The digital gaming industry has become more conspicuous over the past years. From an economic point of view, its growth began in the 1970s with the emergence of the Atari video game company, created by Nollan Busnell and Ted Dabney (Mora-Cantallops & Sicilia, 2018), and has now surpassed more established industries such as, for example, the cinema (Pinto, Coronel & Bresolin, 2013). In addition, the inclusion of digital video games in the daily lives of the population has a role in the social, cognitive and emotional development of children and young adults who enjoy this kind of entertainment (Ashinoff, 2014; Kühn, Gleich, Lorenz, Lindenberger, & Gallinat, 2014, Stansbury, Wheeler, & Buckingham, 2014).

There has been an increasing debate about the consequences of video gaming on people’s behavior and attitudes, as it had once occurred with movies and other forms of media. It is noteworthy that researchers have positively associated video gaming with a set of negative influences, such as greater cognitive accessibility to aggressive content (Anderson, Carnagey, Flanagan, Benjamin, Eubanks & Valentine, 2004), higher propensity for antisocial behavior (DeLisi, Vaughn, Gentile, Anderson & Shook, 2013; Gentile & Bushman, 2012; Mariano, 2016), greater occurrence of negative affective states (Anderson & Dill, 2000), and reduction in prosocial behaviors (Anderson et al., 2010; Bushman & Huesmann, 2014).

Intense debate has aroused among researchers who argue that this influence does not exist or that it is not relevant, such as Elson and Ferguson (2013), and those who confirm an association, such as Bushman and Heusmann (2014) and Krahé (2014). The probable association becomes more significant when the most successful games display content related to violence or unfavorable social messages, such as God of War and Red Dead Redemption (Gouveia, Mariano, Nascimento, Grangeiro, Medeiros, 2017; Kasumovic, Blake, Dixson, & Denson, 2015).

When comparing this discussion with others related to the effects of other media on antisocial behavior, exposure to violence in digital games is different due to permanent attention, interactivity and active psychological participation of players, which according to some authors could easily promote more negative effects, such as aggressive thoughts, than other media (Ferreira, Carneiro, Miguéis, Soares, & Esteves, 2009, Granic, Lobel & Engels, 2014).

Swing, Gentile and Anderson (2009), following this reasoning, argue that certain characteristics of digital games can potentiate lasting changes in behavior, such as: difficulty level control, allowing greater adjustment to the player’s expertise; recurrent application of specific skills, which characterizes a training situation; existence of an active learning process due to the players’ engagement; reward systems and immediate feedback; possibility of generalization of specific behaviors in broader contexts.

Therefore, it is still relevant to investigate and further understand the phenomena related to the exposure of youth to violent games (Anderson & Bushman, 2001; Gee, 2003). To explain and understand the effects of exposure to violent content in entertainment media, we will use the Risk Factor Paradigm that seeks to identify risk factors for delinquent behavior, seeking to design appropriate prevention measures (Farrington, 2000). Exposure to violence has been identified as a risk factor (among several others such as, for example, previous involvement in physical aggression, gender, and hostile attribution bias) (Bushman & Huesmann, 2006; Anderson et al., 2010). Studies that have included exposure to violence through media as a risk factor for aggressive and antisocial behavior have paved the way for this approach and for expanding knowledge about video game violence in the risk factor paradigm, such as Boxer, Huesmann, Bushman, O’Brien and Moceri (2009), Braun, Stopfer, Müller, Beutel and Egloff (2016), DeLisi et al., (2013), Exelmans, Custers, and Van den Bulck (2015), Gentile and Bushman (2012) and Mariano (2016).

Considering the existence of this effect triggers a whole series of repercussions. If the nature of the game plays an important role in the probability of manifestation of different behaviors, the knowledge produced by this area can subsidize, on the one hand, as it draws attention of digital game developers to which characteristics may favor the training of different behaviors and lead to a new perspective for the development of serious games (for example, aiming to reduce antisocial behaviors). On the other hand, deepening knowledge on the effects of digital games on behavior has the potential to provide information to the governmental organizations responsible for regulating the content production sector (Funk, Pizzo, & Baker, 2018).

Considering the relevance of this construct, the aim of the present study is to investigate the relationship between violent video games, dispositional traits and antisocial behaviors. The definition of personality used in this study was proposed by McAdams (2006), who conceives it as an individual variation represented by patterns of development, dispositional traits, characteristics of adaptation and self-definition of life, situated in culture and social context.

Next, we shall discuss the General Aggression Model, a widely used theoretical model in the study of this issue, as it is integrative and seeks to explain the processes that are involved in aggression, followed by evidence that supports or questions its assumptions (Arriaga, Monteiro & Esteves, 2008).

First we sought to investigate how the variables (VG = Violence in Games, AG = Agreeableness, OP = Openness, C = Conscientiousness, N = Neuroticism, E = Extroversion, AS = Antisocial Behavior, D = Delinquent Behavior, PA = Physical Aggression ; VA = Verbal Aggression, A = Anger; H = Hostility) of the study were related. Table 1 shows the means and standard deviations of the variables and the correlation scores among them.

Table 1

		Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12
1	VG	3.29	1.84
2	AG	4.37	0.69	-0.17**
3	OP	3.75	0.62	-0.09	0.29**
4	C	3.29	0.69	-0.13*	0.13*	0.15**
5	N	3.03	0.97	-0.01	0.06	0.12*	-0.20**
6	E	3.13	0.85	-0.00	0.21**	0.18**	0.21**	0.07
7	AS	2.23	1.19	0.14*	-0.24**	-0.05	-0.22**	0.06	0.01
8	D	1.21	0.75	0.01	-0.27**	-0.11*	-0.02	-0.11*	-0.11*	0.55**
9	PA	1.98	0.75	0.23**	-0.08	0.00	-0.14*	0.26**	-0.01	0.36**	0.11*
10	VA	3.01	1.17	-0.03	0.13*	0.23**	0.08	0.23**	0.04	0.09	-0.07	0.31**
11	A	2.64	1.02	-0.00	0.12*	0.16**	-0.16**	0.59**	0.11*	0.18**	-0.06	0.54**	0.45**
12	H	3.13	0.82	0.05	-0.02	0.10*	-0.08	0.26**	-0.05	0.14*	-0.09	0.32**	0.34**	0.41**

Note: * p <0.05 and ** p <0.01. VG = Violence in Games; AG = Agreeableness; OP = Openness; C = Conscientiousness; N = Neuroticism; E = Extroversion; AS = Antisocial Behavior; D = Delinquent Behavior; PA = Physical Aggression; VA = Verbal Aggression, A = Anger; H = Hostility.

There was a positive correlation between exposure to violent games and physical aggression (r = .23, p <0.001), the only dimension of the Buss-Perry Aggression Questionnaire related to the Scale of Violence in Video Games. In the questionnaire on Antisocial Behavior and Delinquency, a weak correlation was found between Violence in Video Games and the Antisocial dimension (r = .14, p = .03). For personality traits, there were two negative correlations between the factors Violence in Video Games and Conscientiousness (r = -.13, p = .03) and Agreeableness (r = -.17, p = .01).

The Conscientiousness factor also correlated negatively with Physical Aggression (r = -.14, p = .03). On this latter factor, other significant associations were found, for example, with the trait of Neuroticism (r = .26, p <.01) and Antisocial Behavior (r = .36, p <.01). The Verbal Aggression dimension, in turn, correlated with the Agreeableness (r = .13, p= .04) and Neuroticism (r = .23, p < .01) factors in addition to the Physical Aggression dimension (r = .31, p <.01). However, there was no statistically significant relationship between Verbal Aggression and exposure to violent games. Finally, when analyzing the variable sex, we observed a significant difference between men and women for Physical Aggression, t (247) = 2.09, p = .04, d = .27, and Verbal Aggression, t (247) = 2.27, p = .02, d = .30. Men (M = 2.10) presented higher rates of physical aggression than women (M = 1.90), but women (M = 3.14) presented higher rates of verbal aggression than men (M = 2.80).

In accordance with the correlation findings, we sought to investigate the predictive power of the variables for the Physical Aggression dimension (Dependent Variable) using simple and multiple regression analyses. First the factor Violence in Video Games (Independent Variable), the main objective of the study, was included, followed by the constituent variables of the scales and the sex variable, established as the specific objectives.

When the Violence in Video Games factor (Model 1) was included, a predictive power was observed (β = .23, p <.01). However, this model explains only 5.2% of the variance in Physical Aggression.

In Model 2, the Agreeableness, Openness, Conscientiousness, Neuroticism, Extroversion, Antisocial Behavior, Delinquent Behavior, Verbal Aggression, Anger and Hostility factors were included. Therefore, controlling these variables, we found that violence in video games still predicts physical aggression (β = .19, p <.01). On the other hand, we found that anger and antisocial behavior had better predictive power (β = .49, p <.01 and β = .22, p <.01, respectively). As shown in Table 2, Model 2 accounts for 42% of the variance explanation, versus 5.2% for the first model.

Table 2

	R	R²	F	Sig (F)	Beta	t	P
VG	0.227	0.052	13.428	0.001	0.227	3.664	0.001
VG +	0.649	0.42	15.628	0.001	0.187	3.651	.000
AM +					-0.045	-.808	.420
AB +					-0.049	-.910	.364
C +					0.025	.460	.646
N +					-.062	-.994	.321
E +					-.054	-1.011	.313
AS +					.222	3.378	.001
D +					.003	.055	.957
AV +					.086	1.474	.142
R +					.490	6.894	.000
H					.069	1.220	.224
VG +	0.666	0.443	14.378	0.001	.144	2.755	.006
AM +					-.008	-.144	.886
AB +					-.036	-.682	.496
C +					.012	.219	.827
N +					-.047	-.755	.451
E +					-.067	-1.261	.208
AS +					.182	2.756	.006
D +					.003	.043	.966
AV +					.090	1.567	.118
R +					.512	7.262	.000
H +					.091	1.617	.107
I +					-.043	-.845	.399
S					-.179	-3.075	.002

Note: PA: Physical Aggression; Violence in Games = VG; Agreeableness = AG; Openness = OP; Conscientiousness = C; Neuroticism = N; Extroversion = E; Antisocial = AS; Delinquency = D; Verbal Aggression = VA, Anger = A; Hostility = H; Sex = S; Age = I.

Finally, Model 3 included sex and age as independent variables. The sex variable presented a predictive power of β = -.17, p <.01. This model explains 44.3% of the variance with violent video gaming and it predicts physical aggression, β = .14, p <.01. However, anger (β = .51, p <.01), antisocial behavior (β = .18, p <.01) and sex were the best predictors.

The aim of the present study was to investigate the relationship between violent video gaming, dispositional traits and antisocial behavior. Evidence suggests that exposure to violent games may explain the manifestation of antisocial behaviors, particularly those related to physical aggression. Moreover, evidence suggests that a set of variables, such as those related to personality, sex, and specific affective states, have considerable explanatory power, and studies that seek to understand these interactions may be promising avenues for further research.

The scientific literature indicates a positive relationship between youth exposure to violent games and aggression, which corroborates the data found in the present study and studies discussed below. Among these studies, the meta-analysis of Greitemeyer and Mügge (2014) revealed an increase in variables related to aggression. In the study by Gentile, Swing, Anderson, Rinker, and Thomas (2016), usual players of violent games demonstrated desensitization to aggression.

The data found by Hasan et al., (2013) showed that violent video game players presented higher levels of aggression. Kim and Sundar (2013) have identified increased aggressiveness in players who have used weapon-shaped controllers. Finally, in the study by Saleem, Anderson and Gentile (2012), children playing violent video games presented more offensive behaviors and less prosocial behaviors. In the present study, the preference for violent digital games can be a predictor of physical aggression even if the effect of several variables such as personality traits, anger, antisocial behavior or sex are controlled.

The regression analyses revealed a predictive power of experience with violent digital games regarding physical aggression. These findings support the GAM prediction that violent media content influences the manifestation of aggressive behavior. In this sense, playing violent video games would increase the expectation that aggressive situations should be treated aggressively, leading to increased aggressiveness (Anderson & Bushman, 2002). Anger, as a predictor of aggressive behavior, would be a facilitator of aggressiveness as it was the best predictor of physical aggression in the present study. In addition, studies point to increased aggressiveness due to cathartic reactions and increase in the likelihood of a new aggressive behavior, which is different from the cathartic hypothesis that reaction causes a decrease in aggressiveness. These findings corroborate the GAM integrative hypothesis on human aggression and its cyclic triggering (Bushman, Baumeister, & Stack, 1999; Tedeschi & Felson, 1994).

The correlation between exposure to violent games and antisocial behavior discussed in the results has also been the object of studies on delinquency and electronic gaming. Using the Risk Factor Paradigm, exposure to violent games is a risk factor for delinquent behavior (Boxer et al., 2009; DeLisi et al., 2013, Exelmans, Custers, & Van den Bulck, 2015; Gentile & Bushman, 2012). Within this perspective, violent games would be another factor among many others that comprise the problem of aggression and delinquent behavior. The second most important factor to explain physical aggression was antisocial behavior, which was expected, since aggression can be understood as another antisocial behavior (Pimentel, 2004).

An alternative interpretation for the data found may suggest, in agreement with several criticisms to the GAM model, that exposure to violent games, although taking place in the explanatory model of physical aggression, may be overvalued to the detriment of other variables with greater predictive power (Elson & Ferguson, 2013). Therefore, the preference for violent digital games could just be another consequence, for example, of more stable dispositional traits and related to antisocial behaviors. In addition, participants with a high level of Neuroticism and low levels of Agreeableness and Conscientiousness, for example, might be more susceptible to the effect of game content on their behavior (Markey & Markey, 2010), leading to a more dispositional interpretation of the effects of media on behavior.

However, as GAM assumes relevant variables of input, individual characteristics (Anderson & Bushman, 2002) as well as a set of evidence favorable to an interaction relationship between these variables for understanding the relationship between violent game playing and antisocial behavior. The results found with the participants of this study indicated a negative correlation between violent games and Conscientiousness and Agreeableness traits, confirmed by the results found by Anderson et al., (2004). On the other hand, Chory and Goodboy (2011) found no correlation between Conscientiousness and violent video game playing but corroborated the negative correlation with Agreeableness. Thus, further in-depth research must be conducted, since new empirical evidence, particularly focusing on testing these relationships, can promote positive discussions on the subject.

With regard to sex, this variable was also considered one of the risk factors associated with violent games and aggressive and antisocial behaviors, as suggested in the findings of the present study. Therefore, these findings corroborate the literature that boys are more attracted to violent games (Lemmens, Valkenburg, & Peter, 2011; Vandercammen & Vandenbrande, 2011).

However, this study is not without limitations. Among them, three deserve attention. First, the results are based on self-reporting measures that may lead to bias in responses due to social desirability. Second, there may be other important risk factors to better detail the problem that were not included in the research. Finally, the cross-sectional study precludes inference of causality. The results are restricted to the study sample.

Despite the limitations, the study can help to explain the problem involving violent games and aggression by raising questions about possible predictive factors. On the other hand, although the significance of video game violence has been proven to understand physical aggression, the role of anger and antisocial behavior, as well as sex, should be emphasized. These variables proved to be better predictors of aggressive behavior. Therefore, further studies should include these variables in an experimental design to investigate the possible effect of violent games on the behavior of players of different age groups. For example, the difference between the time of exposure to games, which was not considered in this research, and use of specific types of games could be investigated.

Finally, the predictive power of video game violence in aggressive behavior, after controlling for several variables, such as personality traits, was confirmed (Barlett & Anderson, 2012; Tang & Fox, 2016). The present study contributes to those that have investigated the role of violent media in aggressive and antisocial behaviors (Braun, Stopfer, Müller, Beutel, & Egloff, 2016; DeLisi et al., 2013), promoting the development in the field in Brazil.