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Jumping to interpretations: Social anxiety disorder and the identification of emotional facial expressions
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Behaviour Research and Therapy 45 (2007) 591–599 www.elsevier.com/locate/brat
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Jumping to interpretations: Social anxiety disorder and the identification of emotional facial expressions Jan Mohlmana,, Cheryl N. Carminb, Rebecca B. Pricea a
Rutgers, the State University of New Jersey, Department of Psychology, 152 Frelinghuysen Road, Piscataway, NJ 08854, USA b University of Illinois at Chicago, USA Received 20 October 2005; received in revised form 12 February 2006; accepted 9 March 2006
Abstract A small body of research suggests that socially anxious individuals show biases in interpreting the facial expressions of others. The current study included a clinically anxious sample in a speeded emotional card-sorting task in two conditions (baseline and threat) to investigate several hypothesized biases in interpretation. Following the threat manipulation, participants with generalized social anxiety disorders (GSADs) sorted angry cards with greater accuracy, but also evidenced a greater rate of neutral cards misclassified as angry, as compared to nonanxious controls. The controls showed the opposite pattern, sorting neutral cards with greater accuracy but also misclassifying a greater proportion of angry cards as neutral, as compared to GSADs. These effects were accounted for primarily by low-intensity angry cards. Results are consistent with previous studies showing a negative interpretive bias, and can be applied to the improvement of clinical interventions. r 2006 Elsevier Ltd. All rights reserved. Keywords: Social anxiety disorder; Emotional faces; Facial interpretation; Interpretive bias
Introduction Accurate ‘‘on-line’’ interpretation of the facial expressions of others is a skill that may facilitate social interaction (Ekman & Friesen, 1971). For instance, when attempting to sustain a lively conversation, we use the emotional displays of others to quickly infer their level of interest in what we have to say. Subsequently, we might adjust the tone or content of our speech whenever a negative emotional display is perceived. If the ability to efficiently and accurately decode human facial expressions is likely to play a role in the development and maintenance of social competence, then an inability to accurately interpret facial expressions may give rise to problems in social functioning (Gard, Gard, Dossett, & Turone, 1982). For example, misinterpreting emotional displays during social interactions can interrupt the flow of conversation, give rise to discomfort and confusion in speakers and listeners, and lead to excessively negative social expectations (Mullins & Duke, 2004; Pozo, Carver, Wellens, & Scheier, 1991). Corresponding author.
E-mail address: [email protected] (J. Mohlman). 0005-7967/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.brat.2006.03.007
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Deficits in this interpretative skill may also be associated with social anxiety disorder as an etiological or maintenance factor. However, the opposite expectation, that socially anxious individuals have special expertise in processing social threat cues, has also been proposed (e.g., Curran, 1977). Others hypothesize that socially anxious individuals are best characterized as having inordinately negative expectations rather than a skills deficit per se, which leads to negative interpretations of some or all emotional cues (e.g., Pozo et al., 1991). Evidence of interpretive biases for facial cues in social anxiety comes mostly from analogue studies. Typically, a threat manipulation is performed in order to maximize mood-congruent interpretive bias in individuals vulnerable to performance anxiety. Most studies have presented stationary facial slide displays and used verbal or nonverbal emotion classification tasks as the dependent measure. For instance, Gard et al. (1982) demonstrated that at baseline, an anxious group was more accurate than a control group at classifying facial slides into six emotion categories; however, the anxious group became less accurate than the controls when the task was performed under stressful conditions, indicating generally impaired performance. Using two forced-choice classification tasks for bimodal emotional displays (negative vs. neutral facial slides; negative vs. positive video clips), Winton, Clark and Edelmann. (1995) reported that high scorers on the Fears of Negative Evaluation Scale (FNE; Watson & Friend, 1969) performed better than low scorers at identifying negative emotional displays, but worse at identifying neutral displays, with a significantly greater number of false positives (neutral displays classified as negative). Signal detection analysis revealed that the group difference was explained by a negative response bias in the high scorers rather than an enhanced ability to detect negative emotional cues. Using a similar forced-choice nonverbal task with four emotion categories (happy, sad, angry and fearful), Mullins and Duke (2004) found a different pattern of results; response times, but not accuracy, were impacted by FNE scores and a threat manipulation. In the no-threat condition, high FNE scorers were slower than low scorers to identify low-intensity angry and sad cues; following a stressor, high scorers were faster to identify high-intensity angry and fearful cues. Richards and colleagues (2002) found that an anxious group categorized significantly more faces as ‘‘fearful’’ than controls, with no significant differences on any other category (angry, happy, sad, disgusted, and surprised). In a follow-up study employing a stress manipulation, the group difference for fearful faces was replicated; additionally, all participants classified more faces as ‘‘angry’’ following the manipulation, and the low scorers showed an increased sensitivity for ‘‘happy’’ faces. This body of work suggests that anxious individuals are more attuned than controls to negative emotional displays, fearful and angry faces in particular. Other studies solicited judgments in the context of an active social interaction. Using a staged interaction task, Pozo et al. (1991) required subjects to pose questions to an ‘‘interaction partner’’ via a two-way television monitor (in reality, the monitor displayed a pre-recorded videotape). High scorers on the Self-Consciousness Scale (SCS; Fenigstein, Scheier, & Buss, 1975) consistently rated lower interest and acceptance on the part of their interaction partner than the lower SCS group, suggesting either a negative expectancy bias in the high group or a positive bias in the low group. Similarly, during a speech giving task, Veljaca and Rapee (1998) reported that high scorers on the Albany Panic and Phobia Questionnaire (Rapee, Craske, & Barlow, 1994) showed increased sensitivity for detecting negative indicators from the audience and decreased sensitivity for detecting positive indicators. The authors also identified a response bias whereby the higher group evidenced a more liberal criterion for judging negative indicators than the lower group. Anxious individuals also seem to pay more attention to negative nonverbal cues in dynamic tasks. In summary, results suggest that individuals who score in the higher range on anxiety questionnaires have a lower threshold than controls (i.e., increased rates of misclassification errors) for identifying negative nonverbal cues, rather than a skills deficit or special expertise in decoding such cues. However, because results have been mixed, additional studies are needed to test this effect in clinically anxious individuals, and to improve upon methodology. The goal of this paper was to extend results from the literature on heightened state anxiety and the interpretative skills of individuals with generalized social anxiety disorder (GSAD) versus nonanxious controls (NACs). This was accomplished through the inclusion of a clinically anxious sample, increased attention paid to aspects of facial stimuli (e.g., graded intensity, standardized, prototypical examples of emotions, straight ahead gaze on all stimuli), an effective threat manipulation, and the use of a timed task, which might be more likely to yield biased results than an untimed task due to the reduced time available for intellectual processing of emotional cues. A categorization task was used in which participants had 90 s to sort a deck of 96 stimulus cards showing 25, 50, or 100% intensity of emotional faces to corresponding emotional target cards (happy, sad, angry, neutral).
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All participants completed the emotional identification task at rest and in a heightened anxiety state. Cards displaying angry faces were chosen as the threat cue of interest because anxious individuals tend to interpret them as threatening (e.g., Gilboa-Schechtman, Foa, & Amir, 1999; Ohman, 1993). A group (GSAD, NAC) by condition (baseline, threat) by valence (happy, sad, angry, neutral facial expressions) interaction was expected in which GSADs would outperform NACs on accurately sorting angry cards, but would perform worse than NACs on sorting neutral cards, following the threat manipulation. Additionally, it was hypothesized that GSADs would misclassify a greater number of neutral cards to the angry target cards than NACs, and NACs would misclassify a greater number of angry cards to the neutral target card than GSADs. These effects were not expected in the baseline condition, as interpretive biases have been related to mood in some prior investigations (e.g., Pozo et al., 1991; Veljaca & Rapee, 1998). The effects of graded stimulus intensity are somewhat difficult to predict. Low-intensity emotional stimuli have been associated with decreased attentional focus (Wilson & MacLeod, 2003), leading ostensibly to incomplete processing and misclassification errors. Consistent with this assumption, some have proposed that low-intensity emotional displays are more difficult to distinguish than high-intensity displays (e.g., Alvarado & Jameson, 2002), and thus more difficult to categorize (e.g., Hess, Blairy, & Kleck, 1997). This may be particularly true in elevated anxiety conditions when attention is divided (e.g., Rapee & Heimberg, 1997) or time is constrained. On the other hand, social anxiety has been associated with selective attention and increased vigilance for negative social cues, especially in heightened anxiety states (e.g., Winton et al., 1995). Thus, it is also possible that GSADs will be more accurate than NACs at identifying negative facial displays in their earlier stages of formation, such as at 25% emotional intensity. Due to these vagaries, hypotheses based on stimulus intensity were somewhat speculative. However, we proposed that if the GSADs were indeed more accurate in sorting cards to angry targets, between-group differences would be most evident at 25%, as compared to 50% and 100% emotional intensities (Alvarado & Jameson, 2002). Method Participants Participants were drawn from a pool of 694 students in undergraduate psychology courses at Syracuse University (Syracuse, NY) between March 2002 and May 2004, who completed the FNE (Watson & Friend, 1969) in mass testing sessions. Those who scored either above 20 or below 9 (the upper and lower quartiles) were contacted by phone and after giving verbal consent, were given the structured clinical interview for DSMIV axis I disorders (SCID; First, Gibbon, Spitzer, & Williams, 1995) by the first author, who had 10 years experience in administering the interview. Although all calls were audiotaped, the sound quality on a subset of tapes ðn ¼ 8Þ was poor. Of the 18 audible tapes, six were randomly chosen (33%) and rated by a second assessor, who was a doctoral student with two years’ training on the SCID. The two raters matched the GSAD diagnosis on five of the six tapes (83%). Those who either met GSAD criteria as a principal diagnosis and scored above 20 on the FNE ðn ¼ 26Þ or did not meet GSAD criteria and scored below 9 on the FNE ðn ¼ 26Þ were included in the study. Means on the FNE were highly similar to those obtained from samples with social anxiety disorder reported in other studies (Oei, Kenna, & Evans, 1991), which increased confidence that our participants were being accurately diagnosed. Of the participants in the GSAD group, for whom GSAD was always the primary diagnosis, four also met criteria for dysthymic disorder, two generalized anxiety disorder, two panic disorder, one major depressive disorder, and six specific phobia. Those in the NAC group did not meet criteria for any other anxiety or mood disorder. w2 tests indicated that the groups were not significantly different on sex ratio, ethnic breakdown, education, marital, or employment status. Measures Participants also completed the Social Phobia Scale (SPS; Mattick & Clarke, 1998), the State Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, & Lushene, 1983), and the Beck Depression Inventory (BDI; Beck & Steer, 1987). All are used frequently in studies of mood and cognition, and demonstrated good psychometric
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properties in the current and prior samples. The Nelson Adult Reading Test—American Version (AMNART; Grober & Sliwinski, 1991) was administered as an estimate of verbal intelligence. Stimuli Stimuli were two decks of 96 300 300 cards featuring computer-generated line drawings of the same face displaying neutral, happy, sad, and angry expressions (Musterle & Rossler, 1986), with 8 cards in each category shown at 25%, 50%, and 100% affective intensities (Horstmann, 2002), for a total of 24 cards depicting each target emotion, and 24 cards showing the same neutral expression. According to Horstmann (2002), images were computer-generated using Ekman and Friesen’s (1978) and Hjortsjo’s (1969) suggestions for muscle coordination of different basic emotional states. Each face was constructed using linear interpolation of a set of values for each emotional display, starting with a neutral face as the basic template. Stimulus decks were arranged in two different orders (a, b), determined by a random number generator. There were four target cards depicting neutral and 100% happy, sad, and angry faces. These particular stimuli were chosen as targets because they had previously received high ratings by Horstmann’s (2002) two independent samples of participants on dimensions of ‘‘typicality’’ as exemplars of target emotions and likelihood that an individual in the target-affective state would demonstrate that specific facial expression (‘‘likelihood’’). Additionally, the face on the cards was standardized (Caucasian dark-haired male) to avoid biases based on the sex, ethnicity or other aspects of appearance. Decks were counterbalanced such that in half of each group of participants, decks a and b were used in the threat, baseline condition on 50% of trials, and in the baseline, threat condition in the remaining trials. Following the card-sorting task, participants rated target stimuli on the dimensions of emotional valence using a seven-point Likert-type scale. The scale ranged from ‘‘1’’ ¼ ‘‘very negative’’ to ‘‘7’’ ¼ ‘‘very positive.’’ Procedure Participants were run on an individual basis in classrooms. After completing the informed consent process, participants were asked to complete the packet of self-report questionnaires. In the baseline condition, which occurred first for 50% of participants in each group, the following instructions were given for the emotional interpretation task: ‘‘The next activity is an emotional IQ task. I am going to ask you to sort each one of the cards in this deck below the target card that you think it matches. You will be matching cards based on the emotion depicted on the face on the card. You are going to be timed, so please sort as quickly as you can, but you must look carefully at the face on each card. Some emotional expressions on the cards will be quite obvious and others will be more subtle and difficult to categorize. For instance (participant was shown sample face depicting varying degrees of disgust), this set of faces depicts the same emotion, disgust, except the emotion on the faces differs in intensity. Do you have any questions on how to complete this task? Please remember to sort as quickly as possible. You may now begin.’’ The experimenter timed each sorting trial with a stopwatch preset to 90 s. During the task the experimenter kept time, facing slightly away from the participant while he or she completed the sorting trials. This was to ensure that instructions for sorting were followed, without eliciting additional anxiety by closely observing the participant’s performance. The threat manipulation, which occurred first for the other 50% of participants, involved deception as a means of producing heightened anxiety. To implement the deception, a confederate (who was actually a research assistant in the first author’s lab) was brought into the room and introduced as another participant in the study. The experimenter said, ‘‘One of the goals of our study is to validate the emotional IQ task by collecting data in two conditions: alone or when you are competing against another participant in our study. The reason for the competition is that we want to increase your motivation to do your best on the task. So if the other participant performs better than you do, then she/he gets an extra course credit. If you perform better than she/he does, then you will receive a second course credit. So whoever does better wins the extra credit. The two
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of you have been paired to compete for this part of our experiment, so one of you will work in here with me and the other will work with our other experimenter in the room next door.’’ Then the confederate always left the room, ostensibly to work with another RA from the study, and the actual participant stayed with the original experimenter. Next, task instructions were given, as shown above. Those who completed the baseline condition second were told, ‘‘Now that you have completed the task in a state of increased motivation, you will be asked to partake in it again, only this time without competing against another subject. This is so that we can assess performance under more naturalistic conditions.’’ After each set of instructions (baseline, threat), participants completed a brief rating scale of mood, which served as a manipulation check. Participants’ ratings of confidence in their ability to perform the task (henceforth called ‘‘confidence’’) and anxiety were made on a seven-point Likert-type scale (1 ¼ ‘‘little confidence’’, ‘‘little anxiety,’’4 ¼ ‘‘neutral,’’ and 7 ¼ ‘‘much confidence’’, ‘‘much anxiety’’). Confidence ratings were collected to check the effects of the manipulation in the NAC group. We wanted to be certain that the manipulation had similar effects across groups (i.e., increasing anxiety without a significant increase in confidence, which would suggest that it led to beneficial motivating effects). Between the trials of the card sorting task, participants engaged in a neutral task (completing crossword puzzles) meant to minimize carryover effects from the threat manipulation. This period lasted 15 min, and participants were told that it was a rest period and a chance for the experimenter to set up the next trial of the task. At the end of the study, all participants were informed about the deception used in the study and given the opportunity to discuss any relevant thoughts or feelings with the experimenter. However, before revealing this, we also asked participants to rate on a seven-point Likert type scale the extent to which they truly believed that they were indeed competing against the confederate in the threat condition (1 ¼ ‘‘didn’t believe it at all,’’ 4 ¼ ‘‘didn’t believe or disbelieve it,’’ and 7 ¼ ‘‘believed it completely’’). Regardless of the outcome, all participants received two credits, and there was no actual competition between participants at any point in the study. Results Self-report questionnaires, stimulus ratings, manipulation check A multivariate analysis of variance (MANOVA) was conducted to test for differences on age, verbal IQ, and self-report measures. The omnibus test was significant, F(1,50) ¼ 16.17, po0.001, Z2 ¼ 0:64. Bonferroniadjusted univariate tests with alpha set at 0.008 (age, verbal IQ, four questionnaires) indicated that GSADs scored significantly higher than NACs on the SPS, F(1,50) ¼ 36.70, po0:001, Z2 ¼ 0:43, the BDI, F(1,50) ¼ 17.19, po0:001, Z2 ¼ 0:26, and the trait scale of the STAI, F(1,50) ¼ 39.06, po0:001, Z2 ¼ 0:44. There were no significant differences between the groups on the state scale of the STAI, age, or verbal I.Q. Descriptive data, stimulus ratings, and results of the manipulation check are shown in Tables 1, 2, and 3, respectively. Emotional card-sorting task To test predictions about interpretive abilities, accuracy data on each stimulus type was compared with repeated measures Group (GSAD, NAC) by Condition (baseline, threat) by Valence (angry, sad, happy, neutral) by Order (Threat, Baseline; Baseline, Threat) MANCOVAs, with proportion of accurately sorted cards entered as the dependent variable. The intensity factor was not run as part of the model because the neutral cards had only one intensity level, whereas the other three types of cards had three intensity levels (25%, 50%, and 100%). BDI, STAI trait scores, and Deck Order were tested as covariates, however, there were no significant effects of these variables in any of the models and they were subsequently omitted. The repeated measures test of the proportion of cards accurately sorted to each target revealed a violation of sphericity assumptions (Mauchley’s test, po0:005), thus the Greenhouse Geisser correction was applied. The test of accuracy yielded significant main effects of Condition, F(1,46) ¼ 16.17, po0:001, Z2 ¼ 0:58, and significant Group by Condition, (F(3,44) ¼ 4.48, po0:05, Z2 ¼ 0:09, Group by Valence (F(3,44) ¼ 20.11, po0:001, Z2 ¼ 0:58, and Condition by Valence interactions, (F(3,44) ¼ 3.00, po0:05, Z2 ¼ 0:17. However, these effects were qualified by a significant Group by Condition by Valence interaction, F(3,44) ¼ 15.77,
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Table 1 Characteristics of the sample
Age Female Education (years) Unmarried Employed Ethnicity Caucasian African American Hispanic/Latino Asian American FNE SPS BDI STAI-state STAI-trait AMNART (verbal IQ)
NAC ðn ¼ 26Þ mean (SD)
GSAD ðn ¼ 26Þ mean (SD)
21.08 (2.52) 65% 13.08 (1.30) 100% 46%
21.46 (2.79) 62% 13.51 (1.31) 100% 54%
81% 15% 2% 2% 4.62 (2.17) 8.80 (5.60) 4.92 (5.31) 28.15 (5.76) 29.50 (8.03) 115.19 (7.86)
88% 8% 0 4% 23.54 (2.21) 20.12 (7.55) 16.00 (12.29) 35.69 (7.26) 45.80 (7.27) 111.94 (8.32)
Note. FNE—Fears of Negative Evaluation Scale (Watson & Friend, 1969), SPS—Social Phobia Scale (Mattick & Clark, 1998), BDI (Beck Depression Inventory, Beck & Steer, 1987), STAI—State Trait Anxiety Inventory (Spielberger, Gorsuch, & Lushene, 1983), and AMNART—American Nelson Adult Reading Test (Grober & Sliwinski, 1991). po0.008.
Table 2 Stimulus valence ratings ðn ¼ 52Þ
Neutral face Sad face Happy face Angry face
3.76 1.53 6.46 1.25
(1.97)a (1.72) (1.80)b (1.68)
Note. Ratings made on 100% intensity target cards, on a 1 to 7 scale, on which 1 ¼ very negative, 2 ¼ negative, 3 ¼ slightly negative, 4 ¼ neutral, 5 ¼ slightly positive, 6 ¼ positive, and 7 ¼ very positive. Ratings were collapsed across groups due to the lack of significant differences. a Significantly different from sad, angry, and happy cards; b Significantly different from sad, angry, and neutral cards.
Table 3 Manipulation check ratings
Baseline anxiety Baseline confidence Threat anxiety Threat confidence Belief in deception
NAC ðn ¼ 26Þ mean (SD)
GSAD ðn ¼ 26Þ mean (SD)
1.42 5.69 2.15 5.90 5.31
2.56 4.96 5.74 4.80 5.71
(1.39) (2.22) (2.31) (2.32) (2.09)
Note. Anxiety, confidence rating scale: 1 ¼ very little, 4 ¼ neutral, and 7 ¼ very much. Groups significantly different at po0.001. a Within group effect on anxiety ratings was significant for both groups.
(1.83) (3.02) (2.70),a (2.87) (2.32)
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Table 4 Proportion of cards sorted correctly to each target card NAC ðn ¼ 26Þ mean (SD)
GSAD ðn ¼ 26Þ mean (SD)
Baseline condition Neutral target Happy target Sad target Angry target
0.95 0.96 0.97 0.94
(0.06) (0.06) (0.05) (0.19)
0.93 0.94 0.95 0.93
(0.07) (0.05) (0.19) (0.08)
Threat condition—accuracy Neutral target Happy target Sad target Angry target
0.96 0.94 0.96 0.80
(0.05) (0.09) (0.10) (0.08)
0.84 0.88 0.89 0.97
(0.06) (0.09) (0.08) (0.05)
Threat condition, accuracy by intensity 25% Angry 50% Angry 100% Angry
0.76 (0.04) 0.89 (0.05) 0.89 (0.06)
0.96 (0.05) 0.94 (0.04) 0.93 (0.05)
Note. Asterisks denote significant between-group effects. po0.01.
po0:001, Z2 ¼ 0:52. Univariate tests (alpha ¼ 0.006) revealed that GSADs sorted a greater number of angry stimulus cards correctly, F(1,50) ¼ 33.74, po0:001, Z2 ¼ 0:41 and fewer neutral cards correctly than NACs, F(1,50) ¼ 34.25, po0:001, Z2 ¼ 0:49, following the threat manipulation. There were no other significant effects. The effect involving the angry target was then investigated further with three between-groups tests of intensity (25, 50, and 100%), which indicated that the effect was accounted for by the 25% intensity stimuli (F(1,50) ¼ 11.59, po0:01, Z2 ¼ 0:49), rather than the 50 or 100% intensities. According to chi square tests, GSADs were more likely (probability ¼ .29, mean 1.58, sd ¼ 1.01) than NACs (probability ¼ .05, mean ¼ 0.15, sd ¼ 0.44) to misclassify neutral cards as angry, and NACs were more likely (probability ¼ .26, mean ¼ 1.54, sd ¼ 1.03) than GSADs (probability ¼ .06, mean ¼ 0.27, sd ¼ 0.67) to misclassify 25% angry cards as neutral, w2 (10, N ¼ 51) ¼ 22.13, po.005, in the threat condition. See Table 4 for descriptive information. Discussion Based on the small body of literature on the interpretive abilities of analogue samples (e.g., Pozo et al., 1991; Veljaca & Rapee, 1998; Winton et al., 1995), we expected that in the threat condition, the GSAD group would sort angry cards more accurately and neutral cards less accurately than NACs. Additionally, it was expected that the GSAD group would evidence more of a specific type of misclassification error (neutral cards sorted to the angry target), revealing a negative interpretive bias. Results were consistent with our predictions for stimuli sorted to the angry target card. It would appear likely that socially anxious individuals are more specifically attuned to threatening stimuli, as there were no effects on the happy or sad cards. As expected, GSADs outperformed NACs in correctly interpreting 25% intensity angry cues as angry in the stressful condition. NACs outperformed GSADs at sorting neutral cards accurately, but showed a tendency to sort 25% angry cards to the neutral target, suggesting a positive bias. Generally, effect sizes ranged from small to medium. These results are somewhat consistent with prior findings (e.g., Pozo et al., 1991; Veljaca & Rapee, 1998; Winton et al., 1995) suggesting that socially anxious participants demonstrate a negative expectancy bias, rather than simply greater accuracy, when interpreting threatening facial expressions. We can rule out response bias as an alternative hypothesis, which would have been characterized by a more general tendency to misclassify cards from other negative emotional categories (i.e., sad) as either angry (GSADs) or neutral (NACs).
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The fact that the GSAD participants responded with more anxiety to the manipulation complicates the interpretation of the present effects, however. It is possible that performance is related to the postmanipulation increase in anxiety in a linear pattern, and that if NACs had responded with as much as anxiety as GSADs, their performance would have been more similar. There may also have been aspects of the study that contributed above and beyond the threat manipulation; for example, the speeded aspect and the fact that the experimenter remained in the room during the card sorting tasks. One alternative hypothesis, that the manipulation had opposite effects on the groups (i.e., causing increased anxiety in GSADs but increased motivation in NACs), can be ruled out because NACs showed a small but significant increase in anxiety paired with a nonsignificant increase in confidence in response to the alleged competition. Limitations and future directions The task used in this investigation was novel and therefore does not have well-documented psychometric properties. Additionally, a major weakness of this investigation was the lack of a trial-by-trial reaction time measure, which might have yielded additional findings. Although the study revealed differences between GSAD and NAC groups on the emotional interpretation task, there are some limitations to the generalizability of results. Our sample was selected on the basis of high FNE (Watson & Friend, 1969) scores as well as GSAD status. Undoubtedly, future studies should include nonstudent samples of participants to provide results with better external validity than in this investigation. It is also unclear whether the biases found here would also occur in generally anxious individuals or those with disorders other than GSAD. Although the line drawings used as stimuli may have decreased ecological validity as compared to photographic images, perusal of relevant literature indicates that in some cases, facial expressions are better recognized in cartoon than realistic depictions. This is because expressions can be exaggerated beyond displays that a human face is capable of (Calder et al., 2000) and because they eliminate certain idiosyncratic facial features (e.g., freckles, unusual hairline) that might be distracting in experimental paradigms. Another possible limitation of these stimuli was that the faces used in the task were all male, which may have increased threat value but limited generalizability of results to female faces. These limitations aside, results may have relevance to the treatment of GSAD. The negative interpretation bias described herein adds to prior findings using words, sentences, nonverbal cues, and morphed faces as stimuli. Clearly, this interpretation bias is robust among socially anxious individuals and should be a major target of any social anxiety intervention. Discrimination tasks that include training on accurate features of negative statements and facial expressions might be particularly helpful to those who show biased interpretation of neutral or ambiguous social cues, however, in the present study these biases emerged only under threat conditions. This is consistent with research indicating that memory biases for linguistic social cues (e.g., autobiographical information) emerge following threat inductions (Hirsch & Clark, 2004). It might also be beneficial to educate those with social anxiety about the positive bias found among NACs; even when in a threatening context, those who are not socially anxious tend to interpret slightly angry expressions as neutral. This might be reassuring to GSADs who are overly concerned about offending or angering others in social interactions. This study sought to extend what is known about facial interpretation biases in social anxiety. In the threat condition, GSADs evidenced greater accuracy, but also a greater number of misclassification errors (sorting neutral cards to the angry target) as compared to NACs. Interestingly, they showed the opposite pattern when sorting neutral face cards, showing lower accuracy and fewer angry cards misclassified as neutral as compared to NACs. This suggests that individuals with GSAD are not characterized by deficits in recognizing angry faces, nor are they necessarily adept at such tasks. Instead, their expertise is qualified by a tendency to misinterpret neutral facial expressions as angry in conditions of elevated anxiety. References Alvarado, N., & Jameson, K. A. (2002). The relation between emotion terms and components of anger expressions. Motivation and Emotion, 26, 153–182. Beck, A. T., & Steer, R. A. (1987). Beck depression inventory: manual. San Antonio, TX: The Psychiatric Corporation.
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Calder, A. J., Rowland, D., Young, A. W., Nimmo-Smith, I., Keane, J., & Perrett, D. I. (2000). Caricaturing facial expressions. Cognition, 76, 105–146. Curran, J. P. (1977). Skills training as an approach to the treatment of heterosexual social anxiety. Journal of Personality, 84, 140–157. Ekman, P., & Friesen, W. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17, 124–129. Ekman, P., & Friesen, W. (1978). Pictures of facial affect. Palo Alto, CA: Consulting Psychologists Press. Fenigstein, A., Scheier, M., & Buss, A. (1975). Public and private self consciousness: assessment and theory. Consulting and Clinical Psychology, 37, 75–86. First, M. B., Gibbon, M., Spitzer, R. L., & Williams, J. B. W. (1995). Structured clinical interview for DSM-IV Axis I disorders (SCID-I, version 2.0, October 1995, Final Version). Gard, K. A., Gard, G. C., Dossett, D., & Turone, R. (1982). Accuracy in nonverbal communication as affected by trait and state anxiety. Perceptual and Motor Skills, 55, 747–753. Gilboa-Schechtman, E., Foa, E. B., & Amir, N. (1999). Attentional biases for facial expressions in social phobia: The face-in-the-crowd paradigm. Cognition and Emotion, 13, 303–318. Grober, E., & Sliwinski, M. (1991). Development and validation of a model for estimating premorbid verbal intelligence in the elderly. Journal of Clinical and Experimental Neuropsychology, 13, 933–949. Hess, U., Blairy, S., & Kleck, R. E. (1997). The intensity of emotional facial expressions and decoding accuracy. Journal of Nonverbal Behavior, 21, 241–257. Hirsch, C. R., & Clark, D. M. (2004). Information-processing bias in social phobia. Clinical Psychology Review, 24, 799–826. Hjortsjo, C. H. (1969). Man’s face and mimic language. Lund, Sweden: Studentlitteratur. Horstmann, G. (2002). Facial expressions of emotion: Does the prototype represent central tendency, frequency of instantiation, or an ideal? Emotion, 2, 297–305. Mattick, R. P., & Clarke, J. C. (1998). Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour Research and Therapy, 36, 455–470. Mullins, D. T., & Duke, M. P. (2004). Effects of social anxiety on nonverbal accuracy and response time I: Facial expressions. Journal of Nonverbal Behavior, 28, 3–33. Musterle, W., & Rossler, O. E. (1986). Computer faces: The human Lorenz matrix. Biosystems, 19, 61–80. Oei, T. P., Kenna, D., & Evans, L. (1991). The reliability, validity, and utility of the SAD and FNE scales for anxiety disorder patients. Personality and Individual Differences, 12, 111–116. Ohman, A. (1993). Fear and anxiety as emotional phenomenon: Clinical phenomenology, evolutionary perspectives, and information processing mechanisms. In M. Lewis, & J. M. Haviland (Eds.), Handbook of emotions (pp. 511–536). New York: Guilford Press. Pozo, C., Carver, C. S., Wellens, A. R., & Scheier, M. F. (1991). Social anxiety and social perception: Construing others’ reactions to the self. Personality and Social Psychology Bulletin, 17, 355–362. Rapee, R. M., & Heimberg, R. G. (1997). A cognitive-behavioral model of anxiety in social phobia. Behaviour Research and Therapy, 35, 741–756. Rapee, R. M., Craske, M. G., & Barlow, D. H. (1994). Assessment instrument for panic disorder that includes fear of sensation-producing activities: The Albany Panic and Phobia Questionnaire. Anxiety, 1, 114–122. Richards, A., French, C. C., Calder, A. J., Webb, B., Fox, R., & Young, A. W. (2002). Anxiety-related bias in the classification of emotionally ambiguous facial expressions. Emotion, 2, 273–287. Spielberger, C. D., Gorsuch, R. L., & Lushene, R. (1983). State-trait anxiety inventory test manual for form X. Palo Alto, CA: Consulting Psychological Press. Veljaca, K. A., & Rapee, R. M. (1998). Detection of negative and positive audience behaviours by socially anxious subjects. Behaviour Research and Therapy, 36, 311–321. Watson, D., & Friend, R. (1969). Measurement of social-evaluative anxiety. Journal of Consulting and Clinical Psychology, 33, 448–457. Wilson, E., & MacLeod, C. (2003). Contrasting two accounts of anxiety-linked attentional bias: selective attention to varying levels of stimulus intensity. Journal of Abnormal Psychology, 112, 212–218. Winton, E., Clark, D., & Edelmann, R. (1995). Social anxiety, fear of negative evaluation, and the detection of negative emotion in others. Behaviour Research and Therapy, 33, 193–196.
Further reading Turner, S. M., Stanley, M. A., Beidel, D. C., & Bond, L. (1989). The Social Phobia and Anxiety Inventory: Construct validity. Journal of Psychopathology and Behavioral Assessment, 11, 221–234.
Behaviour Research and Therapy 45 (2007) 591–599 www.elsevier.com/locate/brat
Shorter communication
Jumping to interpretations: Social anxiety disorder and the identification of emotional facial expressions Jan Mohlmana,, Cheryl N. Carminb, Rebecca B. Pricea a
Rutgers, the State University of New Jersey, Department of Psychology, 152 Frelinghuysen Road, Piscataway, NJ 08854, USA b University of Illinois at Chicago, USA Received 20 October 2005; received in revised form 12 February 2006; accepted 9 March 2006
Abstract A small body of research suggests that socially anxious individuals show biases in interpreting the facial expressions of others. The current study included a clinically anxious sample in a speeded emotional card-sorting task in two conditions (baseline and threat) to investigate several hypothesized biases in interpretation. Following the threat manipulation, participants with generalized social anxiety disorders (GSADs) sorted angry cards with greater accuracy, but also evidenced a greater rate of neutral cards misclassified as angry, as compared to nonanxious controls. The controls showed the opposite pattern, sorting neutral cards with greater accuracy but also misclassifying a greater proportion of angry cards as neutral, as compared to GSADs. These effects were accounted for primarily by low-intensity angry cards. Results are consistent with previous studies showing a negative interpretive bias, and can be applied to the improvement of clinical interventions. r 2006 Elsevier Ltd. All rights reserved. Keywords: Social anxiety disorder; Emotional faces; Facial interpretation; Interpretive bias
Introduction Accurate ‘‘on-line’’ interpretation of the facial expressions of others is a skill that may facilitate social interaction (Ekman & Friesen, 1971). For instance, when attempting to sustain a lively conversation, we use the emotional displays of others to quickly infer their level of interest in what we have to say. Subsequently, we might adjust the tone or content of our speech whenever a negative emotional display is perceived. If the ability to efficiently and accurately decode human facial expressions is likely to play a role in the development and maintenance of social competence, then an inability to accurately interpret facial expressions may give rise to problems in social functioning (Gard, Gard, Dossett, & Turone, 1982). For example, misinterpreting emotional displays during social interactions can interrupt the flow of conversation, give rise to discomfort and confusion in speakers and listeners, and lead to excessively negative social expectations (Mullins & Duke, 2004; Pozo, Carver, Wellens, & Scheier, 1991). Corresponding author.
E-mail address: [email protected] (J. Mohlman). 0005-7967/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.brat.2006.03.007
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Deficits in this interpretative skill may also be associated with social anxiety disorder as an etiological or maintenance factor. However, the opposite expectation, that socially anxious individuals have special expertise in processing social threat cues, has also been proposed (e.g., Curran, 1977). Others hypothesize that socially anxious individuals are best characterized as having inordinately negative expectations rather than a skills deficit per se, which leads to negative interpretations of some or all emotional cues (e.g., Pozo et al., 1991). Evidence of interpretive biases for facial cues in social anxiety comes mostly from analogue studies. Typically, a threat manipulation is performed in order to maximize mood-congruent interpretive bias in individuals vulnerable to performance anxiety. Most studies have presented stationary facial slide displays and used verbal or nonverbal emotion classification tasks as the dependent measure. For instance, Gard et al. (1982) demonstrated that at baseline, an anxious group was more accurate than a control group at classifying facial slides into six emotion categories; however, the anxious group became less accurate than the controls when the task was performed under stressful conditions, indicating generally impaired performance. Using two forced-choice classification tasks for bimodal emotional displays (negative vs. neutral facial slides; negative vs. positive video clips), Winton, Clark and Edelmann. (1995) reported that high scorers on the Fears of Negative Evaluation Scale (FNE; Watson & Friend, 1969) performed better than low scorers at identifying negative emotional displays, but worse at identifying neutral displays, with a significantly greater number of false positives (neutral displays classified as negative). Signal detection analysis revealed that the group difference was explained by a negative response bias in the high scorers rather than an enhanced ability to detect negative emotional cues. Using a similar forced-choice nonverbal task with four emotion categories (happy, sad, angry and fearful), Mullins and Duke (2004) found a different pattern of results; response times, but not accuracy, were impacted by FNE scores and a threat manipulation. In the no-threat condition, high FNE scorers were slower than low scorers to identify low-intensity angry and sad cues; following a stressor, high scorers were faster to identify high-intensity angry and fearful cues. Richards and colleagues (2002) found that an anxious group categorized significantly more faces as ‘‘fearful’’ than controls, with no significant differences on any other category (angry, happy, sad, disgusted, and surprised). In a follow-up study employing a stress manipulation, the group difference for fearful faces was replicated; additionally, all participants classified more faces as ‘‘angry’’ following the manipulation, and the low scorers showed an increased sensitivity for ‘‘happy’’ faces. This body of work suggests that anxious individuals are more attuned than controls to negative emotional displays, fearful and angry faces in particular. Other studies solicited judgments in the context of an active social interaction. Using a staged interaction task, Pozo et al. (1991) required subjects to pose questions to an ‘‘interaction partner’’ via a two-way television monitor (in reality, the monitor displayed a pre-recorded videotape). High scorers on the Self-Consciousness Scale (SCS; Fenigstein, Scheier, & Buss, 1975) consistently rated lower interest and acceptance on the part of their interaction partner than the lower SCS group, suggesting either a negative expectancy bias in the high group or a positive bias in the low group. Similarly, during a speech giving task, Veljaca and Rapee (1998) reported that high scorers on the Albany Panic and Phobia Questionnaire (Rapee, Craske, & Barlow, 1994) showed increased sensitivity for detecting negative indicators from the audience and decreased sensitivity for detecting positive indicators. The authors also identified a response bias whereby the higher group evidenced a more liberal criterion for judging negative indicators than the lower group. Anxious individuals also seem to pay more attention to negative nonverbal cues in dynamic tasks. In summary, results suggest that individuals who score in the higher range on anxiety questionnaires have a lower threshold than controls (i.e., increased rates of misclassification errors) for identifying negative nonverbal cues, rather than a skills deficit or special expertise in decoding such cues. However, because results have been mixed, additional studies are needed to test this effect in clinically anxious individuals, and to improve upon methodology. The goal of this paper was to extend results from the literature on heightened state anxiety and the interpretative skills of individuals with generalized social anxiety disorder (GSAD) versus nonanxious controls (NACs). This was accomplished through the inclusion of a clinically anxious sample, increased attention paid to aspects of facial stimuli (e.g., graded intensity, standardized, prototypical examples of emotions, straight ahead gaze on all stimuli), an effective threat manipulation, and the use of a timed task, which might be more likely to yield biased results than an untimed task due to the reduced time available for intellectual processing of emotional cues. A categorization task was used in which participants had 90 s to sort a deck of 96 stimulus cards showing 25, 50, or 100% intensity of emotional faces to corresponding emotional target cards (happy, sad, angry, neutral).
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All participants completed the emotional identification task at rest and in a heightened anxiety state. Cards displaying angry faces were chosen as the threat cue of interest because anxious individuals tend to interpret them as threatening (e.g., Gilboa-Schechtman, Foa, & Amir, 1999; Ohman, 1993). A group (GSAD, NAC) by condition (baseline, threat) by valence (happy, sad, angry, neutral facial expressions) interaction was expected in which GSADs would outperform NACs on accurately sorting angry cards, but would perform worse than NACs on sorting neutral cards, following the threat manipulation. Additionally, it was hypothesized that GSADs would misclassify a greater number of neutral cards to the angry target cards than NACs, and NACs would misclassify a greater number of angry cards to the neutral target card than GSADs. These effects were not expected in the baseline condition, as interpretive biases have been related to mood in some prior investigations (e.g., Pozo et al., 1991; Veljaca & Rapee, 1998). The effects of graded stimulus intensity are somewhat difficult to predict. Low-intensity emotional stimuli have been associated with decreased attentional focus (Wilson & MacLeod, 2003), leading ostensibly to incomplete processing and misclassification errors. Consistent with this assumption, some have proposed that low-intensity emotional displays are more difficult to distinguish than high-intensity displays (e.g., Alvarado & Jameson, 2002), and thus more difficult to categorize (e.g., Hess, Blairy, & Kleck, 1997). This may be particularly true in elevated anxiety conditions when attention is divided (e.g., Rapee & Heimberg, 1997) or time is constrained. On the other hand, social anxiety has been associated with selective attention and increased vigilance for negative social cues, especially in heightened anxiety states (e.g., Winton et al., 1995). Thus, it is also possible that GSADs will be more accurate than NACs at identifying negative facial displays in their earlier stages of formation, such as at 25% emotional intensity. Due to these vagaries, hypotheses based on stimulus intensity were somewhat speculative. However, we proposed that if the GSADs were indeed more accurate in sorting cards to angry targets, between-group differences would be most evident at 25%, as compared to 50% and 100% emotional intensities (Alvarado & Jameson, 2002). Method Participants Participants were drawn from a pool of 694 students in undergraduate psychology courses at Syracuse University (Syracuse, NY) between March 2002 and May 2004, who completed the FNE (Watson & Friend, 1969) in mass testing sessions. Those who scored either above 20 or below 9 (the upper and lower quartiles) were contacted by phone and after giving verbal consent, were given the structured clinical interview for DSMIV axis I disorders (SCID; First, Gibbon, Spitzer, & Williams, 1995) by the first author, who had 10 years experience in administering the interview. Although all calls were audiotaped, the sound quality on a subset of tapes ðn ¼ 8Þ was poor. Of the 18 audible tapes, six were randomly chosen (33%) and rated by a second assessor, who was a doctoral student with two years’ training on the SCID. The two raters matched the GSAD diagnosis on five of the six tapes (83%). Those who either met GSAD criteria as a principal diagnosis and scored above 20 on the FNE ðn ¼ 26Þ or did not meet GSAD criteria and scored below 9 on the FNE ðn ¼ 26Þ were included in the study. Means on the FNE were highly similar to those obtained from samples with social anxiety disorder reported in other studies (Oei, Kenna, & Evans, 1991), which increased confidence that our participants were being accurately diagnosed. Of the participants in the GSAD group, for whom GSAD was always the primary diagnosis, four also met criteria for dysthymic disorder, two generalized anxiety disorder, two panic disorder, one major depressive disorder, and six specific phobia. Those in the NAC group did not meet criteria for any other anxiety or mood disorder. w2 tests indicated that the groups were not significantly different on sex ratio, ethnic breakdown, education, marital, or employment status. Measures Participants also completed the Social Phobia Scale (SPS; Mattick & Clarke, 1998), the State Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, & Lushene, 1983), and the Beck Depression Inventory (BDI; Beck & Steer, 1987). All are used frequently in studies of mood and cognition, and demonstrated good psychometric
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properties in the current and prior samples. The Nelson Adult Reading Test—American Version (AMNART; Grober & Sliwinski, 1991) was administered as an estimate of verbal intelligence. Stimuli Stimuli were two decks of 96 300 300 cards featuring computer-generated line drawings of the same face displaying neutral, happy, sad, and angry expressions (Musterle & Rossler, 1986), with 8 cards in each category shown at 25%, 50%, and 100% affective intensities (Horstmann, 2002), for a total of 24 cards depicting each target emotion, and 24 cards showing the same neutral expression. According to Horstmann (2002), images were computer-generated using Ekman and Friesen’s (1978) and Hjortsjo’s (1969) suggestions for muscle coordination of different basic emotional states. Each face was constructed using linear interpolation of a set of values for each emotional display, starting with a neutral face as the basic template. Stimulus decks were arranged in two different orders (a, b), determined by a random number generator. There were four target cards depicting neutral and 100% happy, sad, and angry faces. These particular stimuli were chosen as targets because they had previously received high ratings by Horstmann’s (2002) two independent samples of participants on dimensions of ‘‘typicality’’ as exemplars of target emotions and likelihood that an individual in the target-affective state would demonstrate that specific facial expression (‘‘likelihood’’). Additionally, the face on the cards was standardized (Caucasian dark-haired male) to avoid biases based on the sex, ethnicity or other aspects of appearance. Decks were counterbalanced such that in half of each group of participants, decks a and b were used in the threat, baseline condition on 50% of trials, and in the baseline, threat condition in the remaining trials. Following the card-sorting task, participants rated target stimuli on the dimensions of emotional valence using a seven-point Likert-type scale. The scale ranged from ‘‘1’’ ¼ ‘‘very negative’’ to ‘‘7’’ ¼ ‘‘very positive.’’ Procedure Participants were run on an individual basis in classrooms. After completing the informed consent process, participants were asked to complete the packet of self-report questionnaires. In the baseline condition, which occurred first for 50% of participants in each group, the following instructions were given for the emotional interpretation task: ‘‘The next activity is an emotional IQ task. I am going to ask you to sort each one of the cards in this deck below the target card that you think it matches. You will be matching cards based on the emotion depicted on the face on the card. You are going to be timed, so please sort as quickly as you can, but you must look carefully at the face on each card. Some emotional expressions on the cards will be quite obvious and others will be more subtle and difficult to categorize. For instance (participant was shown sample face depicting varying degrees of disgust), this set of faces depicts the same emotion, disgust, except the emotion on the faces differs in intensity. Do you have any questions on how to complete this task? Please remember to sort as quickly as possible. You may now begin.’’ The experimenter timed each sorting trial with a stopwatch preset to 90 s. During the task the experimenter kept time, facing slightly away from the participant while he or she completed the sorting trials. This was to ensure that instructions for sorting were followed, without eliciting additional anxiety by closely observing the participant’s performance. The threat manipulation, which occurred first for the other 50% of participants, involved deception as a means of producing heightened anxiety. To implement the deception, a confederate (who was actually a research assistant in the first author’s lab) was brought into the room and introduced as another participant in the study. The experimenter said, ‘‘One of the goals of our study is to validate the emotional IQ task by collecting data in two conditions: alone or when you are competing against another participant in our study. The reason for the competition is that we want to increase your motivation to do your best on the task. So if the other participant performs better than you do, then she/he gets an extra course credit. If you perform better than she/he does, then you will receive a second course credit. So whoever does better wins the extra credit. The two
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of you have been paired to compete for this part of our experiment, so one of you will work in here with me and the other will work with our other experimenter in the room next door.’’ Then the confederate always left the room, ostensibly to work with another RA from the study, and the actual participant stayed with the original experimenter. Next, task instructions were given, as shown above. Those who completed the baseline condition second were told, ‘‘Now that you have completed the task in a state of increased motivation, you will be asked to partake in it again, only this time without competing against another subject. This is so that we can assess performance under more naturalistic conditions.’’ After each set of instructions (baseline, threat), participants completed a brief rating scale of mood, which served as a manipulation check. Participants’ ratings of confidence in their ability to perform the task (henceforth called ‘‘confidence’’) and anxiety were made on a seven-point Likert-type scale (1 ¼ ‘‘little confidence’’, ‘‘little anxiety,’’4 ¼ ‘‘neutral,’’ and 7 ¼ ‘‘much confidence’’, ‘‘much anxiety’’). Confidence ratings were collected to check the effects of the manipulation in the NAC group. We wanted to be certain that the manipulation had similar effects across groups (i.e., increasing anxiety without a significant increase in confidence, which would suggest that it led to beneficial motivating effects). Between the trials of the card sorting task, participants engaged in a neutral task (completing crossword puzzles) meant to minimize carryover effects from the threat manipulation. This period lasted 15 min, and participants were told that it was a rest period and a chance for the experimenter to set up the next trial of the task. At the end of the study, all participants were informed about the deception used in the study and given the opportunity to discuss any relevant thoughts or feelings with the experimenter. However, before revealing this, we also asked participants to rate on a seven-point Likert type scale the extent to which they truly believed that they were indeed competing against the confederate in the threat condition (1 ¼ ‘‘didn’t believe it at all,’’ 4 ¼ ‘‘didn’t believe or disbelieve it,’’ and 7 ¼ ‘‘believed it completely’’). Regardless of the outcome, all participants received two credits, and there was no actual competition between participants at any point in the study. Results Self-report questionnaires, stimulus ratings, manipulation check A multivariate analysis of variance (MANOVA) was conducted to test for differences on age, verbal IQ, and self-report measures. The omnibus test was significant, F(1,50) ¼ 16.17, po0.001, Z2 ¼ 0:64. Bonferroniadjusted univariate tests with alpha set at 0.008 (age, verbal IQ, four questionnaires) indicated that GSADs scored significantly higher than NACs on the SPS, F(1,50) ¼ 36.70, po0:001, Z2 ¼ 0:43, the BDI, F(1,50) ¼ 17.19, po0:001, Z2 ¼ 0:26, and the trait scale of the STAI, F(1,50) ¼ 39.06, po0:001, Z2 ¼ 0:44. There were no significant differences between the groups on the state scale of the STAI, age, or verbal I.Q. Descriptive data, stimulus ratings, and results of the manipulation check are shown in Tables 1, 2, and 3, respectively. Emotional card-sorting task To test predictions about interpretive abilities, accuracy data on each stimulus type was compared with repeated measures Group (GSAD, NAC) by Condition (baseline, threat) by Valence (angry, sad, happy, neutral) by Order (Threat, Baseline; Baseline, Threat) MANCOVAs, with proportion of accurately sorted cards entered as the dependent variable. The intensity factor was not run as part of the model because the neutral cards had only one intensity level, whereas the other three types of cards had three intensity levels (25%, 50%, and 100%). BDI, STAI trait scores, and Deck Order were tested as covariates, however, there were no significant effects of these variables in any of the models and they were subsequently omitted. The repeated measures test of the proportion of cards accurately sorted to each target revealed a violation of sphericity assumptions (Mauchley’s test, po0:005), thus the Greenhouse Geisser correction was applied. The test of accuracy yielded significant main effects of Condition, F(1,46) ¼ 16.17, po0:001, Z2 ¼ 0:58, and significant Group by Condition, (F(3,44) ¼ 4.48, po0:05, Z2 ¼ 0:09, Group by Valence (F(3,44) ¼ 20.11, po0:001, Z2 ¼ 0:58, and Condition by Valence interactions, (F(3,44) ¼ 3.00, po0:05, Z2 ¼ 0:17. However, these effects were qualified by a significant Group by Condition by Valence interaction, F(3,44) ¼ 15.77,
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Table 1 Characteristics of the sample
Age Female Education (years) Unmarried Employed Ethnicity Caucasian African American Hispanic/Latino Asian American FNE SPS BDI STAI-state STAI-trait AMNART (verbal IQ)
NAC ðn ¼ 26Þ mean (SD)
GSAD ðn ¼ 26Þ mean (SD)
21.08 (2.52) 65% 13.08 (1.30) 100% 46%
21.46 (2.79) 62% 13.51 (1.31) 100% 54%
81% 15% 2% 2% 4.62 (2.17) 8.80 (5.60) 4.92 (5.31) 28.15 (5.76) 29.50 (8.03) 115.19 (7.86)
88% 8% 0 4% 23.54 (2.21) 20.12 (7.55) 16.00 (12.29) 35.69 (7.26) 45.80 (7.27) 111.94 (8.32)
Note. FNE—Fears of Negative Evaluation Scale (Watson & Friend, 1969), SPS—Social Phobia Scale (Mattick & Clark, 1998), BDI (Beck Depression Inventory, Beck & Steer, 1987), STAI—State Trait Anxiety Inventory (Spielberger, Gorsuch, & Lushene, 1983), and AMNART—American Nelson Adult Reading Test (Grober & Sliwinski, 1991). po0.008.
Table 2 Stimulus valence ratings ðn ¼ 52Þ
Neutral face Sad face Happy face Angry face
3.76 1.53 6.46 1.25
(1.97)a (1.72) (1.80)b (1.68)
Note. Ratings made on 100% intensity target cards, on a 1 to 7 scale, on which 1 ¼ very negative, 2 ¼ negative, 3 ¼ slightly negative, 4 ¼ neutral, 5 ¼ slightly positive, 6 ¼ positive, and 7 ¼ very positive. Ratings were collapsed across groups due to the lack of significant differences. a Significantly different from sad, angry, and happy cards; b Significantly different from sad, angry, and neutral cards.
Table 3 Manipulation check ratings
Baseline anxiety Baseline confidence Threat anxiety Threat confidence Belief in deception
NAC ðn ¼ 26Þ mean (SD)
GSAD ðn ¼ 26Þ mean (SD)
1.42 5.69 2.15 5.90 5.31
2.56 4.96 5.74 4.80 5.71
(1.39) (2.22) (2.31) (2.32) (2.09)
Note. Anxiety, confidence rating scale: 1 ¼ very little, 4 ¼ neutral, and 7 ¼ very much. Groups significantly different at po0.001. a Within group effect on anxiety ratings was significant for both groups.
(1.83) (3.02) (2.70),a (2.87) (2.32)
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Table 4 Proportion of cards sorted correctly to each target card NAC ðn ¼ 26Þ mean (SD)
GSAD ðn ¼ 26Þ mean (SD)
Baseline condition Neutral target Happy target Sad target Angry target
0.95 0.96 0.97 0.94
(0.06) (0.06) (0.05) (0.19)
0.93 0.94 0.95 0.93
(0.07) (0.05) (0.19) (0.08)
Threat condition—accuracy Neutral target Happy target Sad target Angry target
0.96 0.94 0.96 0.80
(0.05) (0.09) (0.10) (0.08)
0.84 0.88 0.89 0.97
(0.06) (0.09) (0.08) (0.05)
Threat condition, accuracy by intensity 25% Angry 50% Angry 100% Angry
0.76 (0.04) 0.89 (0.05) 0.89 (0.06)
0.96 (0.05) 0.94 (0.04) 0.93 (0.05)
Note. Asterisks denote significant between-group effects. po0.01.
po0:001, Z2 ¼ 0:52. Univariate tests (alpha ¼ 0.006) revealed that GSADs sorted a greater number of angry stimulus cards correctly, F(1,50) ¼ 33.74, po0:001, Z2 ¼ 0:41 and fewer neutral cards correctly than NACs, F(1,50) ¼ 34.25, po0:001, Z2 ¼ 0:49, following the threat manipulation. There were no other significant effects. The effect involving the angry target was then investigated further with three between-groups tests of intensity (25, 50, and 100%), which indicated that the effect was accounted for by the 25% intensity stimuli (F(1,50) ¼ 11.59, po0:01, Z2 ¼ 0:49), rather than the 50 or 100% intensities. According to chi square tests, GSADs were more likely (probability ¼ .29, mean 1.58, sd ¼ 1.01) than NACs (probability ¼ .05, mean ¼ 0.15, sd ¼ 0.44) to misclassify neutral cards as angry, and NACs were more likely (probability ¼ .26, mean ¼ 1.54, sd ¼ 1.03) than GSADs (probability ¼ .06, mean ¼ 0.27, sd ¼ 0.67) to misclassify 25% angry cards as neutral, w2 (10, N ¼ 51) ¼ 22.13, po.005, in the threat condition. See Table 4 for descriptive information. Discussion Based on the small body of literature on the interpretive abilities of analogue samples (e.g., Pozo et al., 1991; Veljaca & Rapee, 1998; Winton et al., 1995), we expected that in the threat condition, the GSAD group would sort angry cards more accurately and neutral cards less accurately than NACs. Additionally, it was expected that the GSAD group would evidence more of a specific type of misclassification error (neutral cards sorted to the angry target), revealing a negative interpretive bias. Results were consistent with our predictions for stimuli sorted to the angry target card. It would appear likely that socially anxious individuals are more specifically attuned to threatening stimuli, as there were no effects on the happy or sad cards. As expected, GSADs outperformed NACs in correctly interpreting 25% intensity angry cues as angry in the stressful condition. NACs outperformed GSADs at sorting neutral cards accurately, but showed a tendency to sort 25% angry cards to the neutral target, suggesting a positive bias. Generally, effect sizes ranged from small to medium. These results are somewhat consistent with prior findings (e.g., Pozo et al., 1991; Veljaca & Rapee, 1998; Winton et al., 1995) suggesting that socially anxious participants demonstrate a negative expectancy bias, rather than simply greater accuracy, when interpreting threatening facial expressions. We can rule out response bias as an alternative hypothesis, which would have been characterized by a more general tendency to misclassify cards from other negative emotional categories (i.e., sad) as either angry (GSADs) or neutral (NACs).
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The fact that the GSAD participants responded with more anxiety to the manipulation complicates the interpretation of the present effects, however. It is possible that performance is related to the postmanipulation increase in anxiety in a linear pattern, and that if NACs had responded with as much as anxiety as GSADs, their performance would have been more similar. There may also have been aspects of the study that contributed above and beyond the threat manipulation; for example, the speeded aspect and the fact that the experimenter remained in the room during the card sorting tasks. One alternative hypothesis, that the manipulation had opposite effects on the groups (i.e., causing increased anxiety in GSADs but increased motivation in NACs), can be ruled out because NACs showed a small but significant increase in anxiety paired with a nonsignificant increase in confidence in response to the alleged competition. Limitations and future directions The task used in this investigation was novel and therefore does not have well-documented psychometric properties. Additionally, a major weakness of this investigation was the lack of a trial-by-trial reaction time measure, which might have yielded additional findings. Although the study revealed differences between GSAD and NAC groups on the emotional interpretation task, there are some limitations to the generalizability of results. Our sample was selected on the basis of high FNE (Watson & Friend, 1969) scores as well as GSAD status. Undoubtedly, future studies should include nonstudent samples of participants to provide results with better external validity than in this investigation. It is also unclear whether the biases found here would also occur in generally anxious individuals or those with disorders other than GSAD. Although the line drawings used as stimuli may have decreased ecological validity as compared to photographic images, perusal of relevant literature indicates that in some cases, facial expressions are better recognized in cartoon than realistic depictions. This is because expressions can be exaggerated beyond displays that a human face is capable of (Calder et al., 2000) and because they eliminate certain idiosyncratic facial features (e.g., freckles, unusual hairline) that might be distracting in experimental paradigms. Another possible limitation of these stimuli was that the faces used in the task were all male, which may have increased threat value but limited generalizability of results to female faces. These limitations aside, results may have relevance to the treatment of GSAD. The negative interpretation bias described herein adds to prior findings using words, sentences, nonverbal cues, and morphed faces as stimuli. Clearly, this interpretation bias is robust among socially anxious individuals and should be a major target of any social anxiety intervention. Discrimination tasks that include training on accurate features of negative statements and facial expressions might be particularly helpful to those who show biased interpretation of neutral or ambiguous social cues, however, in the present study these biases emerged only under threat conditions. This is consistent with research indicating that memory biases for linguistic social cues (e.g., autobiographical information) emerge following threat inductions (Hirsch & Clark, 2004). It might also be beneficial to educate those with social anxiety about the positive bias found among NACs; even when in a threatening context, those who are not socially anxious tend to interpret slightly angry expressions as neutral. This might be reassuring to GSADs who are overly concerned about offending or angering others in social interactions. This study sought to extend what is known about facial interpretation biases in social anxiety. In the threat condition, GSADs evidenced greater accuracy, but also a greater number of misclassification errors (sorting neutral cards to the angry target) as compared to NACs. Interestingly, they showed the opposite pattern when sorting neutral face cards, showing lower accuracy and fewer angry cards misclassified as neutral as compared to NACs. This suggests that individuals with GSAD are not characterized by deficits in recognizing angry faces, nor are they necessarily adept at such tasks. Instead, their expertise is qualified by a tendency to misinterpret neutral facial expressions as angry in conditions of elevated anxiety. References Alvarado, N., & Jameson, K. A. (2002). The relation between emotion terms and components of anger expressions. Motivation and Emotion, 26, 153–182. Beck, A. T., & Steer, R. A. (1987). Beck depression inventory: manual. San Antonio, TX: The Psychiatric Corporation.
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Further reading Turner, S. M., Stanley, M. A., Beidel, D. C., & Bond, L. (1989). The Social Phobia and Anxiety Inventory: Construct validity. Journal of Psychopathology and Behavioral Assessment, 11, 221–234.