The Impact of Reinforcement Sensitivity Theory on Aggressive Behavior

Abstract

Why some people, and not others, exhibit aggression remains unknown. Research points to certain personality traits being linked to either proactive or reactive aggression, or both. The current study attempts to address these gaps from the perspective of revised Reinforcement Sensitivity Theory (rRST). Using 146 participants, results revealed Behavioral Approach System (BAS)-Impulsivity and the Behavioral Inhibition System (BIS) predicted proactive and reactive aggression. Defensive Fight predicted reactive aggression. BAS-Impulsivity predicted proactive aggression partially mediated by Social Dominance Orientation. These findings suggest proactive and reactive aggression are motivated by a combination of anxiety and rapid, approach-motivational personality traits. This study provides further understanding of the specific relations between rRST and aggression, is integrated into the general aggression model, and has implications for treatment of aggressive individuals.

Keywords

personality reinforcement sensitivity theory aggression social dominance orientation

Global examples of aggression such as gun violence and world wars are alarming and highlight the high levels of aggression in modern society (Van de Vliert & Van Lange, 2019; Van Lange et al., 2017). Understanding why some individuals, but not others, engage in aggressive behavior could help reduce the high cost of aggression to society, which includes refugeeism (DeWall & Anderson, 2011), divorce (Bowlus & Seitz, 2006), mental health issues (Kilpatrick et al., 2003), and suicide (Hinduja & Patchin, 2010) as these individuals could be identified for treatment and intervention. Aggression is influenced by personality (Caprara et al., 1994) and social attitudes (Thomsen et al., 2008) and this may interact with situational factors such as hot climate, provocation, and low socioeconomic status (Bettencourt et al., 2006) to predispose certain individuals to aggress (Anderson & Bushman, 2002). Research has overlooked conceptualizing personality as a biopsychologically derived approach-avoidance system (Corr, 2013; Fernandez & Walker, 2019; Sovereign & Walker, 2020) in the prediction of aggressive behavior. The current study enhances understanding of the neurobiological systems underlying personality and its association with aggressive behavior and examines how social attitudes may influence this relationship.

Aggression is any directed behavior intended to harm a target individual who is, in turn, motivated to avoid the perpetrator’s behavior (Baughman et al., 2012; Bettencourt et al., 2006). Extreme aggression has the goal of intense harm, for example, sexual assault and murder (Anderson & Bushman, 2002; Walker et al., 1993). Aggressive behavior is distinct from outcomes resulting in accidental harm and also from trait aggression, cognitions or propensity to verbal and physical aggression (Anderson & Bushman, 2002; Buss & Perry, 1992). Research demonstrates that aggressive behavior is associated with a high individual, societal, and economic burden (Fairchild et al., 2019). Given the costs of aggression to society, it is necessary to better understand the various predictors of aggression including personality, cognitive, and social factors.

Some early theories asserted that aggressive cognitions predict aggressive behavior. Crick and Dodge (1994) suggested aggressive cognitions are acquired through early social experiences and perpetuated via cognitive processing, affective experiences, evaluation, and aggressive behavior. Huesmann (1998) asserted that cognitions predict aggressive behavior through “scripts,” or aggressive cognitions, which had an associated negative affect (anger). People who are more likely to engage in aggressive behavior have more readily available aggressive cognitions (Huesmann, 1998). Empirical research has provided support for the role of cognition in predicting aggressive behaviors (De Castro et al., 2002; Epps & Kendall, 1995; Thomsen et al., 2008).

In an attempt to identify reliable predictors of aggression, empirical studies have used person factors, or personality variables. Costa and McCrae’s (1990) five-factor model demonstrated neuroticism predicted, and agreeableness inversely predicted, trait aggressiveness, and aggressive behavior (Gleason et al., 2004; Miller et al., 2003; Sharpe & Desai, 2001). Factor 1 psychopathy (emotional detachment) and sadism predicted proactive (unprovoked) aggression in adult males (Reidy et al., 2011). Meta-analyses report personality variables, such as trait aggressiveness and impulsivity, predict aggressive behavior regardless of provocation (Bettencourt et al., 2006; Bushman, 1995), whereas others suggest narcissism and trait anger interact with provocation to predict aggression (Bettencourt et al., 2006; Bushman & Baumeister, 1998). While these studies are important, insufficient research on aggression has been conducted from an approach and avoidance perspective.

Anderson and Bushman (2002) attempted to integrate these theories to present a unified theory of aggression, the General Aggression Model. The General Aggression Model asserts that aggressive behavior is predicted by three main factors (inputs, routes, and outcomes) within the interaction between a person variable (e.g., personality trait) and situational variable (e.g., provocation). It does this through knowledge structures that are cognitions, beliefs, attitudes, and scripts used to understand the social environment and select social action. Aggressive knowledge structures motivate aggressive behavior via repeated rehearsal and automation. Empirical evidence supports the application of the General Aggression Model to aggressive behavior with aggressive beliefs and scripts predicting aggression (Hosie et al., 2014). While the theory has merit in being applied to reactive aggression (Anderson & Bushman, 2002) and accounts for both cognition and personality, evidence has yet to provide how and why particular personality variables, such as trait aggressiveness, and predict aggressive behavior (Bushman, 1995). This is pertinent as personality is empirically considered to be a stronger predictor of aggressive behavior than cognition (Ferguson & Dyck, 2012).

Revised Reinforcement Sensitivity Theory (rRST) is a biologically based theory of personality which may address both how and why particular personality variables predict aggressive behavior (Corr, 2008, 2013; Corr & Perkins, 2009; Farrell & Walker, 2019a, 2019b; Shahzadi & Walker, 2019, 2020). According to rRST, aggressive behavior is the outcome of the interplay of three major neuropsychological systems underlying personality which mediate threat stimuli, generate emotion, and motivate approach-avoidance behavior (Corr & Perkins, 2009). The first system is the approach-oriented Behavioral Approach System (BAS) and the other two are avoidant Fight-Flight-Freeze System (FFFS), and Behavioral Inhibition System (BIS).

Individuals have considerable differences regarding the sensitivity of these systems to elicit motivating stimuli for aggressive behavior (Corr & Perkins, 2009; Dolatyar & Walker, 2020; Farrell et al., 2020). BAS, activated by appetitive or rewarding stimuli, motivates approach behavior due to sensations of excitement and anticipation, akin to the personality trait of extraversion (Corr, 2013; Corr & Perkins, 2009). BAS activation and its approach behavior has been demonstrated to be related to proactive predatory aggression, evidenced in animal and human studies (Carver & Harmon-Jones, 2009; Corr & Perkins, 2009). BIS, activated by conflicting or ambiguous stimuli, inhibits approach behavior due to sensations of anxiety and apprehension akin to the personality trait of neuroticism (Corr, 2013). BIS activation inhibits aggression while it attempts to resolve conflict via co-activation of BAS/FFFS (Corr & Perkins, 2009). Individual differences in the sensitivity of these systems to elicit aggressive behavior are attributed to the personality components, such as excited anticipation (extraversion) or anxious apprehension (neuroticism), motivating approach and avoidance (Corr, 2013).

Human studies suggest the personality motivations that underpin defensive aggression are less clear. Typically, FFFS is activated by aversive or threatening stimuli, causing sensations of fear and motivating avoidance behavior (Corr & Perkins, 2009). Empirical studies support rRST’s prediction of defensive behavior, or aggression avoidance, with BIS, Flight and Freeze predicting a tendency to move away from threat (Krupic et al., 2016). However, a tendency to move toward threat has been predicted by Fight and BAS (Krupic et al., 2016) suggesting human aggression involves some aspect of BAS activation, even when it is defensive. Relations have been demonstrated between BAS and Fight scales in human studies of anger and aggression (Beaver et al., 2008; Carver & Harmon-Jones, 2009; Miller et al., 2012; Smits & Kuppens, 2005). Aggression in the face of threat may produce BAS-related emotional safety signals (Corr & Cooper, 2016). To accommodate the idiosyncrasy of defensive aggression, or a motivation to move toward threat, Corr and Cooper (2016) devised a standalone measure of Defensive Fight.

The role of provocation in aggressive behavior is complex. Aggressive behavior can be due to sensations of stress in response to a perceived threat (reactive, provoked/ defensive aggression), or undertaken without stress or the perception of threat (proactive, unprovoked/predatory aggression) and instead motivated by a premeditated desire to intimidate or gain from another (Bettencourt et al., 2006; Bushman & Anderson, 2001; Dodge & Coie, 1987). This dichotomy is considered the dual-model of proactive and reactive aggression, or unprovoked or provoked aggression (Anderson & Bushman, 2002; Corr & Perkins, 2009). Corr and Perkins (2009), attribute motivations to this dual-model of aggressive behavior using the neurobiological approach-avoidance system conceptualized by the rRST. Proactive (unprovoked/ predatory) aggression is reward/goal-directed, occurring without provocation as a response to activation of the BAS approach system. Defensive (provoked/reactive) aggression occurs with provocation, as a response to activation of the FFFS (Fight)/Defensive Fight systems, motivating a fight response. Recent studies support this dual-model motivational system for aggression (Carver & Harmon-Jones, 2009; Corr & Perkins, 2009). However, research has yet to demonstrate the activation of the BAS or FFFS (Fight)/Defensive Fight systems during proactive (unprovoked/predatory) or reactive (provoked/defensive) aggression.

The different behavioral outcomes implicit in the dual-model of proactive and reactive, or unprovoked or provoked, aggression suggest that there may be individual differences in motivations for aggression beyond personality, and beyond survival or stress response to threat, possibly involving social domination or control (Anderson & Bushman, 2002; Corr & Perkins, 2009; Dodge & Coie, 1987). Social attitudes may predict an increase or decrease in aggressive behavior, potentially mediating the association between personality and aggressive behavior. Even the General Aggression Model acknowledges the important motivational role of social influence on aggressive behavior through its knowledge scripts, which are primarily used to understand the social environment and select social action (Anderson & Bushman, 2002).

Individual social attitudes can be understood via constructs such as their Social Dominance Orientation (SDO; Sidanius, 1993). SDO as a social attitude construct is aligned with a desire for in-group superiority, dominance, and self-enhancement (Corr et al., 2013; Thomsen et al., 2008). Duckitt and Sibley (2010) suggest a dual-process motivational model structure to social attitudes where the five-factor model of personality dimensions predispose individuals to develop social attitudes that motivate them to display associated attitudes and behaviors, such as low openness and agreeableness (Cohrs et al., 2012). Studies indicate the personality trait BAS, and not FFFS, predicted SDO (Corr et al., 2013) and SDO has predicted sexual aggression, ethnic persecution, and aggression in children (McQuade et al., 2016; Thomsen et al., 2008; Walker et al., 1993). Thus, BAS predicts proactive aggressive behavior and BAS also predicts SDO, which in turn predicts proactive aggressive behavior. Examination of a potential mediation effect may impact theories of personality and aggression by suggesting personality predicts proactive aggressive behavior through social attitudes such as SDO.

This study aims to examine relations between personality factors of rRST and either proactive (unprovoked) or reactive (provoked) aggression to understand the personality dimensions that predict aggressive behavior. It will also explore the impact of neuropsychological dimensions of personality on aggressive behavior with mediation by SDO. The current study aims to enhance understanding of the neurobiological systems underpinning personality and their associations with either proactive or reactive aggression. The outcomes may offer utility in developing treatment and intervention for aggression in clinical and non-clinical settings. It is hypothesized that BAS will positively predict proactive aggressive behavior. It is second hypothesized that BAS will positively predict reactive aggressive behavior. It is third hypothesized that Defensive Fight will positively predict reactive aggressive behavior. Finally, it is hypothesized that the effect of BAS on proactive aggressive behavior will be mediated SDO.

Method

Participants

The final sample included 146 participants (56% men, 44% women, M_age = 38.53, SD_age = 12.54). Participants were required to be over the age of 18 and from the United States. The participants were recruited from Prime Panels which is a compilation of a range of online research panels with access to over 50 million people (for more details about Prime Panels see https://www.cloudresearch.com/products/prime-panels/). Research has found Prime Panels participants to provide better quality responses than other online research panels such as Amazon Mechanical Turk (Chandler et al., 2019). The U.S. participants identified their ethnic background Caucasian/White (84%), Black (5%), Hispanic/Latino (4%), Asian/Indian (3%), Multiracial (2%), Indigenous/ Aboriginal (1%), and Pacific/Torres Strait Islander (1%). As this sample is from the United States and the majority identify as Caucasian/White, the generalizability of the study is arguably limited to Western countries (see limitations section for more discussion about generalizability). To ensure the quality of data, each participant’s data were checked and participants were removed who did not complete the study properly (e.g., responding all 1s). Participants have a Prime Panels ID and the study is no longer advertised to them once they have completed the study; furthermore, each IP address was checked to ensure unique responding. Prime Panels say they have screening procedures in place so that the participants are good quality. A post hoc power analysis suggested 146 participants enabled 95% power to detect a medium effect at the .05 level of significance.

Procedure

Before commencement, ethical approval was gained from a local institutional review board. All ethical processes were followed in the procedure of this study. An advertisement was placed on the online research platform Prime Panels and participants clicked on a link which took them to the Qualtrics platform where the study was hosted. Participants provided informed consent for the study. This study needed to accurately measure aggressive behavior without confounding effects of social desirability. Therefore, participants were led to believe they were completing a study concerning personality and goal-directed behavior. Furthermore, participants were informed that the study was anonymous to assure them that there would not be any reputation repercussions. After completing demographic items and each of the measures, participants were debriefed with a statement informing them that the study concerned aggression.

Materials

The Reinforcement Sensitivity Theory of Personality Questionnaire (RST-PQ; Corr & Cooper, 2016) assessed Gray and McNaughton’s (2003) BAS, BIS, and FFFS. The self-report measure consists of 65 items rated on a 4-point scale (1 = not at all, 4 = highly). The eight-item Defensive Fight subscale was also used. Examples are “I am a very active person” (BAS-Reward Interest), “Good news makes me feel overjoyed” (BAS-Reward Reactivity), “I’m always buying things on impulse” (BAS-Impulsivity), “I’m motivated to be successful in my personal life” (BAS-Goal-Drive Persistence), “I feel sad when I suffer minor setbacks” (BIS), “There are some things I simply cannot go near” (FFFS), and “I can be an aggressive person when I need to be” (Defensive Fight). Higher scores indicate increased activation of the dimension. The internal reliability was acceptable (α = .76–.96).

SDO, a prominent social attitude dimension, was measured by the SDO scale (SDO-6; Pratto et al., 1994). The SDO-6 consists of 16 items rated on a 7-point scale (1 = very negative, 7 = very positive). Half the items indicate approval for inequality and half indicate approval for equality. An example item is “some groups of people are simply not the equal of others.” Items nine to 16 are reverse-scored and higher scores indicate a preference for in-group domination. The internal reliability was excellent (α = .97).

The Reactive/Proactive Aggression Questionnaire (RPQ; Raine et al., 2006) measured reactive and proactive aggressive behavior. The RPQ consists of 23 items on a 3-point scale (0 = never, 2 = often) with two subscales, measuring proactive (12 questions) and reactive (11 questions) aggressive behavior. An example of reactive aggression is “Yelled at others when they have annoyed you” and of proactive aggression is “Had fights with others to show who was on top.” Higher scores indicate increased aggressive behavior. The internal reliability was acceptable for both proactive (α = .89) and reactive aggression (α = .87).

Results

Data were downloaded from Qualtrics and analyzed using SPSS version 25. Means, standard deviations, and correlations are shown in Table 1. As can be seen in Table 2, a series of regression analyses were performed to investigate the neuropsychological dimensions of personality on aggressive behavior. In the initial analysis, the BAS, FFFS, and BIS scales of the RST-PQ (Corr & Cooper, 2016) were used to predict either proactive or reactive aggressive behavior.

Table 1.

Pearson Product–Moment Correlations Between All Variables.

Variable	M	SD	BAS-RI	BAS-GDP	BAS-RR	BAS-I	FFFS	DF	BIS	PAB	RAB	SDO
BAS	77.69	15.52	.88**	.76**	.87**	.59**	.14	.36**	.03	.17*	.26**	−.009
BAS-RI	17.31	4.59		.71**	.62**	.42**	−.07	.26**	−.12	.15	.15	−.11
BAS-GDP	20.07	4.91			.57**	.06	.01	.27**	−.25**	−.06	.11	−.09
BAS-RR	25.19	6.04				.43**	.30**	.38**	.13	.10	.25**	−.001
BAS-I	15.12	4.23					.16	.21*	.35**	.40**	.30**	.19*
FFFS	23.14	6.57						.13	.41**	.02	.14	−.05
DF	21.20	4.58							.22**	.13	.44**	−.04
BIS	48.97	16.90								.17*	.41**	−.10
PAB	14.22	3.45									.48**	.38**
RAB	18.10	4.21										.05
SDO	37.60	20.50

Note. BAS-RI = BAS-Reward Interest; BAS-GDP = BAS Goal-Drive Persistence; BAS-RR = BAS-Reward Reactivity; BAS-I = BAS-Impulsivity; FFFS = Fight-Flight-Freeze System; DF = Defensive Fight; BIS = Behavioral Inhibition System; PAB = Proactive Aggressive Behavior; RAB = Reactive Aggressive Behavior; SDO = Social Dominance Orientation; BAS = Behavioral Activation System.

p < .05. **p < .01.

Table 2.

Regression Analysis in the Prediction of Proactive and Reactive Aggressive Behavior.

DV	IV	b	SE	β	p	95% CI	R ²
PAB	BAS	.04	0.02	.18	.03	[0.003, 0.07]
	FFFS	−.04	0.04	−.09	.34	[−0.13, 0.05]
	BIS	.04	0.02	.20	.03	[0.004, 0.07]	.06
RAB	BAS	.07	0.02	.25	.001	[0.03, 0.11]
	FFFS	−.05	0.05	−.07	.37	[−0.15, 0.06]
	BIS	.11	0.02	.43	<.001	[0.07, 0.15]	.23
PAB	BAS-RI	.08	0.08	.12	.37	[−0.10, 0.27]
	BAS-GDP	−.09	0.08	−.14	.29	[−0.25, 0.08]
	BAS-RR	−.03	0.06	−.06	.57	[−0.15, 0.08]
	BAS-IMP	.29	0.08	.38	<.001	[0.14, 0.44]	.17
RAB	BAS-RI	−.13	0.13	−.15	.29	[−0.38, 0.11]
	BAS-GDP	.09	0.11	.11	.40	[−0.13, 0.31]
	BAS-RR	.11	0.08	.15	.18	[−0.05, 0.26]
	BAS-IMP	.29	0.10	.29	.005	[0.09, 0.49]	.11
PAB	DF	.10	0.06	.13	.11	[−0.02, 0.21]	.02
RAB	DF	.40	0.07	.44	<.001	[0.27, 0.54]	.19

Note. CI = confidence interval; PAB = Proactive Aggressive Behavior; BAS = Behavioral Activation System; FFFS = Fight-Flight-Freeze System; BIS = Behavioral Inhibition System; RAB = Reactive Aggressive Behavior; BAS-RI = BAS-Reward Interest; BAS-GDP = BAS Goal-Drive Persistence; BAS-RR = BAS-Reward Reactivity; BAS-IMP = BAS-Impulsivity; DF = Defensive Fight; DV = Dependent Variable; IV = Independent Variable.

Hypothesis 1 (H1) was that BAS would positively predict proactive aggressive behavior. As expected, proactive aggressive behavior was positively predicted by BAS. Hypothesis 2 (H2) was that BAS would positively predict reactive aggressive behavior. As expected, reactive aggressive behavior was positively predicted by BAS. As expected, FFFS did not predict proactive aggressive behavior or reactive aggressive behavior. Unexpectedly, BIS also predicted proactive aggressive behavior and reactive aggressive behavior. These results support H1 and H2 with the additional result of BIS predicting proactive and reactive aggression.

As initial findings suggested BAS predicted proactive and reactive aggressive behavior, further multiple regression analyses were performed with all four BAS subscales to determine which BAS thematic facets uniquely predict proactive or reactive aggressive behavior. As Table 2 suggests, Impulsivity predicted both proactive aggressive behavior and reactive aggressive behavior. In contrast, proactive aggressive behavior was not predicted by Reward Interest, Goal-Drive Persistence, and Reward Reactivity. Also, reactive aggressive behavior was not predicted by Reward Interest, Goal-Drive Persistence, and Reward Reactivity.

Hypothesis 3 (H3) was that Defensive Fight would positively predict reactive aggressive behavior. Simple linear regression was conducted with Defensive Fight as the predictor and reactive aggressive behavior or proactive aggressive behavior as the criterion. As expected, Defensive Fight predicted reactive aggressive behavior, but not proactive aggressive behavior. This result supports H3.

Hypothesis 4 (H4) was that BAS would positively predict proactive aggressive behavior mediated by SDO. While total BAS predicted proactive aggressive behavior, facet analysis suggested BAS-Impulsivity was the only meaningful facet, therefore mediation analysis was conducted using BAS-Impulsivity. Mediation was analyzed with bootstrapping methodology using the PROCESS macro (Hayes, 2012), therefore an indirect effect with a confidence interval outside zero is the key indicator of evidence of mediation. Results indicated BAS-Impulsivity predicted SDO and SDO predicted proactive aggressive behavior. The total effect was BAS-Impulsivity predicted proactive aggressive behavior when SDO was not in the model. Most importantly, there was an indirect effect of BAS-Impulsivity on proactive aggressive behavior through SDO. However, the direct effect of BAS-Impulsivity predicting proactive aggressive behavior when SDO was in the model did not become nonsignificant which suggests the significant indirect effect should be interpreted as partial mediation rather than full mediation. Figure 1 presents the mediation model predicting proactive aggressive behavior from BAS-Impulsivity through SDO. These partial mediation results provide some support for H4.

Figure 1.

Effect of BAS-Impulsivity on proactive aggressive behavior through social dominance.

Discussion

The current study aimed to examine the impact of rRST on proactive and reactive aggressive behavior from the personality dimensions of BAS, BIS, and FFFS and to examine SDO as a mediator of the relationship between BAS and proactive aggressive behavior. The first three hypotheses were supported and the last hypothesis was partially supported. BAS predicted both proactive and reactive aggressive behavior, Defensive Fight predicted reactive but not proactive aggressive behavior, and BAS-Impulsivity predicted proactive aggressive behavior partially mediated by SDO. These results improve understandings of the impact of personality on aggressive behavior and may have applied implications.

Support was provided for H1 as BAS positively predicted proactive (unprovoked) aggressive behavior. This is consistent with established rRST literature (Carver & Harmon-Jones, 2009; Corr & Perkins, 2009) suggesting approach-motivational personality dimensions predict aggressive behavioral responses in nonprovoking contexts. The study also found the BAS subscale of Impulsivity uniquely predicted proactive aggressive behavior, whereas the remaining three BAS subscales (Reward Interest, Goal-Drive Persistence, and Reward Reactivity) did not. This indicates proactive aggressive behavior is an actioned response to rapid, non-planned biological approach activation mechanisms rather than openness to new experiences or expectation of reward (Reward Interest), goal planning and maintenance (Goal-Drive Persistence), or experience of pleasure (Reward Reactivity). Results are consistent with a substantial body of literature that supports impulsivity predicting aggressive behavior across multiple indices (Bettencourt et al., 2006; Hecht & Latzman, 2015; Miller et al., 2012). The literature on aggression has often focused on higher-order personality constructs such as psychopathy and narcissism as predictors of unprovoked aggressive behavior (Reidy et al., 2011). The current study has progressed further to delineate, at a fundamental neuropsychological level, the approach-motivation personality dimension of impulsivity, which involves rapid and non-planned reactivity, predicts proactive verbal and physical aggressive behavior.

In a further endorsement of the rRST theory of personality in the prediction of aggressive behavior, H2 was also supported as BAS positively predicted reactive aggressive behavior. Parallel to the study’s initial findings, the BAS subscale of Impulsivity uniquely predicted reactive aggressive behavior, whereas the remaining three BAS scales did not. These findings suggest the approach motivation of Impulsivity, a factor within the BAS system, uniquely predicts aggressive behavior in provoking contexts. Such results concur with rRST theory and empirical evidence proposing provoked aggressive behavior in humans is motivated by approach personality traits derived within BAS (Corr & Cooper, 2016; Krupic et al., 2016). Results that support H2 also align with the General Aggression Model (Anderson & Bushman, 2002). This theory asserts aggressive behavior is a product of person variables (such as personality traits) and situational variables (such as provocation). The current study indicates the BAS personality trait of Impulsivity, together with provocation, motivates aggressive behavior. At a neuropsychological level of personality, the results of H1 and H2 indicate that Impulsivity predicts both proactive and reactive forms of verbal and physical aggressive behavior, which adds to the theoretical and empirical knowledge of personality predictors of aggression. These findings highlight aggression is a neuropsychological approach tendency to fast, impulsive action that predicts verbal and physical aggressive behavior.

Findings supporting H3 further clarify the interactions between each personality system, with Defensive Fight predicting reactive, but not proactive, aggressive behavior. In animal models, defensive aggression has been indicated to be controlled by the Fight component of FFFS (Corr & Perkins, 2009), but human models indicate defensive aggression may be potentiated by BAS, via the Fight component initiating BAS-related safety signals (Corr & Cooper, 2016). The current study’s findings support such BAS potentiation with positive correlations found between each of the four BAS subscales and Defensive Fight, and a lack of significant correlation between FFFS and Defensive Fight. This suggests defensive aggression in humans is potentiated via a more complex motivational pathway than in rodent behavioral literature. Defensive aggression may be the outcome of perceived provocation initiating rapid, early-stage BAS activation via Fight, whereby an impulsive, fast approach motivation is required to potentiate a protective, defensive behavioral response to imminent threat. The correlation between Defensive Fight and BIS was found to be significant in this study, whereas the correlation between Defensive Fight and FFFS was not, suggesting perceived threat may cause anxiety in individuals, rather than fear. This anxiety may provide the BAS-activated, impulse driven, safety signal that motivates defensive fight.

Interestingly, the current study found the rRST personality dimension of BIS also positively predicted unprovoked and provoked aggressive behavior. Furthermore, BIS and BAS-Impulsivity had significant positive intercorrelations. This is expected as literature indicates BAS and BIS systems do not operate with mutual exclusivity especially regarding anger and aggressive behavior (Smits & Kuppens, 2005). Rather than motivating an inhibitory response, BIS activation may interplay with BAS-Impulsivity to motivate a rapid, anxiety-driven approach response of aggression across unprovoked and provoked aggression. This concurs with urgency in aggressive response indicated in studies examining impulsivity in proactive (unprovoked) and reactive (provoked) aggressive behavior (Hecht & Latzman, 2015). Findings have indicated different facets of impulsivity predict each form of aggression. Proactive aggression is characterized by positive urgency, whereas reactive aggression is characterized by negative urgency (Hecht & Latzman, 2015). Future research that examines the potential moderating effect of BAS-Impulsivity on BIS in approach-avoidance conflict contexts would enrich knowledge of the BAS and BIS interactions in the prediction of proactive and reactive aggressive behavior.

Previous research has provided strong support for associations between poor impulse control (Seibert et al., 2010), neuroticism and anxiety (Nederlof et al., 2014), emotion regulation (Holley et al., 2017), and aggression. Current research suggests the connection between BAS-Impulsivity and BIS anxiety in the prediction of unprovoked and provoked aggression may be due to disinhibition and emotional dysregulation. Miller et al. (2012) provide initial support for this finding as proactive aggression was uniquely associated with personality traits related to disinhibition in a large sample of undergraduates. Reactive aggression was uniquely associated with personality traits associated with emotion dysregulation. Further research is required to clarify these processes in the prediction of unprovoked and provoked aggression from rRST personality dimensions. Both BAS and BIS each predicted proactive and reactive aggressive behavior, suggesting individuals that aggress in one form (e.g., proactive) are likely to aggress in other forms (e.g., reactive), consistent with mounting empirical evidence whereby a generality of personality traits is strongly correlated with aggressive behavior across both dualities (Bettencourt et al., 2006; Hecht & Latzman, 2015).

H4 was that BAS will positively predict proactive aggressive behavior through SDO. As previous analysis suggested the BAS facet of Impulsivity was the key component in aggression, this facet was used in the analysis. H4 had some support with BAS-Impulsivity predicting proactive aggressive behavior partially mediated by SDO. Findings indicate BAS-Impulsivity is a rapid, action-oriented approach motivational driver. The general disagreeableness and aggressive fight component to SDO (Pratto et al., 1994) may drive BAS-Impulsivity to elicit unprovoked aggressive behavior. Together with the positive correlations between SDO and proactive aggression, and the lack of correlation between SDO and FFFS, BIS, or Defensive Fight, results suggest SDO is related to a more predatory form of motivational approach than a defensive form of avoidance. In support of the dual-process motivational model structure to social attitudes (Duckitt & Sibley, 2010), the current findings suggest BAS-Impulsivity predisposes individuals to develop a SDO social attitude that motivates them to display associated behaviors, such as proactive aggression (Cohrs et al., 2012). In concurrence with rRST research (Corr, 2013; Corr & Perkins, 2009), the current findings further suggest proactive aggression is not related to negative motivations of avoidance based in FFFS, but to positive-approach motivations driven by BAS via SDO.

The significance of impulsivity predicting both proactive and reactive aggression is twofold. Both proactive and reactive aggressive behavior are motivated by rapid, urgent approach-oriented action. Rather than being influenced by fear, or avoidance of harm, aggression may be predicted from the biopsychological desire to approach. However, the nuanced path by which impulsivity predicts aggression differs depending on the form of aggression. Reactive aggression appears driven by urgent, approach/anxiety-motivated drive to take defensive action. Proactive aggression appears driven by urgent, approach/anxiety-motivated drive influenced by social attitudes seeking dominance. Anxiety has long been considered a predictor of aggression, further supported by this study, suggesting a need to address violence by reducing approach/action-orientation, rather than fear reduction.

The findings from this study are beneficial to creating interventions and treatments for individuals who aggress and reduce the health burden to society. Modifying social attitudes, such as SDO, via education may reduce proactive behavioral aggression. Anxiety management programs may assist in reducing both forms of aggression. Understanding how personality dimensions put individuals at greater risk of aggressing may assist identification of “at risk” individuals for treatment. Aggression has substantial financial and social costs to society at the individual level through potential interpersonal attacks and at the societal level through fear and medical burdens among other costs (Fairchild et al., 2019). By targeting specific individuals, treatments can be more targeted and effective.

A limitation of the study may be reduced external validity because of the sample. The participants were able to self-select into the study with the only controls as being from the United States and over the age of 18. A review found that self-selection into studies meant certain individuals were more likely to participate than others, such as individuals who were interested in the topic, wanted to contribute to science, or who valued the incentive offered (Keusch, 2015). While the demographic characteristics of the sample is close to the U.S. adult population, they are not exactly the same. While the United States includes 49% men (Duffin, 2020b), the current study includes 56% men. While the United States is 61% Caucasian (Duffin, 2020a), the current sample includes 84% Caucasian participants. The average age of the sample and the U.S. population is 38 years (Duffin, 2019). Therefore, some caution needs to be given regarding the generalizability of the study to the U.S. population.

The study may not generalize to outside the United States or Western countries. Most people in the world are not Western, educated, industrialized, rich and democratic (WEIRD; Henrich et al., 2010). The United States is less than 5% of the world’s population and Western countries are less than 12% of the world’s populations (Arnett, 2008). Studies have found large differences between countries on topics ranging from student learning patterns (Marambe et al., 2012) to different biological responses to anxiety and depression medication (Kazdin, 1999). Studies on aggression are arguably particularly important to examine between countries as geographical latitudinal differences in aggression have been found with higher aggression when the country is closer to the equator (Van de Vliert & Van Lange, 2019). A Chinese study even found within-country latitudinal differences in aggression with higher interpersonal conflict in southern China compared to northern China (Wei & Wang, 2020). While this study is an important study within a particular country, the results cannot be inferred to generalize to the entire human population on the weighty topic of personality and aggression.

The study contains limitations regarding selection bias. The study attempted to reduce selection bias issues by masking the true nature of the study and telling participants it concerned personality and goal-directed behavior. As per the ethical approval, participants were told the study regarded personality and aggression in the debriefing statement. Nevertheless, participants are still entering the study based on an interest in predictors of personality and this may mean the sample is less of a general sample. While Prime Panels has access to a large pool of participants, it does not have access to all people in the community and therefore the sample is still a convenience sample rather than a community sample.

The design of the study was cross-sectional and self-report. While previous research and theory suggested that the results were in a particular direction, the design of the study prohibits causal inference. Using a common method (self-report) within a cross-sectional design may also inflate the correlations between variables (Podsakoff et al., 2003). The use of self-report in an aggression study also means that participants may be more susceptible to social desirability bias (Latkin et al., 2017) and participants may have trouble with sufficient introspection to determine their responses (Greenwald & Banaji, 1995).

The primary concern for future research is to seek to overcome the country limitations of the current study. Based on concerns raised about the generalizability of American psychology studies (e.g., Arnett, 2008; Henrich et al., 2010; Sue, 1999), more studies on personality and aggression should be conducted in non-American countries. If possible, country comparisons should be conducted. For example, a recent study examined self-concept in 68 countries (Marsh et al., 2020) and therefore was legitimately able to make pan-human generalizations. Studies should particularly be conducted along different points in the earth’s geographic latitude to examine differences as north–south differences in aggression are arguably even more important than east–west differences (Van de Vliert & Van Lange, 2019; Wei & Wang, 2020).

In conclusion, the present study elucidates the relationship between rRST and aggression and the mediating mechanism of SDO. BAS and BAS-Impulsivity predicted both proactive and reactive aggressive behavior. There was an unexpected relation with BIS in predicting both proactive and reactive aggressive behavior. Defensive Fight predicted reactive, but not proactive, aggressive behavior. The prediction of proactive aggressive behavior from BAS-Impulsivity was partially explained by SDO. These findings may influence the development of tailored intervention and treatment programs for individuals at risk of aggressing. By understanding how particular personality traits and social attitudes are linked to specific forms of aggression, education can be tailored to inform individuals on how to improve dysfunctional aggressive behavior patterns. These findings overall support the dual-model distinction of proactive/unprovoked and reactive/provoked aggression and how these forms of aggression relate to basic neuropsychological approach and avoidance systems of personality. These systems may further account for the individual differences in social attitudes that mediate unprovoked aggression. Understanding individual differences that predict proactive and reactive aggressive behavior is essential to minimizing aggression.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nicola Farrell

Benjamin R. Walker

Author Biographies

Kristy-Elizabeth M. Parker has completed 4 years of psychological study and runs her own company that integrates psychological insights.

Nicola Farrell is a provisional psychologist having completed 4 years of psychological study, and leads an Allied Health team administering psychological services to children.

Benjamin R. Walker has a PhD in psychology and conducts research in a range of areas including personality, aggression, and attachment.

References

Anderson

C. A.

Bushman

B. J.

(2002). Human aggresson. Annual Review of Psychology, 53, 27–51. https://doi.org/10.1146/annurev.psych.53.100901.135231

Arnett

(2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist, 63, 602–614. https://doi.org/10.1037/0003-066X.63.7.602

Baughman

H. M.

Dearing

Giammarco

Vernon

P. A.

(2012). Relationships between bullying behaviours and the Dark Triad: A study with adults. Personality and Individual Differences, 52, 571–575. https://doi.org/10.1016/j.paid.2011.11.020

Beaver

J. D.

Lawrence

A. D.

Passamonti

Calder

A. J.

(2008). Appetitive motivation predicts the neural response to facial signals of aggression. Journal of Neuroscience, 28, 2719–2725. https://doi.org/10.1523/JNEUROSCI.0033-08.2008

Bettencourt

B. A.

Talley

Benjamin

A. J.

Valentine

(2006). Personality and aggressive behavior under provoking and neutral conditions: A meta-analytic review. Psychological Bulletin, 132, 751–777. https://doi.org/10.1037/0033-2909.132.5.751

Bowlus

A. J.

Seitz

(2006). Domestic violence, employment, and divorce. International Economic Review, 47, 1113–1149. https://doi.org/10.1111/j.1468-2354.2006.00408.x

Bushman

B. J.

(1995). Moderating role of trait aggressiveness in the effects of violent media on aggression. Journal of Personality and Social Psychology, 69, 950–960. https://doi.org/10.1037/0022-3514.69.5.950

Bushman

B. J.

Anderson

C. A.

(2001). Is it time to pull the plug on hostile versus instrumental aggression dichotomy?Psychological Review, 108, 273–279. https://doi.org/10.1037/0033-295X.108.1.273

Bushman

B. J.

Baumeister

(1998). Threatened egotism, narcissism, self-esteem, and direct and displaced aggression: Does self-love or self-hate lead to violence?Journal of Personality and Social Psychology, 75, 219–229. https://doi.org/10.1037/0022-3514.75.1.219

10.

Buss

Perry

(1992). The Aggression Questionnaire. Journal of Personality and Social Psychology, 63, 452–459. https://doi.org/10.1037/0022-3514.63.3.452

11.

Caprara

G. V.

Barbaranelli

Pastorelli

Perugini

(1994). Individual differences in the study of human aggression. Aggressive Behavior, 20, 291–303. https://doi.org/10.1002/1098-2337(1994)20:4<291::AID-AB2480200403>3.0.CO;2-B

12.

Carver

C. S.

Harmon-Jones

(2009). Anger is an approach-related affect: Evidence and implications. Psychological Bulletin, 135, 183–204. https://doi.org/10.1037/a0013965

13.

Chandler

Rosenzweig

Moss

A. J.

Robinson

Litman

(2019). Online panels in social science research: Expanding sampling methods beyond Mechanical Turk. Behavior Research Methods, 51, 2022–2038. https://doi.org/10.3758/s13428-019-01273-7

14.

Cohrs

J. C.

Kämpfe-Hargrave

Riemann

(2012). Individual differences in ideological attitudes and prejudice: Evidence from peer-report data. Journal of Personality and Social Psychology, 103, 343–361. https://doi.org/10.1037/a0028706

15.

Corr

P. J.

(Ed.). (2008). The reinforcement sensitivity theory of personality. Cambridge University Press.

16.

Corr

P. J.

(2013). Approach and avoidance behaviour: Multiple systems and their Interactions. Emotion Review, 5, 285–290. https://doi.org/10.1177/1754073913477507

17.

Corr

P. J.

Cooper

A. J.

(2016). The Reinforcement Sensitivity Theory of Personality Questionnaire (RST-PQ): Development and validation. Psychological Assessment, 28, 1427–1440. https://doi.org/10.1037/pas0000273

18.

Corr

P. J.

Hargreaves-Heap

Tsutsui

Russell

Seger

(2013). Personality and social attitudes: Evidence for positive-approach motivation. Personality and Individual Differences, 55, 846–851. https://doi.org/10.1016/j.paid.2013.07.012

19.

Corr

P. J.

Perkins

A. M.

(2009). Differentiating defensive and predatory aggression: Neuropsychological systems and personality in sex differences. Behavioral and Brain Sciences, 32, 274–275. https://doi.org/10.1017/S0140525X09990434

20.

Costa

McCrae

(1990). Personality disorders and the Five-Factor Model of personality. Journal of Personality Disorders, 4, 362–371. https://doi.org/10.1521/pedi.1990.4.4.362

21.

Crick

N. R.

Dodge

K. A.

(1994). A review and reformulation of social information-processing mechanisms in children’s social adjustment. Psychological Bulletin, 115, 74–101. https://doi.org/10.1037/0033-2909.115.1.74

22.

De Castro

B. O.

Veerman

J. W.

Koops

Bosch

J. D.

Monshouwer

H. J

. (2002). Hostile attribution of intent and aggressive behavior: A meta-analysis. Child Development, 73, 916–934. https://doi.org/10.1111/1467-8624.00447

23.

DeWall

C. N.

Anderson

C. A.

(2011). The general aggression model. In Shaver

P. R.

Mikulincer

(Eds.), Herzilya series on personality and social psychology. Humanaggression and violence: Causes, manifestations, and consequences (pp. 15–33). American Psychological Association. https://doi.org/10.1037/12346-001

24.

Dodge

K. A.

Coie

J. D.

(1987). Social-information-processing factors in reactive and proactive aggression in children’s peer groups. Journal of Personality and Social Psychology, 53, 1146–1158. https://doi.org/10.1037/0022-3514.53.6.1146

25.

Dolatyar

Walker

B. R.

(2020). Reinforcement sensitivity theory and mindfulness. Personality and Individual Differences, 163, 110089. https://doi.org/10.1016/j.paid.2020.110089

26.

Duckitt

Sibley

C. G.

(2010). Personality, ideology, prejudice, and politics: A dual-process motivational model. Journal of Personality, 78, 1861–1894. https://doi.org/10.1111/j.1467-6494.2010.00672.x

27.

Duffin

(2019). Median age of the resident population of the United States from 1960 to 2018. https://www.statista.com/statistics/241494/median-age-of-the-uspopulation/#:~:text=In%202018%2C%20the%20median%20age,even%20younger%2C%20at%2029.5%20years

28.

Duffin

(2020a). Percentage distribution of population in the United States in 2016 and 2060, by race and Hispanic origin. https://www.statista.com/statistics/270272/percentage-of-us-population-by-ethnicities/

29.

Duffin

(2020b). Total population in the United States by gender from 2010 to 2024. https://www.statista.com/statistics/737923/us-population-by-gender/

30.

Epps

Kendall

P. C.

(1995). Hostile attributional bias in adults. Cognitive Therapy and Research, 19, 159–178. https://doi.org/10.1007/BF02229692

31.

Fairchild

Hawes

D. J.

Frick

P. J.

Copeland

W. E.

Odgers

C. L.

Franke

Freitag

C. M.

De Brito

S. A.

(2019). Conduct disorder. Nature Reviews Disease Primers, 5, 1–25. https://doi.org/10.1038/s41572-019-0095-y

32.

Farrell

Sait

Walker

B. R.

(2020). Reinforcement sensitivity theory and goals in tattooed individuals. Personality and Individual Differences, 167, 110234. https://doi.org/10.1016/j.paid.2020.110234

33.

Farrell

Walker

B. R.

(2019a). Reinforcement sensitivity theory and the 2 × 2 standpoints model of achievement goals. Personality and Individual Differences, 139, 317–320. https://doi.org/10.1016/j.paid.2018.11.035

34.

Farrell

Walker

B. R.

(2019b). Reinforcement sensitivity theory and problem gambling in a general population sample. Journal of Gambling Studies, 35, 1163–1175. https://doi.org/10.1007/s10899-019-09850-3

35.

Ferguson

C. J.

Dyck

(2012). Paradigm change in aggression research: The time has come to retire the General Aggression Model. Aggression and Violent Behavior, 17, 220–228. https://doi.org/10.1016/j.avb.2012.02.007

36.

Fernandez

Walker

B. R.

(2019). Spirituality in the stands: Sports spectatorship, spirituality, wellbeing and personality. Journal of Spirituality in Mental Health. Advance online publication. https://doi.org/10.1080/19349637.2019.1654427

37.

Gleason

K. A.

Jensen-Campbell

L. A.

South Richardson

(2004). Agreeableness as a predictor of aggression in adolescence. Aggressive Behavior, 30, 43–61. https://doi.org/10.1002/ab.20002

38.

Gray

J. A.

McNaughton

(2003). The neuropsychology of anxiety: An enquiry into the function of the septo-hippocampal system (2nd ed.). Oxford University Press.

39.

Greenwald

A. G.

Banaji

M. R.

(1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4–27. https://doi.org/10.1037/0033-295X.102.1.4

40.

Hayes

A. F.

(2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. http://www.afhayes.com/public/process2012.pdf

41.

Hecht

L. K.

Latzman

R. D.

(2015). Revealing the nuanced associations between facets of trait impulsivity and reactive and proactive aggression. Personality and Individual Differences, 83, 192–197. https://doi.org/10.1016/j.paid.2015.04.021

42.

Henrich

Heine

S. J.

Norenzayan

(2010). The weirdest people in the world?Behavioral and Brain Sciences, 33, 61–135. https://doi.org/10.1017/S0140525X0999152X

43.

Hinduja

Patchin

J. W.

(2010). Bullying, cyberbullying, and suicide. Archives of Suicide Research, 14, 206–221. https://doi.org/10.1080/13811118.2010.494133

44.

Holley

S. R.

Ewing

S. T.

Stiver

J. T.

Bloch

(2017). The relationship between emotion regulation, executive functioning, and aggressive behaviors. Journal of Interpersonal Violence, 32, 1692–1707. https://doi.org/10.1177/0886260515592619

45.

Hosie

Gilbert

Simpson

Daffern

(2014). An examination of the relationship between personality and aggression using the general aggression and five factor models. Aggressive Behavior, 40, 189–196. https://doi.org/10.1002/ab.21510

46.

Huesmann

L. R.

(1998). The role of social information processing and cognitive schema in the acquisition and maintenance of habitual aggressive behavior. In Geen

Donnerstein

(Eds.), Human aggression: Theories, research, and implications for social policy (pp. 73–109). Academic Press.

47.

Kazdin

A. E.

(1999). Overview of research design issues in clinical psychology. In Kendall

P. C.

Butcher

J. N.

Holmbeck

G. N.

(Eds.), Handbook of research methods in clinical psychology (pp. 3–30). John Wiley.

48.

Keusch

(2015). Why do people participate in web surveys? Applying survey participation theory to internet survey data collection. Management Review Quarterly, 65, 183–216. https://doi.org/10.1007/s11301-014-0111-y

49.

Kilpatrick

D. G.

Ruggiero

K. J.

Acierno

Saunders

B. E.

Resnick

H. S.

Best

C. L.

(2003). Violence and risk of PTSD, major depression, substance abuse/dependence, and comorbidity: Results from the National Survey of Adolescents. Journal of Consulting and Clinical Psychology, 71, 692–700. https://doi.org/10.1037/0022-006X.71.4.692

50.

Krupic

Krizanic

Corr

P. J.

(2016). Personality and defensive behaviour: A factor analytic approach to threat scenario choices. Personality and Individual Differences, 94, 303–308. https://doi.org/10.1016/j.paid.2016.01.045

51.

Latkin

C. A.

Edwards

Davey-Rothwell

M. A.

Tobin

K. E.

(2017). The relationship between social desirability bias and self-report of heath, substance use, and social network factors among urban substance users in Baltimore, Maryland. Addictive Behaviors, 73, 133–136. https://doi.org/10.1016/j.addbeh.2017.05.005

52.

Marambe

K. N.

Vermunt

J. D.

Boshuizen

H. P. A.

(2012). A cross-cultural comparison of student learning patterns in higher education. Higher Education, 64, 299–316. https://doi.org/10.1007/s10734-011-9494-z

53.

Marsh

H. W.

Parker

P. D.

Guo

Pekrun

Geetanjali

(2020). Psychological comparison processes and self-concept in relation to five distinct frame-of-reference effects: Pan-human cross-cultural generalizability over 68 countries. European Journal of Personality, 2, 180–202. https://doi.org/10.1002/per.2232

54.

McQuade

J. D.

Breaux

R. P.

Gómez

A. F.

Zakarian

R. J.

Weatherly

(2016). Biased self-perceived social competence and engagement in subtypes of aggression: Examination of peer rejection, social dominance goals, and sex of the child as moderators. Aggressive Behavior, 42, 498–509. https://doi.org/10.1002/ab.21645

55.

Miller

J. D.

Lynam

Leukefeld

(2003). Examining antisocial behavior through the lens of the Five Factor Model of personality. Aggressive Behavior, 29, 497–514. https://doi.org/10.1002/ab.10064

56.

Miller

J. D.

Zeichner

Wilson

L. F.

(2012). Personality correlates of aggression. Journal of Interpersonal Violence, 27, 2903–2919. https://doi.org/10.1177/0886260512438279

57.

Nederlof

A. F.

Muris

Hovens

J. E.

(2014). Anger, anxiety, and feelings of delusional threat as predictors of aggressive attitudes: An experimental mood induction study in a non-clinical sample. Personality and Individual Differences, 57, 25–30. https://doi.org/10.1016/j.paid.2013.09.006

58.

Podsakoff

P. M.

Mackenzie

S. B.

Lee

J. Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. https://doi.org/10.1037/0021-9010.88.5.879

59.

Pratto

Sidanius

Stallworth

L. M.

Malle

B. F.

(1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67, 741–763. https://doi.org/10.1037/0022-3514.67.4.741

60.

Raine

Dodge

Loeber

Gatzke-Kopp

Lynam

Reynolds

Stouthamer-Loeber

Liu

(2006). The Reactive–Proactive Aggression Questionnaire: Differential correlates of reactive and proactive aggression in adolescent boys. Aggressive Behavior, 32, 159–171. https://doi.org/10.1002/ab.20115

61.

Reidy

D. E.

Zeichner

Seibert

L. A.

(2011). Unprovoked aggression: Effects of psychopathic traits and sadism. Journal of Personality, 79, 75–100. https://doi.org/10.1111/j.1467-6494.2010.00691.x

62.

Seibert

L. A.

Miller

J. D.

Pryor

L. R.

Reidy

D. E.

Zeichner

(2010). Personality and laboratory-based aggression: Comparing the predictive power of the Five-Factor Model, BIS/BAS, and impulsivity across context. Journal of Research in Personality, 44, 13–21. https://doi.org/10.1016/j.jrp.2009.09.003

63.

Shahzadi

Walker

B. R.

(2019). Reinforcement sensitivity theory and relationship satisfaction via mastery. Personality and Individual Differences, 139, 46–52. https://doi.org/10.1016/j.paid.2018.11.007

64.

Shahzadi

Walker

B. R.

(2020). Reinforcement sensitivity theory and adult attachment: A replication study. Current Psychology. Advance online publication. https://doi.org/10.1007/s12144-020-00685-5

65.

Sharpe

Desai

(2001). The revised Neo Personality Inventory and the MMPI-2 Psychopathology Five in the prediction of aggression. Personality and Individual Differences, 31, 505–518. https://doi.org/10.1016/S0191-8869(00)00155-0

66.

Sidanius

(1993). The psychology of group conflict and the dynamics of oppression: A social dominance perspective. In Iyengar

McGuire

(Eds.), Explorations in political psychology (pp. 183–219). Duke University Press.

67.

Smits

D. J. M.

Kuppens

(2005). The relations between anger, coping with anger, and aggression, and the BIS/BAS system. Personality and Individual Differences, 39, 783–793. https://doi.org/10.1016/j.paid.2005.02.023

68.

Sovereign

Walker

B. R.

(2020). Mind, body and wellbeing: Reinforcement sensitivity theory and self-cultivation systems as wellbeing influencers. Journal of Happiness Studies. Advance online publication. https://doi.org/10.1007/s10902-019-00216-5

69.

Sue

(1999). Science, ethnicity, and bias: Where have we gone wrong?American Psychologist, 54, 1070–1077. https://doi.org/10.1037/0003-066x.54.12.1070

70.

Thomsen

Green

Sidanius

(2008). We will hunt them down: How social dominance orientation and right-wing authoritarianism fuel ethnic persecution of immigrants in fundamentally different ways. Journal of Experimental Social Psychology, 44, 1455–1464. https://doi.org/10.1016/j.jesp.2008.06.011

71.

Van de Vliert

Van Lange

P. A. M.

(2019). Latitudinal psychology: An ecological perspective on creativity, aggression, happiness, and beyond. Perspectives on Psychological Science, 14, 860–884. https://doi.org/10.1177/1745691619858067

72.

Van Lange

P. A. M.

Rinderu

M. I.

Bushman

B. J

. (2017). Aggression and violence around the world: A model of Climate, Aggression, and Self-control in Humans (CLASH). Behavioral and Brain Sciences, 40, 1469–1825. https://doi.org/10.1017/S0140525X16000406

73.

Walker

Rowe

Quinsey

(1993). Authoritarianism and sexual aggression. Journal of Personality and Social Psychology, 65, 1036–1045. https://doi.org/10.1037/0022-3514.65.5.1036

74.

Wei

X. D.

Wang

F. Y.

(2020). Southerners are wiser than northerners regarding interpersonal conflicts in China. Frontiers in Psychology, 11, Article 225. https://doi.org/10.3389/fpsyg.2020.00225