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Interaction of anger with anxiety and responses to emotional facial expressions
Personality and Individual Differences 50 (2011) 398–403
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Interaction of anger with anxiety and responses to emotional facial expressions Andrey V. Bocharov ⇑, Gennady G. Knyazev Institute of Physiology, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk 630117, Russia
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Article history: Received 12 May 2010 Received in revised form 13 October 2010 Accepted 2 November 2010 Available online 23 November 2010 Keywords: Anger Anxiety Joint-subsystems hypothesis Emotional facial expressions EEG Event-related spectral perturbations
a b s t r a c t In this study, effects of interaction of anger with anxiety on the perception of emotional facial expressions and associated with this perception oscillatory dynamics of cortical responses elicited by presentation of angry, neutral, and happy faces were investigated. Subjects filled out the Buss–Perry aggression scales and the Spielberger’s State Trait Anxiety Inventory. Anxiety moderated the effect of anger both on estimates of angry and happy faces and on face presentation-related spectral perturbations. In the low anxiety group, anger scores were positively related to the extent of face presentation-related theta synchronization. In the high anxiety group this effect was not significant. The results are discussed in light of Corr’s ‘‘joint subsystems’’ hypothesis (Corr, 2002). Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved.
1. Introduction The ability to understand emotional information conveyed by facial expressions of other people is crucial for building interpersonal relationships, career, and, sometimes, even survival (Ellis & Young, 1998). The human face provides the most salient cue to another person’s emotional state. Facial expressions are the unique source of information having social meaning (Bruce & Young, 1986). Existing empirical research shows that some personality traits such as anger and anxiety are associated with biases in the perception of emotionally loaded stimuli, particularly such socially significant stimuli as emotional facial expressions. Individuals scoring high on trait anger showed an attentional bias for angry faces (Putnam, Hermans, & van Honk, 2004; van Honk, Tuiten, de Haan, van den Hout, & Stam, 2001). High anger subjects attributed more hostility to characters of situations (Epps & Kendall, 1995) or more negativity to explanations of events (Wenzel & Lystad, 2005). Participants high on trait anxiety show attentional biases toward threatening information (Derryberry & Reed, 2002; Fox, 2002; Weinstein, 1995). High as opposed to low anxiety subjects show larger negative emotional reaction during presentation of angry faces and rate these faces as more unpleasant and as expressing more disgust (Dimberg & Thunberg, 2007). Previously we have shown that anger and anxiety were associated with a tendency to perceive all facial expressions as more hostile (Knyazev, Bocharov, Slobodskaya, & Ryabichenko, 2008b; Knyazev, Bocharov, & ⇑ Corresponding author. Tel.: +7 383 333 48 65; fax: +7 383 332 42 54. E-mail address: [email protected] (A.V. Bocharov).
Slobodskoj-Plusnin, 2009). These perceptional biases are bound to have some psychophysiological manifestation. However, until recently, few studies took into account personality-related individual differences in the face presentation-related cortical oscillatory responses. We have shown recently that anxiety and anger show opposite effects on face presentation-related oscillatory responses. Anger is associated with increased theta band synchronization and decreased alpha band desynchronization (Knyazev et al., 2009), whereas anxiety shows the opposite pattern of relations (Knyazev, Bocharov, Levin, Savostyanov, & Slobodskoj-Plusnin, 2008a). This is puzzling, because, as we discussed earlier, at perceptional level these traits show similar biases (i.e., exaggeration of aggressiveness and hostility in the facial expressions). From a theoretical perspective, the observed differences in oscillatory dynamics seem more corresponding to behavioural manifestations, because in terms of behavioural outcome, anger and anxiety underlie opposite behavioural tendencies – approach and avoidance behaviour, respectively. One way to try to resolve this puzzle would be to more scrupulously investigate the effects of different combinations of anger and anxiety on the perception of different facial expressions and associated cortical oscillatory responses. The majority of published studies investigated isolated effects of anger or anxiety and, to the best of our knowledge, no studies investigated effects of different combinations of these traits. This is unfortunate, because in reality each individual has some combination of anger and anxiety and his or her behaviour depends on their interaction. Therefore, in this study we aimed to investigate the interaction between anger and anxiety in their influence on the perception of emotional facial expressions and on associated with this
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A.V. Bocharov, G.G. Knyazev / Personality and Individual Differences 50 (2011) 398–403
perception oscillatory cortical responses. Theoretically, there could be two possibilities: (1) the two traits do not interact with each other (i.e., effects of, e.g., anger would be similar on different levels of anxiety); (2) the two traits interact with each other. In the latter case, effects of one trait on the outcome variable would be different on different levels of another trait.
2. Methods 2.1. Subjects The sample included 84 students (41 men and 43 women; age range 17–32 years, mean age 20.9, SD = 2.7) with normal or corrected to normal vision, who received a sum equivalent to about 5% of the living wage for participation. For checking the general health status we used an item from the Achenbach and Rescorla’s (Achenbach & Rescorla, 2003) checklist: ‘‘Whether you have any diseases, physical defects or health infringements? If you have please, describe’’. All included participants reported no diseases. All applicable subject protection guidelines and regulations were followed in the conduct of the research in accordance with the Declaration of Helsinki. All participants gave informed consent to the study. The study has been approved by the Institute of Physiology ethical committee.
2.2. Instruments and procedures Anger (Cronbach’s a = 0.74) was measured by the respective scale of the Buss–Perry aggression questionnaire (Buss & Perry, 1992; Knyazev et al., 2008b). Anxiety (Cronbach’s a = 0.88) was measured by the Spielberger’s State Trait Anxiety Inventory (Hanin, 1989; Spielberger, Gorsuch, & Lushene, 1970). As stimulation we used an ensemble of the photographs presented by Ekman and Friesen (1976). We selected 30 photographs, specifically, 5 different females and 5 different males with 3 different facial expressions (angry, happy, and neutral). The pictures were presented black and white (17 17 cm) and displayed on a screen at a distance of 120 cm from the subjects. The subjects sat in a soundproof and dimly illuminated room. They were instructed to evaluate emotional expression of each presented face on an analogue scale ranging from 100 (very hostile) to 100 (very friendly). First, a fixation cross appeared at the centre of the screen for 1 s. Then a face picture was presented for 4 s, which was followed by presentation of the evaluative scale (Fig. 1). Angry, happy, and neutral faces were delivered randomly, and inter-stimulus-interval randomly varied between 4 and 7 s. The number of face stimulations was 120 for each subject, including 40 faces of each category. In 44 subjects (22 men and 22 women) EEG was recorded during the procedure of faces presentation. Four subjects were excluded, because their EEG was heavily contaminated by movement artifacts. From the remaining 40 subjects (21 females; age range 17–32 years, mean age = 21.3 SD = 3.8) all were right-handed.
Fig. 1. Scheme of one trial. After a fixation cross appeared for 1000 ms, a target stimulus (i.e., angry, neutral, or happy face picture) was presented for 4000 ms. Thereafter an evaluation scale appeared, which was present until the subject marked the degree of hostility-friendliness of the presented face. Between-stimuli interval randomly varied between 4 and 7 s.
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2.3. EEG recording EEG was recorded using a 32-channel PC based system via silver-silver chloride electrodes. A mid-forehead electrode was the ground. The signals were amplified with a multichannel biosignal amplifier with bandpass 0.05–70 Hz, 6 dB/octave and continuously digitized at 300 Hz. The electrodes were placed at 30 head sites according to the International 10–20 system and referred to linked-mastoids. The horizontal and vertical EOG was registered simultaneously. EEG data contaminated with artifact were visually detected and rejected off-line. 1000 ms after face presentation were used as the test interval; 1000 ms prior to the fixation cross presentation served as the pre-stimulus baseline. 2.4. Psychophysiological data reduction 2.4.1. Event-related spectral perturbations (ERSP) To assess face-evoked changes in spectral power, event-related spectral perturbations (ERSP) were calculated using timef function of EEGLAB toolbox. The ERSP (Makeig, 1993) shows mean log event-locked deviations from baseline-mean power at each frequency. The mean value of the spectral power E in a frequency f during the 1000 ms prior to fixation cross presentation was considered as the baseline level and was subtracted from the E(time, f) after face stimulus onset. Method of ERSP calculation realized in EEGLAB toolbox is described in Delorme and Makeig (2004). Time–frequency representations were calculated using Morlet wavelets. 2.5. Data analyses and statistics For each subject, the estimates of faces’ hostility/friendliness were averaged across all face presentations representing face’s gender (i.e., male vs. female faces) and each emotional category (angry vs. neutral vs. happy). Median split was applied to divide subjects into high and low anger and anxiety groups. General Linear Model analysis was used to test the effects of interaction of anger and anxiety as between-subject factor, and emotional category and face’s gender as within-subject factors. Age was entered as a covariate. It is known that multivariate approaches have low sensitivity to regionally specific effects (Friston, 1997). Due to this fact, an alternative to the conventional analysis of variance (ANOVA), the socalled mass-univariate approach is most frequently used for the analysis of neuroimaging data (Worsley et al., 1996). Here we use both the conventional ANOVA analysis and the mass-univariate approach implemented in the statcond function of EEGLAB toolbox. The one-sample Kolmogorov–Smirnov test was used to test that EEG variables were normally distributed. This test showed no deviations from normality for ERSP measures. That enabled us to use parametric tests for hypotheses testing. For conventional ANOVA, for each subject, the net ERSP values were averaged across individual frequency bands of 1–4, 4–8 and 8–12 Hz (to provide the time-varying measures of delta, theta and alpha activities, respectively) and time points of 0–100, 100–200, 200–300, 300–400, and 400–500 ms after stimulus onset. The ERSP values from 30 derivations were averaged for 9 regions to reduce the number of statistical comparisons. These regions were the left frontal (Fp1, F7, F3, FT7, FC3), midline frontal (Fz, FCz), right frontal (Fp2, F8, F4, FT8, FC4), left central (T7, C3, TP7, CP3), midline central (Cz, CPz), right central (T8, C4, TP8, CP4), left posterior (P7, P3, O1), midline posterior (Pz, Oz), and right posterior (P8, P4, O2). Repeated measures ANOVA was conducted to test the effects of interaction of anger and anxiety as between-subject factor, age as covariate, and condition (angry vs. neutral vs. happy
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faces), laterality (left hemisphere vs. midline region vs. right hemisphere), sagittality (frontal vs. central vs. posterior region), and time (5 levels) as within-subject factors. Greenhouse-Geisser correction for sphericity assumption violation was used where necessary. For the mass-univariate analysis the sample was divided into four groups (10 subjects in each) based on median split on anger and anxiety (low anger–low anxiety, low anger–high anxiety, high anger–low anxiety, high anger–high anxiety). The FDR correction for multiple comparisons (Holm, 1979) was applied to reveal areas with significant effects. For FDR correction we employed a q-value threshold of 0.05. 3. Results 3.1. Psychometric measures In this sample (n = 84), mean (SD) for anger was 17.3 (5.3) and for trait anxiety was 41.7 (11.9). The participants of the EEG study (n = 40) have been randomly selected from the larger sample (n = 84). The two groups did not differ significantly on anger (t (84) = 0.44, p = 0.51, d = 0.054) and trait anxiety (t (84) = 0.35, p = 0.727, d = 0.083). 3.2. Effect of anger and anxiety interaction on estimates of emotional facial expressions Repeated measures ANOVA revealed a marginally significant interaction of anger trait anxiety emotional face category (F = 3.7, df = 6.16, p = 0.048, g2 = 0.063). Tests of within-subject contrasts showed that this interaction was significant for happy faces (F = 5.92, df = 6, p = 0.018, g2 = 0.084), was marginal for angry faces (F = 2.99, df = 6, p = 0.089, g2 = 0.053), and was not significant for neutral faces (F = 0.56, df = 6, p = 0.46, g2 = 0.009). Figure 2 shows estimated marginal means of estimates of friendliness for happy (A), neutral (C) faces and of hostility for angry (B) faces in the groups with different combinations of anger and anxiety. As Fig. 2a and b show, in low anxiety subjects, anger is negativity related to estimates of friendliness of happy faces and to estimates of hostility of angry faces. In high anxiety subjects this effect disappears. Subjects with a combination of high anxiety (dashed line) and high anger evaluated neutral faces on a hostility-friendliness scale as near zero (Fig. 2c). Thus, anxiety moderated the effect of anger on estimates of happy and angry facial expressions. The obtained data also show that subjects with higher anger and higher anxiety are most sensitive to the emotional content of the stimuli because they perceived happy faces as more friendly, angry faces as more hostile, and neutral faces as near zero.
3.3. Effects of anger and anxiety interaction on face presentation-related spectral perturbations First, in order to investigate the effect of anger anxiety interaction on ERSP values, repeated measures ANOVA was conducted. The interaction emotional face category laterality sagittality anger trait anxiety was significant for theta (F = 2.47, df = 5.71, p = 0.028, g2 = 0.072), but not for delta (F = 0.71, df = 5.69, p = 0.64, g2 = 0.022) and alpha (F = 1.95, df = 5.61, p = 0.08, g2 = 0.058) bands. In order to localize the observed effects in time and space more precisely, mass-univariate analysis was performed for the theta range of frequencies with the face category (three levels) as a within-subject factor and the four groups as a between-subject factor. This analysis revealed a significant group effect between 310 and 650 ms after stimulus onset that was localized in the central and posterior cortical regions (Fig. 3). In low anxiety subjects, the effect of anger consisted of much stronger theta synchronization in high than in low anger subjects. In high anxiety subjects, this effect was much less pronounced. With the chosen level of FDR correction, there was no significant interaction for emotional face category anger trait anxiety. Without FDR correction, the three-way interaction was significant in two time windows: around 150 ms and around 650 ms post-stimulus onset. The anger trait anxiety interaction was pronounced for happy and angry, but not for neutral faces.
4. Discussion In this study, we have found that anxiety moderates effects of anger on both behavioural and electrophysiological levels. In low anxiety subjects, high anger predisposes to underestimation of both positive and negative emotional stimuli. That is, angry faces are perceived as less hostile and happy faces as less friendly, which imply that a combination of high anger with low anxiety predisposes to lower sensitivity to emotional content. That looks like a contradiction to existing evidence showing that anger generally predisposes to higher emotional reactivity (Ramirez & Andreu, 2006). This evidence is consistent with our previous (Knyazev et al., 2009) and present findings showing that anger is associated with increased face stimuli-related theta synchronization, a valid marker of emotion processing (Aftanas, Reva, Varlamov, Pavlov, & Makhnev, 2004; Aftanas, Varlamov, Pavlov, Makhnev, & Reva, 2001; Krause, Viemero, Rosenqvist, Sillanmaki, & Astrom, 2000). Note that this association of anger with higher emotion processing related theta synchronization is most strongly pronounced in low anxiety participants. It should be borne in mind that experiencing emotion and understanding emotion are two different processes
Fig. 2. Estimated marginal means of estimates of friendliness for happy (A), neutral (C) faces and of hostility for angry (B) faces in high and low anger and high (dashed line) and low (solid line) anxiety scorers.
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Fig. 3. Averaged across all face categories and cortical sites face presentation-related spectral perturbations theta band in groups with different combinations (low and high) anger and anxiety. Areas with no significant between-group differences (p < 0.05 after FDR correction for multiple comparisons) are zeroed out and shown in green. Red color shows synchronization, blue color – desynchronization. Cortical maps at the top of the figure show cortical distribution of most pronounced effects. (For interpretation of the references in color in this figure legend, the reader is referred to the web version of this article.)
that may have different behavioural and physiological correlates. Understanding emotion necessarily includes cognitive components, such as attention to emotional cues and comparison of these cues with retrieved from semantic memory relevant information, whereas emotional reaction per se may omit these components. Actually existing evidence shows that emotional and cognitive processes may be even reciprocally related to each other (e.g., Panksepp, 2003). Thus, conscious, relative to unconscious, emotion processing is associated with greater inhibition of the amygdala (Williams et al., 2006). Furthermore, engagement of the dorsal Anterior Cingulate Cortex, which plays a superordinate role in executive control of attention and motor responses (Lane et al., 1998), differs as a function of individual differences in emotional awareness (Lane, 2008). Individuals who are more emotionally aware are better able to tolerate and consciously process intense emotions than those who are less aware (Kano et al., 2003; Thayer & Lane, 2000). Conversely, individuals functioning at a lower level of emotional awareness are more likely to behave impulsively and be less aware of what they are feeling in the context of high arousal emotions (Lane, 2000). Emotional and cognitive processing has different manifestations in brain oscillatory responses. Whereas emotional processing and related contextual memory operations are
more associated with low frequency (delta and theta bands) synchronization, cognitive processing is more consistently reflected in alpha desynchronization (see Klimesch, 1999; Knyazev, 2007 for review). Noteworthy, increased anger-related low frequency synchronization in response to face stimuli is accompanied by a decrease in alpha band desynchronization (see Fig. 3), which implies lower investment of cognitive resources in high anger subjects. Note that anxiety shows a diametrically opposite pattern of cortical responses, that is, increased alpha desynchronization and decreased theta synchronization, implying that anxious individuals tend to invest more cognitive resources and suppress immediate emotional responding (Knyazev et al., 2008a). Interestingly, effects of interaction of anger with trait anxiety both on estimates of facial expressions and on face presentation-related spectral perturbations were pronounced only for happy and angry, but not for neutral faces, which implies that these effects relate just to emotion and not to sensory processing in general. Thus, the detailed analysis of effects of different combinations of anger and anxiety on the perception of different facial expressions and associated cortical oscillatory responses allows us to suggest that in spite of similar biases in facial affect judgement, anger and anxiety appear to be reciprocally related to each other. It is
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worth noting that this reciprocal relationship is very similar to the one proposed for the Behavioural Approach (BAS) and Behavioural Inhibition (BIS) systems. In Gray’s original theory, the BIS and the BAS are postulated to underlie the personality dimensions of anxiety and impulsivity (Gray, 1987). Although an association of BIS with trait anxiety raises no doubts (McNaughton & Corr, 2004), BAS-related personality trait is still a matter of debate. Recent developments tend to link BAS more to Extraversion than to impulsivity (Depue & Collins, 1999; Smillie, Pickering, & Jackson, 2006). Considerable evidence also links BAS with anger and reactive aggression. The trait anger is characterized by low threshold reactivity with angry feelings being experienced in response to a very wide variety of relatively innocuous triggers, or a more narrow pattern of reactivity to specific classes of stimuli for the person such as competition, rejection, or perceived unfairness (Ramirez & Andreu, 2006).There is evidence that trait anger as measured by Buss–Perry aggression scales (Buss & Perry, 1992) positively correlates with BAS. It has been shown that despite a positive correlation of BIS with anger only BAS correlated with anger when controlling for negative affect (Harmon-Jones, 2004, 2007). Anger has been shown to differently correlate with BAS and BIS depending on its orientation: outside or inside. In the former case it correlated with BAS, in the latter – with BIS (Smits & Kuppens, 2005). Included in Buss–Perry’s anger scale items describe outside anger orientation. Therefore, anger quite rightfully may be treated as a BAS’ proxy. Originally Gray argued that individual differences in the functional capacity of one system are independent of the individual differences in the functional capacity of the other system (Pickering, 1997). But Corr (2002) has put forward a ‘‘joint-subsystems’’ hypothesis of BIS/BAS effects which postulates that the BIS and BAS interact with each other in such a way that activity of one system tends to suppress activity of the other. Generally, the observed in this study interaction of anger with anxiety is in line with the joint-subsystems hypothesis. Acknowledgements The authors are grateful to Helena R. Slobodskaya for her help with translation and adaptation of the Buss–Perry aggression scales. This study was supported by grants of the Russian Foundation for Basic Research (RFBR) Nos. 08-06-00016-A and 08-0600011-A. References Achenbach, T. M., & Rescorla, L. A. (2003). In Manual for the ASEBA adult forms, profiles. Burlington: University of Vermont. Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004). Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans: Temporal and topographic characteristics. Neuroscience and Behavioral Physiology, 34, 859–867. Aftanas, L. I., Varlamov, A. A., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2001). Affective picture processing: Event-related synchronization within individually defined human theta band is modulated by valence dimension. Neuroscience Letters, 303, 115–118. Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77, 305–327. Buss, A. H., & Perry, M. (1992). The aggression questionnaire. Journal of Personality and Social Psychology, 63, 452–459. Corr, P. J. (2002). J.A. Gray’s reinforcement sensitivity theory: Tests of the jointsubsystems hypothesis of anxiety and impulsivity. Personality and Individual Differences, 33, 511–532. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21. Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation, and extraversion. Behavioural and Brain Sciences, 22, 491–569. Derryberry, D., & Reed, M. A. (2002). Anxiety-related attentional biases and their regulation by attentional control. Journal of Abnormal Psychology, 111, 225–236.
Dimberg, U., & Thunberg, M. (2007). Speech anxiety and rapid emotional reactions to angry and happy facial expressions. Scandinavian Journal of Psychology, 48, 321–328. Ekman, P., & Friesen, W. V. (1976). Pictures of facial affect. Palo Alto: Consulting Psychologist Press. Ellis, H. D., & Young, A. W. (1998). Faces in their social and biological context (pp. 67–95). In A. W. Young (Ed.), Face and mind. New York: Oxford University Press. Epps, J., & Kendall, P. C. (1995). Hostile attributional bias in adults. Cognitive Therapy and Research, 19, 159–178. Fox, E. (2002). Processing emotional facial expression the role of anxiety and awareness. Cognitive Affective & Behavioral Neuroscience, 2, 52–63. Friston, K. J. (1997). Testing for anatomical specified regional effects. Human Brain Mapping, 5, 133–136. Gray, J. A. (1987). In The psychology of fear and stress (2nd ed.). Cambridge: Cambridge University Press. Hanin, Y. L. (1989). Cross-cultural perspectives of the individual differences diagnostic. Voprosy Psikhologii, 4, 118–125. Harmon-Jones, E. (2004). Contributions from research on anger and cognitive dissonance to understanding the motivational functions of asymmetrical frontal brain activity. Biological Psychology, 67, 51–76. Harmon-Jones, E. (2007). Trait anger predicts relative left frontal cortical activation to anger-inducing stimuli. International Journal of Psychophysiology, 66, 154–160. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70. Kano, M., Fukudo, S., Gyoba, J., Kamachi, M., Tagawa, M., Mochizuki, H., et al. (2003). Specific brain processing of facial expressions in people with alexithymia: An H2 15O-PET study. Brain, 126, 1474–1484. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance. A review and analysis. Brain Research Reviews, 29, 169–195. Knyazev, G. G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience & Biobehavioral Reviews, 31, 377–395. Knyazev, G. G., Bocharov, A. V., Levin, E. A., Savostyanov, A. N., & Slobodskoj-Plusnin, J. Y. (2008a). Anxiety and oscillatory responses to emotional facial expressions. Brain Research, 1227, 174–188. Knyazev, G. G., Bocharov, A. V., Slobodskaya, H. R., & Ryabichenko, T. I. (2008b). Personality-linked biases in perception of emotional facial expressions. Personality and Individual Differences, 44, 1093–1104. Knyazev, G. G., Bocharov, A. V., & Slobodskoj-Plusnin, J. Y. (2009). Hostility- and gender-related differences in oscillatory responses to emotional facial expressions. Aggressive Behavior, 35, 502–513. Krause, C. M., Viemero, V., Rosenqvist, A., Sillanmaki, L., & Astrom, T. (2000). Relative electroencephalographic desynchronization and synchronization in humans to emotional film content: An analysis of the 4–6 6–8 8–10 and 10–12 Hz frequency bands. Neuroscience Letters, 286, 9–12. Lane, R. D. (2000). Neural correlates of conscious emotional experience (pp. 345– 370). In R. D. Lane, L. Nadel, G. L. Ahern, J. J. B. Allen, A. W. Kaszniak, S. Z. Rapcsak, & G. E. Schwartz (Eds.), Cognitive neuroscience of emotion. New York: Oxford University Press. Lane, R. D. (2008). Neural substrates of implicit and explicit emotional processes: A unifying framework for psychosomatic medicine. Psychosomatic medicine, 70, 214–231. Lane, R. D., Reiman, E. M., Axelrod, B., Yun, L., Holmes, A., & Schwartz, G. E. (1998). Neural correlates of levels of emotional awareness. Evidence of an interaction between emotion and attention in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 10, 525–535. Makeig, S. (1993). Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography and Clinical Neurophysiology, 86, 283–293. McNaughton, N., & Corr, P. J. (2004). A two-dimensional neuropsychology of defense: Fear/anxiety and defensive distance. Neuroscience and Biobehavioral Reviews, 28, 285–305. Panksepp, J. (2003). At the interface of affective, behavioral and cognitive neurosciences. Decoding the emotional feelings of the brain. Brain and Cognition, 52, 4–14. Pickering, A. D. (1997). The conceptual nervous system and personality: From Pavlov to neural network. European Psychologist, 2, 139–163. Putnam, P., Hermans, E., & van Honk, J. (2004). Emotional strop performance for masked angry faces: It’s BAS, not BIS. Emotion, 4, 305–311. Ramirez, J. M., & Andreu, J. M. (2006). Aggression, and some related psychological constructs (anger, hostility, and impulsivity). Some comments from a research project. Neuroscience and Biobehavioral Reviews, 30, 276–291. Smillie, L. D., Pickering, A. D., & Jackson, C. J. (2006). The new reinforcement sensitivity theory: Implications for personality measurement. Personality and Social Psychology Review, 10, 320–335. Smits, D. J. M., & Kuppens, P. (2005). The relations between anger, coping with anger, and aggression, and the BIS/BAS system adolescents. Personality and Individual Differences, 39, 783–793. Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). In Manual for the state-trait anxiety inventory. Palo Alto, CA: Consulting Psychologists Press. Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61, 201–216.
A.V. Bocharov, G.G. Knyazev / Personality and Individual Differences 50 (2011) 398–403 van Honk, J., Tuiten, A., de Haan, E., van den Hout, M., & Stam, H. (2001). Attentional biases for angry faces: Relationships to trait anger and anxiety. Cognition and Emotion, 15, 279–297. Weinstein, A. M. (1995). Visual ERPs evidence for enhanced processing of threatening information in anxious university students. Society of Biological Psychiatry, 37, 847–858. Wenzel, A., & Lystad, C. (2005). Interpretation biases in angry and anxious individuals. Behaviour Research and Therapy, 43, 1045–1054.
403
Williams, L. M., Liddell, B. J., Kemp, A. H., Bryant, R. A., Meares, R., Peduto, A. S., et al. (2006). An amygdala-prefrontal dissociation of subliminal and supraliminal fear. Human Brain Mapping, 27, 652–661. Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach or determining significant signals in images of cerebral activation. Human Brain Mapping, 4, 58–73.
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Interaction of anger with anxiety and responses to emotional facial expressions Andrey V. Bocharov ⇑, Gennady G. Knyazev Institute of Physiology, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk 630117, Russia
a r t i c l e
i n f o
Article history: Received 12 May 2010 Received in revised form 13 October 2010 Accepted 2 November 2010 Available online 23 November 2010 Keywords: Anger Anxiety Joint-subsystems hypothesis Emotional facial expressions EEG Event-related spectral perturbations
a b s t r a c t In this study, effects of interaction of anger with anxiety on the perception of emotional facial expressions and associated with this perception oscillatory dynamics of cortical responses elicited by presentation of angry, neutral, and happy faces were investigated. Subjects filled out the Buss–Perry aggression scales and the Spielberger’s State Trait Anxiety Inventory. Anxiety moderated the effect of anger both on estimates of angry and happy faces and on face presentation-related spectral perturbations. In the low anxiety group, anger scores were positively related to the extent of face presentation-related theta synchronization. In the high anxiety group this effect was not significant. The results are discussed in light of Corr’s ‘‘joint subsystems’’ hypothesis (Corr, 2002). Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved.
1. Introduction The ability to understand emotional information conveyed by facial expressions of other people is crucial for building interpersonal relationships, career, and, sometimes, even survival (Ellis & Young, 1998). The human face provides the most salient cue to another person’s emotional state. Facial expressions are the unique source of information having social meaning (Bruce & Young, 1986). Existing empirical research shows that some personality traits such as anger and anxiety are associated with biases in the perception of emotionally loaded stimuli, particularly such socially significant stimuli as emotional facial expressions. Individuals scoring high on trait anger showed an attentional bias for angry faces (Putnam, Hermans, & van Honk, 2004; van Honk, Tuiten, de Haan, van den Hout, & Stam, 2001). High anger subjects attributed more hostility to characters of situations (Epps & Kendall, 1995) or more negativity to explanations of events (Wenzel & Lystad, 2005). Participants high on trait anxiety show attentional biases toward threatening information (Derryberry & Reed, 2002; Fox, 2002; Weinstein, 1995). High as opposed to low anxiety subjects show larger negative emotional reaction during presentation of angry faces and rate these faces as more unpleasant and as expressing more disgust (Dimberg & Thunberg, 2007). Previously we have shown that anger and anxiety were associated with a tendency to perceive all facial expressions as more hostile (Knyazev, Bocharov, Slobodskaya, & Ryabichenko, 2008b; Knyazev, Bocharov, & ⇑ Corresponding author. Tel.: +7 383 333 48 65; fax: +7 383 332 42 54. E-mail address: [email protected] (A.V. Bocharov).
Slobodskoj-Plusnin, 2009). These perceptional biases are bound to have some psychophysiological manifestation. However, until recently, few studies took into account personality-related individual differences in the face presentation-related cortical oscillatory responses. We have shown recently that anxiety and anger show opposite effects on face presentation-related oscillatory responses. Anger is associated with increased theta band synchronization and decreased alpha band desynchronization (Knyazev et al., 2009), whereas anxiety shows the opposite pattern of relations (Knyazev, Bocharov, Levin, Savostyanov, & Slobodskoj-Plusnin, 2008a). This is puzzling, because, as we discussed earlier, at perceptional level these traits show similar biases (i.e., exaggeration of aggressiveness and hostility in the facial expressions). From a theoretical perspective, the observed differences in oscillatory dynamics seem more corresponding to behavioural manifestations, because in terms of behavioural outcome, anger and anxiety underlie opposite behavioural tendencies – approach and avoidance behaviour, respectively. One way to try to resolve this puzzle would be to more scrupulously investigate the effects of different combinations of anger and anxiety on the perception of different facial expressions and associated cortical oscillatory responses. The majority of published studies investigated isolated effects of anger or anxiety and, to the best of our knowledge, no studies investigated effects of different combinations of these traits. This is unfortunate, because in reality each individual has some combination of anger and anxiety and his or her behaviour depends on their interaction. Therefore, in this study we aimed to investigate the interaction between anger and anxiety in their influence on the perception of emotional facial expressions and on associated with this
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A.V. Bocharov, G.G. Knyazev / Personality and Individual Differences 50 (2011) 398–403
perception oscillatory cortical responses. Theoretically, there could be two possibilities: (1) the two traits do not interact with each other (i.e., effects of, e.g., anger would be similar on different levels of anxiety); (2) the two traits interact with each other. In the latter case, effects of one trait on the outcome variable would be different on different levels of another trait.
2. Methods 2.1. Subjects The sample included 84 students (41 men and 43 women; age range 17–32 years, mean age 20.9, SD = 2.7) with normal or corrected to normal vision, who received a sum equivalent to about 5% of the living wage for participation. For checking the general health status we used an item from the Achenbach and Rescorla’s (Achenbach & Rescorla, 2003) checklist: ‘‘Whether you have any diseases, physical defects or health infringements? If you have please, describe’’. All included participants reported no diseases. All applicable subject protection guidelines and regulations were followed in the conduct of the research in accordance with the Declaration of Helsinki. All participants gave informed consent to the study. The study has been approved by the Institute of Physiology ethical committee.
2.2. Instruments and procedures Anger (Cronbach’s a = 0.74) was measured by the respective scale of the Buss–Perry aggression questionnaire (Buss & Perry, 1992; Knyazev et al., 2008b). Anxiety (Cronbach’s a = 0.88) was measured by the Spielberger’s State Trait Anxiety Inventory (Hanin, 1989; Spielberger, Gorsuch, & Lushene, 1970). As stimulation we used an ensemble of the photographs presented by Ekman and Friesen (1976). We selected 30 photographs, specifically, 5 different females and 5 different males with 3 different facial expressions (angry, happy, and neutral). The pictures were presented black and white (17 17 cm) and displayed on a screen at a distance of 120 cm from the subjects. The subjects sat in a soundproof and dimly illuminated room. They were instructed to evaluate emotional expression of each presented face on an analogue scale ranging from 100 (very hostile) to 100 (very friendly). First, a fixation cross appeared at the centre of the screen for 1 s. Then a face picture was presented for 4 s, which was followed by presentation of the evaluative scale (Fig. 1). Angry, happy, and neutral faces were delivered randomly, and inter-stimulus-interval randomly varied between 4 and 7 s. The number of face stimulations was 120 for each subject, including 40 faces of each category. In 44 subjects (22 men and 22 women) EEG was recorded during the procedure of faces presentation. Four subjects were excluded, because their EEG was heavily contaminated by movement artifacts. From the remaining 40 subjects (21 females; age range 17–32 years, mean age = 21.3 SD = 3.8) all were right-handed.
Fig. 1. Scheme of one trial. After a fixation cross appeared for 1000 ms, a target stimulus (i.e., angry, neutral, or happy face picture) was presented for 4000 ms. Thereafter an evaluation scale appeared, which was present until the subject marked the degree of hostility-friendliness of the presented face. Between-stimuli interval randomly varied between 4 and 7 s.
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2.3. EEG recording EEG was recorded using a 32-channel PC based system via silver-silver chloride electrodes. A mid-forehead electrode was the ground. The signals were amplified with a multichannel biosignal amplifier with bandpass 0.05–70 Hz, 6 dB/octave and continuously digitized at 300 Hz. The electrodes were placed at 30 head sites according to the International 10–20 system and referred to linked-mastoids. The horizontal and vertical EOG was registered simultaneously. EEG data contaminated with artifact were visually detected and rejected off-line. 1000 ms after face presentation were used as the test interval; 1000 ms prior to the fixation cross presentation served as the pre-stimulus baseline. 2.4. Psychophysiological data reduction 2.4.1. Event-related spectral perturbations (ERSP) To assess face-evoked changes in spectral power, event-related spectral perturbations (ERSP) were calculated using timef function of EEGLAB toolbox. The ERSP (Makeig, 1993) shows mean log event-locked deviations from baseline-mean power at each frequency. The mean value of the spectral power E in a frequency f during the 1000 ms prior to fixation cross presentation was considered as the baseline level and was subtracted from the E(time, f) after face stimulus onset. Method of ERSP calculation realized in EEGLAB toolbox is described in Delorme and Makeig (2004). Time–frequency representations were calculated using Morlet wavelets. 2.5. Data analyses and statistics For each subject, the estimates of faces’ hostility/friendliness were averaged across all face presentations representing face’s gender (i.e., male vs. female faces) and each emotional category (angry vs. neutral vs. happy). Median split was applied to divide subjects into high and low anger and anxiety groups. General Linear Model analysis was used to test the effects of interaction of anger and anxiety as between-subject factor, and emotional category and face’s gender as within-subject factors. Age was entered as a covariate. It is known that multivariate approaches have low sensitivity to regionally specific effects (Friston, 1997). Due to this fact, an alternative to the conventional analysis of variance (ANOVA), the socalled mass-univariate approach is most frequently used for the analysis of neuroimaging data (Worsley et al., 1996). Here we use both the conventional ANOVA analysis and the mass-univariate approach implemented in the statcond function of EEGLAB toolbox. The one-sample Kolmogorov–Smirnov test was used to test that EEG variables were normally distributed. This test showed no deviations from normality for ERSP measures. That enabled us to use parametric tests for hypotheses testing. For conventional ANOVA, for each subject, the net ERSP values were averaged across individual frequency bands of 1–4, 4–8 and 8–12 Hz (to provide the time-varying measures of delta, theta and alpha activities, respectively) and time points of 0–100, 100–200, 200–300, 300–400, and 400–500 ms after stimulus onset. The ERSP values from 30 derivations were averaged for 9 regions to reduce the number of statistical comparisons. These regions were the left frontal (Fp1, F7, F3, FT7, FC3), midline frontal (Fz, FCz), right frontal (Fp2, F8, F4, FT8, FC4), left central (T7, C3, TP7, CP3), midline central (Cz, CPz), right central (T8, C4, TP8, CP4), left posterior (P7, P3, O1), midline posterior (Pz, Oz), and right posterior (P8, P4, O2). Repeated measures ANOVA was conducted to test the effects of interaction of anger and anxiety as between-subject factor, age as covariate, and condition (angry vs. neutral vs. happy
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faces), laterality (left hemisphere vs. midline region vs. right hemisphere), sagittality (frontal vs. central vs. posterior region), and time (5 levels) as within-subject factors. Greenhouse-Geisser correction for sphericity assumption violation was used where necessary. For the mass-univariate analysis the sample was divided into four groups (10 subjects in each) based on median split on anger and anxiety (low anger–low anxiety, low anger–high anxiety, high anger–low anxiety, high anger–high anxiety). The FDR correction for multiple comparisons (Holm, 1979) was applied to reveal areas with significant effects. For FDR correction we employed a q-value threshold of 0.05. 3. Results 3.1. Psychometric measures In this sample (n = 84), mean (SD) for anger was 17.3 (5.3) and for trait anxiety was 41.7 (11.9). The participants of the EEG study (n = 40) have been randomly selected from the larger sample (n = 84). The two groups did not differ significantly on anger (t (84) = 0.44, p = 0.51, d = 0.054) and trait anxiety (t (84) = 0.35, p = 0.727, d = 0.083). 3.2. Effect of anger and anxiety interaction on estimates of emotional facial expressions Repeated measures ANOVA revealed a marginally significant interaction of anger trait anxiety emotional face category (F = 3.7, df = 6.16, p = 0.048, g2 = 0.063). Tests of within-subject contrasts showed that this interaction was significant for happy faces (F = 5.92, df = 6, p = 0.018, g2 = 0.084), was marginal for angry faces (F = 2.99, df = 6, p = 0.089, g2 = 0.053), and was not significant for neutral faces (F = 0.56, df = 6, p = 0.46, g2 = 0.009). Figure 2 shows estimated marginal means of estimates of friendliness for happy (A), neutral (C) faces and of hostility for angry (B) faces in the groups with different combinations of anger and anxiety. As Fig. 2a and b show, in low anxiety subjects, anger is negativity related to estimates of friendliness of happy faces and to estimates of hostility of angry faces. In high anxiety subjects this effect disappears. Subjects with a combination of high anxiety (dashed line) and high anger evaluated neutral faces on a hostility-friendliness scale as near zero (Fig. 2c). Thus, anxiety moderated the effect of anger on estimates of happy and angry facial expressions. The obtained data also show that subjects with higher anger and higher anxiety are most sensitive to the emotional content of the stimuli because they perceived happy faces as more friendly, angry faces as more hostile, and neutral faces as near zero.
3.3. Effects of anger and anxiety interaction on face presentation-related spectral perturbations First, in order to investigate the effect of anger anxiety interaction on ERSP values, repeated measures ANOVA was conducted. The interaction emotional face category laterality sagittality anger trait anxiety was significant for theta (F = 2.47, df = 5.71, p = 0.028, g2 = 0.072), but not for delta (F = 0.71, df = 5.69, p = 0.64, g2 = 0.022) and alpha (F = 1.95, df = 5.61, p = 0.08, g2 = 0.058) bands. In order to localize the observed effects in time and space more precisely, mass-univariate analysis was performed for the theta range of frequencies with the face category (three levels) as a within-subject factor and the four groups as a between-subject factor. This analysis revealed a significant group effect between 310 and 650 ms after stimulus onset that was localized in the central and posterior cortical regions (Fig. 3). In low anxiety subjects, the effect of anger consisted of much stronger theta synchronization in high than in low anger subjects. In high anxiety subjects, this effect was much less pronounced. With the chosen level of FDR correction, there was no significant interaction for emotional face category anger trait anxiety. Without FDR correction, the three-way interaction was significant in two time windows: around 150 ms and around 650 ms post-stimulus onset. The anger trait anxiety interaction was pronounced for happy and angry, but not for neutral faces.
4. Discussion In this study, we have found that anxiety moderates effects of anger on both behavioural and electrophysiological levels. In low anxiety subjects, high anger predisposes to underestimation of both positive and negative emotional stimuli. That is, angry faces are perceived as less hostile and happy faces as less friendly, which imply that a combination of high anger with low anxiety predisposes to lower sensitivity to emotional content. That looks like a contradiction to existing evidence showing that anger generally predisposes to higher emotional reactivity (Ramirez & Andreu, 2006). This evidence is consistent with our previous (Knyazev et al., 2009) and present findings showing that anger is associated with increased face stimuli-related theta synchronization, a valid marker of emotion processing (Aftanas, Reva, Varlamov, Pavlov, & Makhnev, 2004; Aftanas, Varlamov, Pavlov, Makhnev, & Reva, 2001; Krause, Viemero, Rosenqvist, Sillanmaki, & Astrom, 2000). Note that this association of anger with higher emotion processing related theta synchronization is most strongly pronounced in low anxiety participants. It should be borne in mind that experiencing emotion and understanding emotion are two different processes
Fig. 2. Estimated marginal means of estimates of friendliness for happy (A), neutral (C) faces and of hostility for angry (B) faces in high and low anger and high (dashed line) and low (solid line) anxiety scorers.
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Fig. 3. Averaged across all face categories and cortical sites face presentation-related spectral perturbations theta band in groups with different combinations (low and high) anger and anxiety. Areas with no significant between-group differences (p < 0.05 after FDR correction for multiple comparisons) are zeroed out and shown in green. Red color shows synchronization, blue color – desynchronization. Cortical maps at the top of the figure show cortical distribution of most pronounced effects. (For interpretation of the references in color in this figure legend, the reader is referred to the web version of this article.)
that may have different behavioural and physiological correlates. Understanding emotion necessarily includes cognitive components, such as attention to emotional cues and comparison of these cues with retrieved from semantic memory relevant information, whereas emotional reaction per se may omit these components. Actually existing evidence shows that emotional and cognitive processes may be even reciprocally related to each other (e.g., Panksepp, 2003). Thus, conscious, relative to unconscious, emotion processing is associated with greater inhibition of the amygdala (Williams et al., 2006). Furthermore, engagement of the dorsal Anterior Cingulate Cortex, which plays a superordinate role in executive control of attention and motor responses (Lane et al., 1998), differs as a function of individual differences in emotional awareness (Lane, 2008). Individuals who are more emotionally aware are better able to tolerate and consciously process intense emotions than those who are less aware (Kano et al., 2003; Thayer & Lane, 2000). Conversely, individuals functioning at a lower level of emotional awareness are more likely to behave impulsively and be less aware of what they are feeling in the context of high arousal emotions (Lane, 2000). Emotional and cognitive processing has different manifestations in brain oscillatory responses. Whereas emotional processing and related contextual memory operations are
more associated with low frequency (delta and theta bands) synchronization, cognitive processing is more consistently reflected in alpha desynchronization (see Klimesch, 1999; Knyazev, 2007 for review). Noteworthy, increased anger-related low frequency synchronization in response to face stimuli is accompanied by a decrease in alpha band desynchronization (see Fig. 3), which implies lower investment of cognitive resources in high anger subjects. Note that anxiety shows a diametrically opposite pattern of cortical responses, that is, increased alpha desynchronization and decreased theta synchronization, implying that anxious individuals tend to invest more cognitive resources and suppress immediate emotional responding (Knyazev et al., 2008a). Interestingly, effects of interaction of anger with trait anxiety both on estimates of facial expressions and on face presentation-related spectral perturbations were pronounced only for happy and angry, but not for neutral faces, which implies that these effects relate just to emotion and not to sensory processing in general. Thus, the detailed analysis of effects of different combinations of anger and anxiety on the perception of different facial expressions and associated cortical oscillatory responses allows us to suggest that in spite of similar biases in facial affect judgement, anger and anxiety appear to be reciprocally related to each other. It is
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worth noting that this reciprocal relationship is very similar to the one proposed for the Behavioural Approach (BAS) and Behavioural Inhibition (BIS) systems. In Gray’s original theory, the BIS and the BAS are postulated to underlie the personality dimensions of anxiety and impulsivity (Gray, 1987). Although an association of BIS with trait anxiety raises no doubts (McNaughton & Corr, 2004), BAS-related personality trait is still a matter of debate. Recent developments tend to link BAS more to Extraversion than to impulsivity (Depue & Collins, 1999; Smillie, Pickering, & Jackson, 2006). Considerable evidence also links BAS with anger and reactive aggression. The trait anger is characterized by low threshold reactivity with angry feelings being experienced in response to a very wide variety of relatively innocuous triggers, or a more narrow pattern of reactivity to specific classes of stimuli for the person such as competition, rejection, or perceived unfairness (Ramirez & Andreu, 2006).There is evidence that trait anger as measured by Buss–Perry aggression scales (Buss & Perry, 1992) positively correlates with BAS. It has been shown that despite a positive correlation of BIS with anger only BAS correlated with anger when controlling for negative affect (Harmon-Jones, 2004, 2007). Anger has been shown to differently correlate with BAS and BIS depending on its orientation: outside or inside. In the former case it correlated with BAS, in the latter – with BIS (Smits & Kuppens, 2005). Included in Buss–Perry’s anger scale items describe outside anger orientation. Therefore, anger quite rightfully may be treated as a BAS’ proxy. Originally Gray argued that individual differences in the functional capacity of one system are independent of the individual differences in the functional capacity of the other system (Pickering, 1997). But Corr (2002) has put forward a ‘‘joint-subsystems’’ hypothesis of BIS/BAS effects which postulates that the BIS and BAS interact with each other in such a way that activity of one system tends to suppress activity of the other. Generally, the observed in this study interaction of anger with anxiety is in line with the joint-subsystems hypothesis. Acknowledgements The authors are grateful to Helena R. Slobodskaya for her help with translation and adaptation of the Buss–Perry aggression scales. This study was supported by grants of the Russian Foundation for Basic Research (RFBR) Nos. 08-06-00016-A and 08-0600011-A. References Achenbach, T. M., & Rescorla, L. A. (2003). In Manual for the ASEBA adult forms, profiles. Burlington: University of Vermont. Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004). Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans: Temporal and topographic characteristics. Neuroscience and Behavioral Physiology, 34, 859–867. Aftanas, L. I., Varlamov, A. A., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2001). Affective picture processing: Event-related synchronization within individually defined human theta band is modulated by valence dimension. Neuroscience Letters, 303, 115–118. Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77, 305–327. Buss, A. H., & Perry, M. (1992). The aggression questionnaire. Journal of Personality and Social Psychology, 63, 452–459. Corr, P. J. (2002). J.A. Gray’s reinforcement sensitivity theory: Tests of the jointsubsystems hypothesis of anxiety and impulsivity. Personality and Individual Differences, 33, 511–532. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21. Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation, and extraversion. Behavioural and Brain Sciences, 22, 491–569. Derryberry, D., & Reed, M. A. (2002). Anxiety-related attentional biases and their regulation by attentional control. Journal of Abnormal Psychology, 111, 225–236.
Dimberg, U., & Thunberg, M. (2007). Speech anxiety and rapid emotional reactions to angry and happy facial expressions. Scandinavian Journal of Psychology, 48, 321–328. Ekman, P., & Friesen, W. V. (1976). Pictures of facial affect. Palo Alto: Consulting Psychologist Press. Ellis, H. D., & Young, A. W. (1998). Faces in their social and biological context (pp. 67–95). In A. W. Young (Ed.), Face and mind. New York: Oxford University Press. Epps, J., & Kendall, P. C. (1995). Hostile attributional bias in adults. Cognitive Therapy and Research, 19, 159–178. Fox, E. (2002). Processing emotional facial expression the role of anxiety and awareness. Cognitive Affective & Behavioral Neuroscience, 2, 52–63. Friston, K. J. (1997). Testing for anatomical specified regional effects. Human Brain Mapping, 5, 133–136. Gray, J. A. (1987). In The psychology of fear and stress (2nd ed.). Cambridge: Cambridge University Press. Hanin, Y. L. (1989). Cross-cultural perspectives of the individual differences diagnostic. Voprosy Psikhologii, 4, 118–125. Harmon-Jones, E. (2004). Contributions from research on anger and cognitive dissonance to understanding the motivational functions of asymmetrical frontal brain activity. Biological Psychology, 67, 51–76. Harmon-Jones, E. (2007). Trait anger predicts relative left frontal cortical activation to anger-inducing stimuli. International Journal of Psychophysiology, 66, 154–160. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70. Kano, M., Fukudo, S., Gyoba, J., Kamachi, M., Tagawa, M., Mochizuki, H., et al. (2003). Specific brain processing of facial expressions in people with alexithymia: An H2 15O-PET study. Brain, 126, 1474–1484. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance. A review and analysis. Brain Research Reviews, 29, 169–195. Knyazev, G. G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience & Biobehavioral Reviews, 31, 377–395. Knyazev, G. G., Bocharov, A. V., Levin, E. A., Savostyanov, A. N., & Slobodskoj-Plusnin, J. Y. (2008a). Anxiety and oscillatory responses to emotional facial expressions. Brain Research, 1227, 174–188. Knyazev, G. G., Bocharov, A. V., Slobodskaya, H. R., & Ryabichenko, T. I. (2008b). Personality-linked biases in perception of emotional facial expressions. Personality and Individual Differences, 44, 1093–1104. Knyazev, G. G., Bocharov, A. V., & Slobodskoj-Plusnin, J. Y. (2009). Hostility- and gender-related differences in oscillatory responses to emotional facial expressions. Aggressive Behavior, 35, 502–513. Krause, C. M., Viemero, V., Rosenqvist, A., Sillanmaki, L., & Astrom, T. (2000). Relative electroencephalographic desynchronization and synchronization in humans to emotional film content: An analysis of the 4–6 6–8 8–10 and 10–12 Hz frequency bands. Neuroscience Letters, 286, 9–12. Lane, R. D. (2000). Neural correlates of conscious emotional experience (pp. 345– 370). In R. D. Lane, L. Nadel, G. L. Ahern, J. J. B. Allen, A. W. Kaszniak, S. Z. Rapcsak, & G. E. Schwartz (Eds.), Cognitive neuroscience of emotion. New York: Oxford University Press. Lane, R. D. (2008). Neural substrates of implicit and explicit emotional processes: A unifying framework for psychosomatic medicine. Psychosomatic medicine, 70, 214–231. Lane, R. D., Reiman, E. M., Axelrod, B., Yun, L., Holmes, A., & Schwartz, G. E. (1998). Neural correlates of levels of emotional awareness. Evidence of an interaction between emotion and attention in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 10, 525–535. Makeig, S. (1993). Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography and Clinical Neurophysiology, 86, 283–293. McNaughton, N., & Corr, P. J. (2004). A two-dimensional neuropsychology of defense: Fear/anxiety and defensive distance. Neuroscience and Biobehavioral Reviews, 28, 285–305. Panksepp, J. (2003). At the interface of affective, behavioral and cognitive neurosciences. Decoding the emotional feelings of the brain. Brain and Cognition, 52, 4–14. Pickering, A. D. (1997). The conceptual nervous system and personality: From Pavlov to neural network. European Psychologist, 2, 139–163. Putnam, P., Hermans, E., & van Honk, J. (2004). Emotional strop performance for masked angry faces: It’s BAS, not BIS. Emotion, 4, 305–311. Ramirez, J. M., & Andreu, J. M. (2006). Aggression, and some related psychological constructs (anger, hostility, and impulsivity). Some comments from a research project. Neuroscience and Biobehavioral Reviews, 30, 276–291. Smillie, L. D., Pickering, A. D., & Jackson, C. J. (2006). The new reinforcement sensitivity theory: Implications for personality measurement. Personality and Social Psychology Review, 10, 320–335. Smits, D. J. M., & Kuppens, P. (2005). The relations between anger, coping with anger, and aggression, and the BIS/BAS system adolescents. Personality and Individual Differences, 39, 783–793. Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). In Manual for the state-trait anxiety inventory. Palo Alto, CA: Consulting Psychologists Press. Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61, 201–216.
A.V. Bocharov, G.G. Knyazev / Personality and Individual Differences 50 (2011) 398–403 van Honk, J., Tuiten, A., de Haan, E., van den Hout, M., & Stam, H. (2001). Attentional biases for angry faces: Relationships to trait anger and anxiety. Cognition and Emotion, 15, 279–297. Weinstein, A. M. (1995). Visual ERPs evidence for enhanced processing of threatening information in anxious university students. Society of Biological Psychiatry, 37, 847–858. Wenzel, A., & Lystad, C. (2005). Interpretation biases in angry and anxious individuals. Behaviour Research and Therapy, 43, 1045–1054.
403
Williams, L. M., Liddell, B. J., Kemp, A. H., Bryant, R. A., Meares, R., Peduto, A. S., et al. (2006). An amygdala-prefrontal dissociation of subliminal and supraliminal fear. Human Brain Mapping, 27, 652–661. Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach or determining significant signals in images of cerebral activation. Human Brain Mapping, 4, 58–73.