of the same expression type. ...
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Altered functioning of the brain’s threat and reward circuitry has been linked to early life adversity and to symptoms of anxiety and depression. To date, however, these relationships have been studied in isolation and in categorical-based approaches. It is unclear to what extent early life adversity and psychopathology have unique effects on brain functioning during threat and reward processing. We examined functional brain activity during a face processing task in threat (amygdala, ventromedial prefrontal cortex) and reward (ventral striatum, orbitofrontal cortex) regions of interest among a sample (N = 103) of young adults (aged 18-19 years) in relation to dimensional measures of early life adversity and symptoms of anxiety and depression. Results demonstrated a significant association between higher scores on the deprivation adversity dimension and greater activation of reward neural circuitry during viewing of happy faces, with the largest effect sizes observed in the orbitofrontal cortex. We found no significant associations between the threat adversity dimension, or symptom dimensions of anxiety and depression, and neural activation in threat or reward circuitries. These results lend partial support to theories of adversity-related alterations in neural activation and highlight the importance of testing dimensional models of adversity and psychopathology in large sample sizes to further our understanding of the biological processes implicated.
... When considering ambiguous laughter, consistent with evidence for associations between psychopathology and a heightened negativity bias (Meyer et al., 2004;Yoon & Zinbarg, 2008), psychopathic traits were associated with higher likelihood of interpreting ambiguous stimuli as hostile, especially among the younger adults across our samples. Regulation in response to emotional displays continues to develop into early adulthood (Silvers et al., 2012), which may impact interpretation of social signals. ...
Introduction Laughter conveys important information that supports social communication and bonding. Research suggests that unique acoustic properties distinguish laughter that promotes affiliation from laughter that conveys dominance, but little is known about potential individual differences in laughter interpretation or contagion based on these specified social functions of laughter. Psychopathy is associated with both affiliative deficits (e.g., lack of empathy and impaired social bonding) and behaviors that assert social dominance (e.g., manipulativeness). Thus, relationships between psychopathic traits and impaired laughter interpretation or contagion could give insight into etiological pathways to psychopathy. Method In two studies conducted with four independent samples (total N = 770), participants categorized laughter clips that varied in cues to affiliation and dominance. Results Participants overall drew rich and accurate social inferences from dominant and affiliative laughter and modulated their interest in joining in with laughter based on the type and degree of affiliation and dominance conveyed. However, individuals higher in psychopathic traits failed to distinguish between laughter types and did not modulate their level of engagement based on laughter features. Conclusion The results suggest a potential mechanism that underlies the broader social difficulties associated with psychopathy.
... Individuals with anxiety disorders and heightened trait anxiety show attentional biases towards threat-related information and tend to interpret the emotions of others in a more negative manner (e.g. Cisler & Koster, 2010;Richards et al., 2002;Yoon & Zinbarg, 2008). Threat bias models of anxiety propose that these emotional biases play a causal role in the aetiology and/or maintenance of anxiety symptoms (e.g. ...
Background: Emotion perception is essential to human interaction and relies on effective integration of emotional cues across sensory modalities. Despite initial evidence for anxiety-related biases in multisensory processing of emotional information, there is no research to date that directly addresses whether the mechanism of multisensory integration is altered by anxiety. Here, we compared audiovisual integration of emotional cues between individuals with low vs. high trait anxiety. Methods: Participants were 62 young adults who were assessed on their ability to quickly and accurately identify happy, angry and sad emotions from dynamic visual-only, audio-only and audiovisual face and voice displays. Results: The results revealed that individuals in the high anxiety group were more likely to integrate angry faces and voices in a statistically optimal fashion, as predicted by the Maximum Likelihood Estimation model, compared to low anxiety individuals. This means that high anxiety individuals achieved higher precision in correctly recognising anger from angry audiovisual stimuli compared to angry face or voice-only stimuli, and compared to low anxiety individuals. Limitations: We tested a higher proportion of females, and although this does reflect the higher prevalence of clinical anxiety among females in the general population, potential sex differences in multisensory mechanisms due to anxiety should be examined in future studies. Conclusions: Individuals with high trait anxiety have multisensory mechanisms that are especially fine-tuned for processing threat-related emotions. This bias may exhaust capacity for processing of other emotional stimuli and lead to overly negative evaluations of social interactions.
Anxiety disorders are the most common mental health disorders and comprise a large number of years lost to disability. The work in this thesis is oriented towards understanding anxiety using a computational approach, focusing on uncertainty estimation as a key process. Chapter 1 introduces the role of uncertainty within anxiety and motivates the subsequent experimental chapters. Chapter 2 is a review of the computational role of the amygdala in humans, a key area for uncertainty computation. Chapter 3 is an experimental chapter which aimed to address gaps in the literature highlighted in the preceding chapters, namely the link between sensory uncertainty processing and anxiety and the role of the amygdala in this process. This chapter focuses on the development of a novel computational hierarchical Bayesian model to quantify sensory uncertainty and its application to neuroimaging data, with intolerance of uncertainty relating to greater neural activation in the insula but not amygdala. Chapter 4 targets the computational mechanisms underlying the negative self-bias observed in subclinical social anxiety. Again, this chapter focuses on the development of novel computational belief-update models which explicitly model uncertainty. Here, we see that a reduced trait self-positivity underpins this negative social evaluation process. The final experimental chapter presented in Chapter 5 investigates the link between different computational mechanisms, such as uncertainty, and a range of mood and anxiety symptomatology. This study revealed cognitive, social and somatic computational profiles that share a threat bias mechanism but have distinct negative-self bias and aversive learning signatures. Contrary to expectations, none of the uncertainty measures showed any associations with anxiety symptom subtypes. Finally, chapter 6 brings together the work in this thesis and alongside limitations of the work, discusses how these experiments contribute to our understanding of anxiety and the role of uncertainty across the anxiety spectrum.
Background: Anticipatory Anxiety (AA) is defined as a course of thoughts, feelings, and actions occurring just and only "before" an anxiety-provoking event. In order to explore this construct, the Anticipation Anxiety Inventory (AAI) was developed and its psychometric properties have been investigated in two studies. Methods: Study 1 used an Exploratory Factor Analysis approach to determine the factor structure of the items of the scale. In study 2, a Confirmatory Factor Analysis was performed to assess the scale structure, the validity of the factor solution, and convergent and discriminatory validity. Results: Exploratory factor analysis from study 1 suggested 13 items across four factors for the AAI: Emotional Hypersensitivity, Physical responses to AA, Dysfunctional Cognitions, and Daily Functioning. In study 2, the Confirmatory Factor Analysis indicated that the 4-factor solution of the AAI had an acceptable fit, excellent internal consistency (α= 0.92), and displayed good convergent and discriminatory validity. Conclusion: The AAI could be proposed as a useful valid and reliable tool to investigate AA. For future implications, more research is needed regarding the utility of this measure in experimental designs or clinical settings.
Three experimentsinvestigated the tendency of high-anxiety individuals to interpretambiguous information in a threatening fashion. Priming ambiguous sentences (concerned with ego-threat, physical-threat, or non-threat events) were presented, followed by a disambiguating sentence in which a target word either confirmed or disconfirmed the consequence implied by the priming context. The sentences were presented word-by-word at a predetermined pace. Subjects read the sentences and pronounced the target word (naming task), which appeared either 500 msec or 1,250 msec after the onset of the last word (pre-target word) in the priming context. Results indicated that high-anxiety subjects named target words confirming threats faster than low-anxiety subjects, relative to non-threat words. Furthermore, this interpretative bias is: (a) strategic, rather than automatic, as it occurred with a 1,250-msec SOA, but not with a 500-msec SOA; (b) temporary, as it was found under evaluative stress conditions increasing state anxiety, butnot withnon-stress; and (c) specificto ego-threats, as it happened with ambiguous information concerning self-esteem and social evaluation, rather than with physical-threat-related information.
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Previous studies have suggested that subjects are slower to colour-name emotional words, when the meaning of these words is related to a fear or current concern. We carried out three experiments on subjects having either high or low fear of snakes, with the aim of replicating this finding, and of testing the effects of arousing fear in subjects at the same time. Unexpectedly, we could only replicate the predicted interference effect in the absence of threat, whether due to a snake or to another threatening stimulus. We conclude that emotional interference effects do depend on high levels of fearfulness or trait anxiety, and on a match between the content of the subjects' concerns and the meaning of the interfering material. However, this interference may be paradoxically obscured by fear arousal, or the presence of a real danger that alters processing priorities in highly fearful subjects.
The development and validation of the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) two companion measures for assessing social phobia fears is described. The SPS assesses fears of being scrutinised during routine activities (eating, drinking, writing, etc.), while the SIAS assesses fears of more general social interaction, the scales corresponding to the DSM-III-R descriptions of Social Phobia—Circumscribed and Generalised types, respectively. Both scales were shown to possess high levels of internal consistency and test–retest reliability. They discriminated between social phobia, agoraphobia and simple phobia samples, and between social phobia and normal samples. The scales correlated well with established measures of social anxiety, but were found to have low or non-significant (partial) correlations with established measures of depression, state and trait anxiety, locus of control, and social desirability. The scales were found to change with treatment and to remain stable in the face of no-treatment. It appears that these scales are valid, useful, and easily scored measures for clinical and research applications, and that they represent an improvement over existing measures of social phobia.
20 patients (mean age 31.9 yrs) with social phobia were compared with 20 normal controls (mean age 32.6 yrs) on a task of face recognition. Ss were instructed to look at 20 photos of persons unknown to them, and to judge the expected quality of contact that would take place with these persons if they were to meet them in real life. Although the social phobics expected a less good contact with these persons, there was no significant group difference on recognition of high- vs low-contact faces. The results are discussed in contrast with another study, which showed evidence of recognition bias for critical faces in a group of social phobics. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Three experiments with 134 undergraduates and 80 recent high school graduates investigated processing of covariation between verbally described psychological characteristics and appearance of a set of stimulus persons. Based on S. Glucksberg and M. McCloskey's (see record 1982-07068-001) 2-stage question-answering model, it was hypothesized that if the information related to the manipulated covariation was processed and registered, it would result in an increase of processing time for questions that might be considered relevant to the covariation. Results indicate that although Ss were unable to articulate the manipulated covariation in any of the experiments, the pattern of response latencies obtained conformed exactly to the predictions. In 2 of these experiments, effects of the stimulus material in Ss' subsequent judgments were found, consistent with the model. Ss behaved as if they had "learned" the rule implied by the covariation and followed it in their subsequent judgments. (30 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
In this chapter, we suggest a task-related distinction between implicit learning and implicit memory. We provide an overview of the relevant results and approaches in implicit learning and implicit memory research. With these in mind, and based on our task-related distinction between the areas, we first outline the parallels between the areas of implicit learning and implicit memory and then take a look at the differences. However, we go beyond establishing parallels and differences in asking how they may be used for a synergistic transfer of concepts and techniques between the areas of implicit learning and implicit memory. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The existence of cognitive biases in anxiety is now well established, and we summarize evidence demonstrating attentional vigilance to cues associated with threat, pessimistic interpretation of ambiguous items and an increased perception of the likelihood of occurrence of negative events. We explore how these reactions can be understood within an evolutionary context, and present a descriptive model consistent with the experimental findings, conducive to modification of responses through learning. A computational implementation of aspects of the model successfully simulates changes in reaction time for a simple task as anxiety levels increase. Future directions include pursuing the causal nature of biases in anxiety and examining the potential for change through training techniques.