The relationship between alexithymia, hostile attribution bias, and aggression

Personality and Individual Differences 159 (2020) 109869

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The relationship between alexithymia, hostile attribution bias, and aggression

T

Xu Lia, Bingbing Lia, , Jiamei Lua, , Li Jina, Juan Xueb, Xianwei Chec ⁎

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a

Educational College, Shanghai Normal University, 100 Guilin Road, Xuhui District, Shanghai 200234,China Chongqing Three Gorges University, Chongqing 404199, China c Monash Alfred Psychiatry Research Centre (MAPrc), The Alfredand Central Clinical School, Monash University, Level 4, 607 StKilda Road, Melbourne VIC 3004, Australia b

ARTICLE INFO

ABSTRACT

Keywords: Alexithymia Difficulty identifying feelings Externally oriented thinking Hostile attribution bias Proactive aggression Reactive aggression

Prior studies found a positive association between alexithymia and aggression. This study extended existing findings by adopting a subdimensional approach to investigate the unique association of alexithymia factors with reactive and proactive aggression, such as difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT). This study also explored factors associated with the link between alexithymia and aggression by investigating the potential moderating role of hostile attribution bias (HAB). Four hundred and eighty-five college students (275 females) were recruited. Results showed that alexithymia was positively associated with aggression; however, different alexithymia factors did not show equally strong associations with aggression subtypes. Specifically, DIF (in contrast to DDF and EOT) was the strongest correlate of reactive aggression, while EOT (in contrast to DIF and DDF) was the strongest correlate of proactive aggression. Meanwhile, HAB was a moderator for the relationship between alexithymia and aggression, but not between alexithymia factors and aggression subtypes. Implications and limitations of the present study are discussed.

1. Introduction Aggression includes any behavior that involves intentional infliction of harm to others (Anderson & Bushman, 2002), such as robbery, assault, and hurting or bullying others. The rate of aggression among college students remains high, despite increasing efforts to curtail these behaviors (Leonard, Quigley & Collins, 2002; Tremblay, Graham & Wells, 2008). Recent studies have indicated that individuals who initiated attacks and those who reported being the target are more prone to mental health problems and behavioral disorders (Ming et al., 2011; Yang et al., 2012). Therefore, it is a crucial step to identify and address factors associated with aggression to develop effective preventive strategies. 1.1. Alexithymia and aggression Alexithymia may be an important emotional personality predictor of aggression. Alexithymia represents a dimensional emotion-processing deficit comprised of three factors: difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT) (Bagby, Taylor & Parker, 1994). DIF refers to deficits

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associated with recognizing, interpreting and distinguishing internal signals. DDF refers to a reduction in the ability to communicate feelings, and EOT refers to a thinking style with a preference for external stimuli rather than internal experiences. Corresponding to the three primary characteristics of alexithymia, the 20-item Toronto Alexithymia Scale (TAS-20; Bagby et al., 1994), which is a widely used measure of alexithymia (Taylor, 2000), contains these three dimensions. Although difficulty in processing emotional signals of others’ is not part of the definition, alexithymic individuals appear to be characterized by limited abilities to read others’ emotions, intentions, and desires (e.g., Lyvers, Kohlsdorf, Edwards & Thorberg, 2017). These deficits in emotion-processing may increase the likelihood of an individual engaging in aggressive responses. For instance, researchers suggested that deficits in emotion perception could be an important component for the development of antisocial behavior (Bowen & Dixon, 2010). This point was reiterated by Roberton, Daffern and Bucks (2015), who suggested that the deficits in attending to, and communicating, negative emotions are responsible for the increased risk of aggression in adult criminal offenders, rather than the negative emotions alone. Cohn, Seibert, and Zeichner (2009) suggested that to express and terminate internal distress, men may resort to destructive

Corresponding authors. E-mail addresses: [email protected] (B. Li), [email protected] (J. Lu).

https://doi.org/10.1016/j.paid.2020.109869 Received 4 May 2019; Received in revised form 9 December 2019; Accepted 27 January 2020 0191-8869/ © 2020 Elsevier Ltd. All rights reserved.

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externalizing behaviors if they have difficulty expressing, sharing, or displaying their emotions in masculine-relevant threatening situations. A direct association between alexithymia and aggression has also been demonstrated among different populations, spanning from community samples, to clinical populations (e.g., Velotti et al., 2016). Despite the findings mentioned, further studies are needed to improve our general understanding about the correlation between alexithymia and aggression. The reasons are as follows.

encoding (i.e. ‘what happened’, (2) interpreting (i.e. ‘why it happened’, (3) possible goals selection (e.g., maintain relationship or terminate intrusion), (4) response selection, (5) evaluation of possible responses, and (6) selecting the most positively evaluated response. Deficits or biases occurring in any of these stages can contribute to aggression. HAB could reflect a deficit in the second stage where the information is being interpreted. A recent meta-analysis showed that HAB, a widely studied construct in the SIP model, was positively associated with aggressive behaviors (De Castro et al., 2002). In contrast to alexithymia, as an emotional personality variable, HAB is an important cognitive personality variable that has been linked to aggression. Based on the SIP model, Lmerise and Arsenio (2000) proposed a revised model that integrated temperament and emotional factors into the previous cognitive model, to expand the model's explanatory power. According to this amended model, cognitive and affective factors may interact and culminate in aggression (Lemerise & Arsenio, 2000). Although in the integrated model, it is not well articulated how different factors interact. Researchers have demonstrated that HAB is an important moderator for aggression socialization (Molano, Jones, Brown & Aber, 2013). Meanwhile, Konrath and colleagues (2012) investigated how alexithymia interacts with partner characteristics (in-group vs. out-group) in predicting aggression. Results showed that the effect of alexithymia on aggression was only found after interacting with out-group (i.e., different religious or political backgrounds) members. One possible reason is that high-level alexithymic individuals might be elicited higher levels of threat perceptions in the out-group condition than low-level alexithymic individuals. No such differences emerged in the in-group condition (Konrath et al., 2012). As such, perceptions of threat may strengthen the association between alexithymia and aggression. Therefore, we supposed that HAB, which represents an individual's tendency to perceive threat, betrayal, and hurt in interpersonal situations (Coccaro, Noblett & Mccloskey, 2009), may serve as a moderator. We specifically expected that the link between alexithymia and aggression would be stronger for individuals with relatively higher rather than lower levels of HAB.

1.2. A sub-dimensional approach Alexithymia is regarded as a heterogeneous and dimensional structure (e.g., Kajanoja, Scheinin, Karlsson, Karlsson & Karukivi, 2017). The three dimensions of alexithymia showed several important distinctions. To begin with, EOT is conceptually distinct from DIF and DDF. Specifically, EOT may be linked to emotional deficits but is not defined as a deficit per se, whereas DIF and DDF are conceptualized as deficit-based components (Bagby et al., 1994). Meanwhile, DIF and DDF tend to show stronger associations (as compared to EOT) with a wide range of relational and emotional difficulties (e.g., Paivio & McCulloch, 2004; Shishido, Gaher & Simons, 2013; Spitzer, SiebelJürges, Barnow, Grabe & Freyberger, 2005). Therefore, an increasing number of researchers encourage the analysis of alexithymia total scores as well as individual subscales scores (e.g., Kajanoja et al., 2017). Individual facets of alexithymia may differentially relate to the various subtypes of aggression. Based on the motivations and functions, aggression is divided into two categories: reactive aggression (RA) and proactive aggression (PA) (Dodge & Coie, 1987). RA occurs as a defensive response to perceived threat, provocation, or frustration, while PA is considered to be an offensive, instrumental behavior conducted in anticipation of some benefits (Dodge & Coie, 1987). On one hand, DIF has been suggested to be a strong predictor of impulse control problems, such as problem gambling and compulsive buying, while DDF and EOT were not (Mitrovic & Brown, 2009; Rose, 2012). Therefore, it could be speculated that among alexithymia factors, DIF may be the strongest correlate of reactive aggression, a construct that can also be defined as impulsive aggression (Stanford et al., 2003). On the other hand, in contrast to DIF and DDF, EOT is uniquely related to certain traits characterized by callousness (i.e., lack of empathy or guilt) and fear insensitivity (Essau, Sasagawa & Frick, 2006; Karpman, 1941). While callousness and insensitivity (e.g., underestimate the probability of being punished) have been suggested as predictors of proactive aggression, they were not predictors of reactive aggression (Marsee & Frick, 2007). Therefore, among alexithymia factors, EOT may be the strongest correlate of proactive aggression. In sum, the three alexithymia factors may not show equally strong associations with reactive and proactive aggression. However, previous research has consistently demonstrated that alexithymia was positively associated with impulsive or reactive aggression (Edwards & Wupperman, 2017; Fossati et al., 2009; Teten, Miller, Bailey, Dunn, & Kent, 2008). Few studies had examined the association between alexithymia and proactive aggression, even fewer the associations between alexithymia factors and aggression subtypes. Therefore, studies adopting a sub-dimensional approach are needed.

1.4. The present study The present study aimed to obtain more precise information about the correlation between alexithymia and aggression. On one hand, this study adopted a sub-dimensional approach to explore the unique relationship between different dimensions of alexithymia and subtypes of aggression. It was hypothesized that the three alexithymia factors would not show equally strong associations with reactive aggression and proactive aggression (hypothesis 1). Specifically, DIF would show a stronger association with reactive aggression than DDF or EOT, while EOT would show a stronger association with proactive aggression than DIF or DDF. On the other hand, the present study investigated how alexithymia and HAB interact, contributing to an increase in aggression. It was hypothesized that HAB would moderate the relationship between alexithymia and aggression (hypothesis 2). Given the limited empirical research on the moderating role of HAB in the relationship between alexithymia dimensions and aggression subtypes, no specific hypothesis was formulated. We tested these hypotheses among college students, a sample who is experiencing transitional periods with a high risk for aggression (Leonard et al., 2002; Tremblay et al., 2008).

1.3. Moderating role of hostile attribution bias (HAB) There is an increasing emphasis on exploring the interactive effects of risk factors contributing to aggression (Anderson and Bushman, 2002; Konrath, Novin & Li, 2012). In addition to alexithymia, HAB is an important individual factor related to aggression (De Castro, Veerman, Koops, Bosch & Monshouwer, 2002). HAB refers to the tendency to interpret the actions of the provocateur as intentionally hostile, even in ambiguous situations (Dodge & Somberg, 1987). According to the Social Information Processing (SIP) model (Crick & Dodge, 1994), information processing is comprised of six stages: (1) information

2. Method 2.1. Participants and procedure The study sample included 485 college students (275 females, 56.70%) with a mean age of 20.39 (standard deviation (SD) = 1.67, range from 17 to 27 years). The university's institutional review board provided ethics approval for this study. All participants provided 2

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written informed consent and participation was completely voluntary. Every participant in the present study completed the following psychological instruments, including the Chinese version (Zhu et al., 2007) of TAS-20, the Social Information Processing–Attribution Bias Questionnaire (SIP-ABQ; Coccaro et al., 2009), and the Chinese version (Zhang, Jia, Chen & Zhang, 2014) of the Reactive-Proactive Aggression Questionnaire (RPQ) (Raine et al., 2006).

scale was 0.93, the reactive aggression subscale was 0.866, and the proactive aggression subscale was 0.94. 2.3. Statistical analysis The data were analyzed using SPSS 22.0 (IBM Corporation, Armonk, USA). Descriptive statistics and correlation analyses were performed to characterize the data. The PROCESS macro for SPSS (Hayes, 2012; Model 1) was used to test the hypothesized moderating effects. The total alexithymia score or one of the factor scores were set as the independent variable, HAB as the moderator, and the level of total aggression, reactive aggression or proactive aggression, respectively, as the dependent variables. There were 12 parallel moderating models tested in total. Continuous variables were mean-centered to reduce any multicollinearity (see Holmbeck, Li, Schurman, Friedman & Coakley, 2002), and simple slope tests were conducted at ± 1 SD. A bias-corrected and accelerated bootstrap based on 5000 samples was performed. The moderation model would be supported if zero were not in the 95% confidence interval (Preacher & Hayes, 2008). Further analyses were conducted to explore the unique relationships between alexithymia dimensions and aggression subtypes, as well as potential moderation by HAB, by controlling non-focal subscales (Salmivalli, Kaukiainen & Lagerspetz, 2000; White, Gordon & Guerra, 2015). For instance, when testing the association between DIF and proactive aggression, the scores for DDF, EOT, and reactive aggression were set as covariates. Similarly, when examining the relationship between EOT and reactive aggression, the scores for DIF, DDF, and proactive aggression were set as covariates.

2.2. Measures 2.2.1. Measures of alexithymia Alexithymia was assessed using the Chinese version of the TAS-20 with responses rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The scale consisted of 20 items that loaded onto three factors: a) DIF (e.g., “I don't know what's going on inside me”), seven items; b) DDF (e.g., “It is difficult for me to find the right words for my feelings”), five items; and c) EOT (e.g., “I prefer talking to people about their daily activities rather than their feelings”), eight items. Items were summed with higher scores indicating a higher level of alexithymia. Cronbach's alpha coefficients for the alexithymia total score (0.82) and DIF (0.82) subscale were good, although that for DDF (0.69) and EOT (0.50) subscale were lower. 2.2.2. Measures of HAB HAB was measured using the SIP–ABQ, which was rated on a 4point Likert scale from not at all likely (0) to very likely (3). The English-language questionnaire was translated into Chinese and then back-translated by three bilingual speakers. This questionnaire consisted of eight vignettes. Each vignette described an ambiguous social situation followed by four questions that assess direct hostile intent (e.g., ‘The club members wanted to ignore me’), indirect hostile intent (e.g., ‘The club members wanted me to feel unimportant’), instrumental nonhostile intent (e.g., ‘The club members were more interested in talking among themselves’), and benign intent (e.g., ‘The club members didn’t hear me say ‘‘Hi”’). The sum scores of the 16 items that assess direct and indirect hostile intent represent the degree of HAB (Coccaro et al., 2009). The alpha reliability in the sample was good (α = 0.89).

3. Results 3.1. Descriptive statistics and correlations All study variables were checked for outliers ( ± 3 SDs). Data from six participants were then excluded, resulting in a total of 479 participants (mean ± SD age, 20.38 ± 1.66) whose data remained for further analyses. T-tests showed that males reported higher levels of EOT [t (1, 477) = 1.93, p = 0.054], HAB [t (1, 477) = 2.63, p = 0.009], reactive aggression [t (1, 477) = 2.76, p = 0.006], proactive aggression [t (1, 477) = 5.79, p < 0.001], and total aggression [t (1, 477) = 5.02, p < 0.001] than females. No other gender differences were found (ts between −0.42 and 1.59, ps ≥ 0.113). Age showed positive associations with HAB (r = 0.11, p = 0.017), proactive aggression (r = 0.10, p = 0.032), and total aggression (r = 0.09, p = 0.053). No other significant links were found between age and the study variables (rs between 0.02 and 0.08, ps ≥ 0.071).

2.2.3. Measures of reactive aggression and proactive aggression Both reactive aggression and proactive aggression were assessed using the Chinese version of the RPQ. The questionnaire consisted of 20 items that loaded onto two factors: (1) reactive aggression (e.g., 'Reacted angrily when provoked by others'), ten items; and (2) proactive aggression (e.g., 'Used physical force to get others to do what you want'), ten items. In the present study, Cronbach's alpha for the total Table 1 Means, standard deviations, and correlations of the study variables (N = 479).

Descriptives Mean SD Correlations Alexithymia DIF DDF EOT HAB TA RA PA

Alexithymia

DIF

DDF

EOT

HAB

TA

RA

PA

54.41 9.68

19.04 5.04

14.25 3.43

21.11 3.56

19.50 7.83

48.41 14.16

29.39 7.24

19.02 8.83

0.89**

0.82** 0.65**

0.68** 0.37** 0.33**

0.33** 0.32** 0.27** 0.20**

0.30** 0.32** 0.13** 0.24** 0.44**

0.25** 0.30** 0.14** 0.10* 0.32** 0.85**

0.28** 0.26** 0.10* 0.30** 0.44** 0.90⁎⁎ 0.55** –

Note: DIF = difficulty in identifying feelings, DDF = difficulty in describing, EOT = externally oriented thinking, HAB = hostile attribution bias, TA = total aggression, RA = reactive aggression, PA = proactive aggression. ⁎ p < 0.05. ⁎⁎ p < 0.01. 3

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Table 2 Partial correlations of the study variables (N = 479).

Alexithymia DIF DDF EOT

Table 3 Moderation results of the bias-corrected bootstrap analyses (N = 479), after controlling age, gender, and non-focal alexithymia and aggression subscales.

RA Model 1

Model 2

Model 3

PA Model 1

Model 2

Model 3

0.24** 0.31** 0.14** 0.09*

0.11 0.20** 0.11** −0.08

0.19** −0.01 −0.15*

0.28** 0.27** 0.09 0.29**

0.18** 0.14** 0.02 0.28**

0.11* −0.13** 0.27**

⁎⁎

MV

IV

DV

β

SE

t

p

95%CI

HAB

Alexithymia

TA RA PA TA RA PA TA RA PA TA RA PA

0.08 0.03 0.05 0.06 0.00 0.06 0.02 0.03 −0.01 0.07 0.03 0.03

0.04 0.04 0.03 0.04 0.04 0.03 0.04 0.03 0.03 0.04 0.04 0.03

2.14 0.81 1.40 1.72 0.02 1.73 0.49 0.78 −0.25 1.75 0.86 0.95

0.032 0.418 0.162 0.086 0.984 0.084 0.626 0.439 0.802 0.080 0.391 0.340

(0.007–0.153)* (−0.040–0.097) (−0.018–0.110) (−0.009–0.136) (−0.067–0.068) (−0.007–0.117) (−0.053–0.089) (−0.040–0.091) (−0.069–0.053) (−0.008–0.147) (−0.041–0.104) (−0.034–0.099)

DIF

Note: DIF = difficulty in identifying feelings, DDF = difficulty in describing, EOT = externally oriented thinking, HAB = hostile attribution bias, TA = total aggression, RA = reactive aggression, PA = proactive aggression. Model 1 = controlling age and gender; Model 2 = controlling age, gender, and the other aggression type; Model 3 = controlling age, gender, the other aggression type, and non-focal alexithymia dimensions. ⁎ p < 0.05. ⁎⁎ p < 0.01.

DDF EOT

Note: *A significant mediation effect with CI does not include 0, CI = confidence interval. IV = independent variable, MV = mediator variable, DV= dependent variable, DIF = difficulty in identifying feelings, DDF = difficulty in describing, EOT = externally oriented thinking, HAB = hostile attribution bias, TA = total aggression, RA = reactive aggression, PA = proactive aggression.

Descriptive statistics and correlations for study variables are presented in Table 1.The three alexithymia factors were significantly correlated with each other (rs between 0.33 and 0.65, ps < 0.001). The two aggression subtypes were moderately associated, r = 0.55, p < 0.001. Meanwhile, all three alexithymia factors were positively correlated with reactive aggression as well as proactive aggression (rs between 0.10 and 0.30, ps ≤ 0.046). Partial correlation analyses (as shown in Table 2) were conducted to explore the unique association between each alexithymia factor and aggression subtype by controlling age, gender, and non-focal subscales. The correlation coefficients (Model 3) showed that DIF was positively associated with reactive aggression (r = 0.19, p < 0.001) and proactive aggression (r = 0.11, p = 0.018). The DDF factor was negatively related to proactive aggression (r = −0.13, p = 0.004), but unrelated to reactive aggression(r = −0.01, p = 0.876).The EOT factor showed a negative association with reactive aggression (r = −0.15, p = 0.001), but a positive association with proactive aggression (r = 0.27, p < 0.001). Further analyses were performed to compare the strength of correlations from the same sample using online software from Lenhard and Lenhard (2014). Results showed that EOT had a stronger association with proactive aggression than DIF, Fisher Z > 2.58, p < 0.01.

Fig. 1. The moderating role of HAB in the association between alexithymia and total aggression.The x-axisrepresents standardized levels of alexithymia and the y-axis represents standardized aggression scores. The solidline indicates the group with high HAB scores (1 SD above the mean) and the dashed line indicates the low HABgroup (1 SD below the mean). HAB = hostile attribution bias.

3.2. Moderation analyses

alexithymia on total aggression was significant, β = 0.27, SE = 0.06, t = 4.45, p < 0.001, 95% CI = [0.15, 0.39]. For the low-score group (1 SD below the mean), the effect of alexithymia on total aggression was also significant, but to a lesser extent, β = 0.11, SE = 0.05, t = 2.22, p = 0.027, 95% CI = [0.01, 0.21].

Results of the moderation analyses (controlling for age and gender) revealed that HAB acted as a moderator in the relationships between alexithymia and total aggression, β = 0.08, SE = 0.04, t = 2.14, p = 0.032, 95% CI = [0.01, 0.15], between alexithymia and proactive aggression, β = 0.07, SE = 0.04, t = 2.00, p = 0.046, 95% CI = [0.00, 0.14], and between DIF and proactive aggression(β = 0.07, SE = 0.04, t = 1.98, p = 0.049, 95% CI = [0.00, 0.15]. No other interaction effects were found (see Supplementary Table 1). Similarly, unique moderation analyses were conducted by controlling age, gender, and non-focal subscales. Results showed that the moderating role of HAB in the association between alexithymia and proactive aggression failed to reach statistical significance after controlling for age, gender, and reactive aggression, β = 0.05, SE = 0.03, t = 1.40, p = 0.162, 95% CI = [−0.02, 0.11]. Further, the moderating role of HAB in the association between DIF and proactive aggression failed to reach statistical significance when age, gender, DDF, EOT, and reactive aggression were added as covariates, β = 0.05, SE = 0.03, t = 1.73, p = 0.084, 95% CI = [−0.01, 0.12]. No other moderating effects were found (see Table 3). Simple slope analyses (as shown in Fig. 1) indicated that for individuals with high levels of HAB (1 SD above the mean), the effect of

4. Discussion The present study aimed to further clarify the correlation between alexithymia and aggression. Consistent with prior research, results showed a positive association between alexithymia and aggression. However, in line with our hypotheses, different alexithymia factors did not show equally strong associations with aggression subtypes. Specifically, DIF was the only alexithymia factor that showed a positive correlation with reactive aggression, while EOT (in contrast to DIF and DDF) was the strongest correlate of proactive aggression. Meanwhile, the present study confirmed the moderating role of HAB in the relationship between alexithymia and aggression in such a way that the higher the HAB, the stronger the relationship. A positive relationship between alexithymia and aggression was found. This result was consistent with other studies demonstrating the predictive value of alexithymia in aggressive behaviors (e.g., 4

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Fossati et al., 2009; Velotti et al., 2016). Emotions provide individuals with critical information for guiding, controlling, and regulating behaviors in accordance with societal demands (Baumeister, Dewall & Vohs, 2010). However, alexithymic individuals are usually unaware of, or confused by, the source, valence, and intensity of emotional experiences (Sifneos, 1973; Vorst & Bermond, 2001). These deficits in processing internal distress could lead to a higher level of externalizing problems. The present study extended prior findings by investigating the unique association between different facets of alexithymia and subtypes of aggression. Results showed that DIF was the only alexithymia factor that positively related to both reactive aggression and proactive aggression. This result seems reasonable because DIF has been conceptualized as a more general risk factor for a wide range of relational and emotional difficulties (e.g., Bagby et al., 1994). Meanwhile, results suggested that DIF may be the main alexithymia factor contributing to reactive aggression, while EOT may be the main alexithymia factor contributing to proactive aggression. Individuals with high DIF may experience difficulties in recognizing one's own and others’ emotional states (Gallup, 1998; Lyvers et al., 2017). It has been suggested that all infants and toddlers have a natural inclination to hostile interpretations and subsequent impulsive behaviors, but the development of abilities to accurately recognize and interpret the mental states of others can help mitigate vicious situations (Dodge, 2006). However, if these abilities do not develop, a variety of impulse-control problems could occur (Mitrovic & Brown, 2009; Rose, 2012). EOT was described as a preference to attend to external stimuli (Sifneos, 1973; Vorst & Bermond, 2001), and was found to be associated with a reduced level of empathy and fear insensitivity (Essau et al., 2006; Karpman, 1941). This could explain why EOT individuals tend to pursue external stimuli and rewards and are often sensitive to external factors that might impede their goals. These individuals may regard proactive aggression as an efficient way to achieve their goals. This is supported by the findings that EOT was positively associated with coldheartedness and blame externalization (Lander, Lutz-Zois, Rye & Goodnight, 2012). Whereas all bivarate correlations were positive, in partial correlation analyses, the relationships between DDF and proactive aggression, and between EOT and reactive aggression were negative. This possibility may be understood as a statistical suppressor effect (Gaylord-Harden, Cunningham, Holmbeck, & Grant, 2010). Overall, present findings are consistent with other studies demonstrating that alexithymia dimensions are differentially related to emotional difficulties and externalizing problems (e.g., Lander et al., 2012; Rose, 2012), and support the description of alexithymia as a heterogeneous and dimensional structure (e.g., Kajanoja et al., 2017). In accordance with our expectations, the present findings also expanded on prior findings by showing that HAB served as a moderator for the relationship between alexithymia and aggression. Although empirical research suggested a positive association of alexithymia and HAB with aggression separately, there is a need to explore how alexithymia (an emotional factor) and HAB (a cognitive factor) interact, contributing to an increase in aggression (Anderson & Bushman, 2002; Konrath et al., 2012; Lemerise & Arsenio, 2000). Results provided evidence that the link between alexithymia and aggression was stronger for individuals with higher levels of HAB than for those with lower HAB. These results might appear to be reasonable. Alexithymic individuals high in HAB are more likely to perceive threat, betrayal, and hurt. This combination of traits along with their incapacity to express and terminate internal distress, can trigger externalizing problems such as aggression (Cohn et al., 2009). Alternatively, alexithymic individuals low in HAB may be relatively less motivated to perform such externalizing behaviors. Present findings may help enrich the development of the integrated model of SIP (Lemerise & Arsenio, 2000), which described in detail the potential role of individual variations in emotional functioning in aggression, but did not describe how emotional factors interact with cognitive factors in predicting aggression.

In addition, no moderating effect of HAB on the correlation between alexithymia dimensions and aggression subtypes was found. This may be explained by the fact that it is difficult to detect a theorized moderator effect in non-experimental studies (McClelland & Judd, 1993). Future research should adopt experimental design to test the potential moderator effect. However, it does not exclude the possibility that HAB may not change the strength of the relationship between alexithymia dimensions and aggression subtypes. Present findings might reflect distinct roles of HAB in the overall alexithymia-aggression relationship and the specific dimension relationships, and support the necessity to analyze data by dimension scores, in addition to total scores (e.g., Kajanoja et al., 2017). Some limitations of the present study should be noted, particularly the cross-sectional design, self-report measures, and relatively homogenous sample. Future studies could avoid these limitations by adopting longitudinal designs that would allow the researchers to establish the directionality of the links between the primary variables. Additionally, future research should consider using alternative methods (e.g., observation-dependent, physiological, neurological) to decrease the influence of subjectivity, including social desirability bias. Further studies are also needed to examine the generalizability of the present findings to other groups, such as clinical samples or geriatric populations. Finally, the reliability coefficient for two of the alexithymia scales was less than optimal, which was consistent with previous research using translated versions of the scale (e.g., Zhu et al., 2007). More research is needed to test the stability of the current findings. In conclusion, the present study found that the three dimensions of alexithymia showed distinct risk profiles for reactive aggression and proactive aggression, especially certain alexithymia factors have stronger relationships with aggression than others. Although no moderating role of HAB in specific dimension relationships has been found, our exploratory research adds to the current knowledge on alexithymia and aggression. First, this study underlines the need for a sub-dimensional approach when exploring the risks associated with alexithymia. Otherwise, the nature of associations between variables may be over generalized. Secondly, this study suggests that those with low alexithymia total scores (i.e., TAS-20 scores below the clinical threshold; Franz et al., 2008), but a relatively high level of certain factor (e.g., DIF) scores, could also be aggressors. These individuals should not be overlooked by researchers and educators. Thirdly, this study could have practical implications. For instance, present findings suggest that prevention and treatment programs focused on improving DIF-related deficits, such as emotion recognition training techniques (PentonVoak et al., 2013; Schönenberg et al., 2014), may help improve aggression, especially reactive aggression (Penton-Voak et al., 2013). Ethical statement We confirm that all study participants provided informed consent, and the study design was approved by the Institutional Review Board at Shanghai Normal University, China. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. CRediT authorship contribution statement Xu Li: Methodology, Writing - original draft. Bingbing Li: Data curation, Formal analysis. Jiamei Lu: Conceptualization. Li Jin: Writing - review & editing. Juan Xue: Writing - review & editing. Xianwei Che: Writing - review & editing. Declaration of Competing Interest none. 5

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Acknowledgments

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