Violent Video Game Players and Non-Players Differ onFacial Emotion RecognitionRuth L. Diaz, Ulric Wong, David C. Hodgins, Carina G. Chiu, and Vina M. Goghari*
Department of Psychology, University of Calgary, Calgary, Canada
Keywords: video games; violence; social cognition; cognition; facial recognition; emotions
INTRODUCTION
Video game playing is becoming a universal form ofentertainment. Approximately 58% of North Americansplay video games (Entertainment Software Associationof Canada, 2013; Entertainment Software Association ofUnited States of America, 2013), with males playing anaverage of 18 hr a week and females 6.5 hr (Phan,Jardina, Hoyle, & Chaparro, 2012). Violent video gamesare among the top best-selling games (EntertainmentSoftware Association of Canada, 2012). Violent videogame playing has been demonstrated to influence bothneurocognition and social cognition (for reviews seeBailey, West, & Anderson, 2011; Barlett, Anderson, &Swing, 2009), and has been associated with differentbehavioral patterns (Anderson et al., 2010). Given thepopularity of violent video game playing (EntertainmentSoftware Association of Canada, 2013; EntertainmentSoftware Association of United States of America,2013), it is necessary to assess how violent video gameplaying influences players’ ability to recognize facialexpressions, a key contributor to successful socialinteractions (Halberstadt, 2003). Only three studies(Bailey & West, 2013; Kirsh & Mounts, 2007; Kirsh,Mounts, & Olczak, 2006) have explored the effects ofviolent video game playing on the recognition of facial
expressions, but none have investigated the effects ofchronic violent video game playing on the recognition offacial expressions. Therefore, the goal of this inves-tigation was to compare facial emotion recognition inindividuals who were frequent violent video gameplayers and those who were not video game players,using an experimental control task to control for generalperceptual and cognitive changes that may also occur.The positive and negative effects of violent video
game playing on neurocognition and social cognition(for reviews see Bailey et al., 2011; Barlett et al., 2009)may be reflected in facial emotion recognition abilities.
Contract grant sponsor: Natural Sciences and Engineering ResearchCouncil.Conflicts of interest: None.�Correspondence to: Dr. Vina M. Goghari, Department of Psychology,Clinical Neuroscience of Schizophrenia Laboratory, AdministrationBuilding, 2500 University Drive NW, University of Calgary, Calgary,AB, Canada T2N 1N4.E-mail: [email protected]
Received 8 September 2014; Revised 8 June 2015; Accepted 9 June2015
DOI: 10.1002/ab.21602Published online XX Month Year in Wiley Online Library(wileyonlinelibrary.com).
Particularly, there are three key reasons why facialemotion recognition may be associated with violentvideo game playing: (i) changes to general andperceptual cognitive processes; (ii) changes in socialmental schemas, behavior, and personality; and (iii)changes in emotion processing.First, the recognition of facial expressions involves
general perceptual and cognitive processes such asexecutive functions, sustained attention, sensorimotorfunction, and information processing speed (Mathersulet al., 2009); deficits in these cognitive processes havebeen associated with abnormal recognition of facialexpression. For example, deficits in cognitive function-ing have been associated with impaired recognition ofemotions in patients with schizophrenia (Lee et al.,2009) and aggressive individuals (Hoaken, Allaby, &Earle, 2007). Research has demonstrated that videogame playing can have both positive and negativeinfluences on perception and cognition. Violent videogame playing has been shown to increase hand–eyecoordination (Barlett et al., 2009; Griffith, Voloschin,Gibb, & Bailey, 1983), visuospatial cognition (Green &Bavelier, 2003; West, Stevens, Pun, & Pratt, 2008), andvisual selective attention (Bavelier, Achtman, Mani, &F€ocker, 2012; Castel, Pratt, & Drummond, 2005; Green& Bavelier, 2003), and to have a negative impact onexecutive functioning (Bailey, West, & Anderson, 2010;Wang et al., 2009). These changes in perception andcognition could influence facial emotion recognition ingamers.Second, facial emotion recognition is related to
individual differences in personality (Honk, Tuiten, deHaan, Van denHout, & Stam, 2001), mood, and behavior(Bediou et al., 2005; Bediou et al., 2007; Bourke,Douglas, & Porter, 2010; Goghari, MacDonald, &Sponheim, 2010; Surcinelli, Codispoti, Montebarocci,Rossi, & Baldaro, 2006). In addition, facial emotionrecognition is based on the appraisal of the emotionexpressed (Adolphs, 2002; Bruce & Young, 1986;Haxby, Hoffman, & Gobbini, 2000). This appraisaldepends on higher level cognitive processes that activateexisting cognitive schemas, which allow the evaluationand labeling of the emotion (Adolphs, 2002; Bruce &Young, 1986; Haxby et al., 2000) as well as theactivation of a response to that emotion, throughsimulation, to allow the understanding of the emotionalstate of others (Adolphs, 2002). Changes in thoseemotional schemas may, therefore, produce changes inthe experience and appraisal of emotions (Bruce &Young, 1986). Violent video game playing has beenassociated with changes in social schemas, behavior, andpersonality over time (Anderson et al., 2010; Anderson& Bushman, 2002; Buckley & Anderson, 2006). In aninfluential meta-analysis, Anderson et al. (2010)
suggested that both short-term and long-term exposureto violent video games increased aggressive behaviors,feelings, and cognitions, increased physiologicalarousal, and decreased prosocial behavior. Exposure toviolent video games also increased desensitization toreal life violence and reduced empathy (Anderson et al.,2010). Moreover, increased time spent playing violentvideo games was associated with more aggression(Anderson & Bushman, 2002). Drawing upon theGeneral Learning Model (GLM; Buckley & Anderson,2006), these changes can be produced through a cyclicalprocess where the interaction between situationalvariables (e.g., video game characteristics and otherenvironmental factors such as exposure to real lifeviolence) and players’ personal characteristics (e.g.,gaming experience, behavioral tendencies, and mentalemotional schemas) can create different cognitive,emotional, and physiological responses to aspects ofthe game. This could in turn lead to changes inknowledge schemas, which could later influencebehavior, the processing of emotions, and producechanges in personality.Moreover, the reward system andthe satisfaction of players’ psychological needs (Ryan,Rigby, & Przybylski, 2006) make video games effectivein fostering the learning of new behavior and the creationof new cognitive schemas (Buckley & Anderson, 2006).Through the mechanisms explained by the GLM,chronic violent video game playing may affect therecognition of facial emotions.Third, the recognition of facial expressions is affected
by the way an individual processes emotions in general.For example, bias toward negatively valenced informa-tion has been associated with impaired recognition offacial expressions in individuals with certain personalitytraits (e.g., Surcinelli et al., 2006) and mood disorders(e.g., Bourke et al., 2010). Research has suggested waysin which violent video games might affect emotionprocessing in general. For example, exposure to violentvideo games can produce attentional bias towardnegatively valenced information (Kirsh, Olczak, &Mounts, 2005) and can induce a hostility attributionbias (Anderson, Gentile, & Buckley, 2007; Bushman &Anderson, 2002), increasing the likelihood of acting inan aggressive manner (Hasan, B�egue, Scharkow, &Bushman, 2013; Kirsh, 1998). Moreover, the interactivenature of video game playing can elicit several emotionsin players depending on the content of the game(Anderson et al., 2010; Anderson & Ford, 1986; Frome,2007; Jansz, 2005; Weber, Ritterfeld, &Mathiak, 2006),the level of difficulty (Anderson et al., 2010; Frome,2007), and the mental schemas that players have aboutwhat a specific situation represents (Frome, 2007).Violent video games, especially first person shootinggames, are quite realistic, involving scenes of brutal
killing, blood, and gore that resemble real life scenes(Frome, 2007; Jansz, 2005;Montag et al., 2012). In orderto be successful in the game, players have to ignore (orsupress) the emotions produced by those scenes, leadingto an alteration of their mental schemas related to thoseemotions (Buckley & Anderson, 2006). Repeatedrehearsal of these new cognitive schemas might producelower sensitivity to unpleasant stimuli in general,through the process of habituation (Bartholow,Bushman, & Sestir, 2006; Buckley & Anderson, 2006;Carnagey, Anderson, & Bushman, 2007; Jansz, 2005;Montag et al., 2012). In summary, violent video gameplayingmay induce negative processing biases and thesechanges may influence players’ emotion processing andfacial emotion recognition specifically.Given that this is an emerging area of research, there
are only three studies that have evaluated video gameplaying and facial emotion recognition. In the first study,Kirsh et al. (2006) found that participants high in violentmedia consumption (TV, movies, and video games) werefaster at identifying a neutral face morphing into anangry face than one morphing into a happy face. Theopposite pattern was found in participants low in violentmedia consumption. The fact that this study combinedboth violent video game playing and the consumption ofother media violence (TV and movies), makes specificclaims of violent video game playing difficult. In asecond study, Kirsh and Mounts (2007) found thatindividuals who played a violent video game for 15minwere faster at identifying angry faces and slower atidentifying happy faces compared to those who played anon-violent video game for the same duration. Inaddition, Bailey and West (2013) found that 10 hours ofviolent video game playing decreased neural activityrelated to the detection of happy faces, while thedetection of angry and neutral faces was not affected.The higher sensitivity toward the recognition of angryfaces found by Kirsh et al. (2006, 2007) was notobserved in this study. In summary, playing violentvideo games seems to affect the recognition of facialemotions, but those effects, especially related to negativefacial expressions, are inconsistent and appear to beinfluenced by the nature of the facial recognition taskperformed.Given the limited literature, several major areas
remain open for investigation regarding the associationbetween violent video game playing and facial emotionrecognition. First, there is no research examining theeffects of violent video game playing on the recognitionof a wider variety of common facial expressions,including fear, sadness, and disgust, using a task thatallows for the analysis of both accuracy and reactiontime. Second, the existing literature is limited to theeffects of short-term exposure on violent video game
playing, and not on high frequency (i.e., two or morehours a day) or long term exposure (i.e., several years).Understanding the impact of long-term exposure tovideo game playing is critical. Currently, the AmericanAcademy of Pediatrics recommends less than 2 hr a dayof television and video game playing combined toreduce negative effects of media on development(American Academic of Pediatrics, 2013). Third,previous research has not evaluated whether generalcognitive deficits could account for any facial recog-nition abnormalities. Face perception involves therecognition of two distinct characteristics, the invariablefacial structures that allow the identification of personalcharacteristics such as gender or age, and the changeablefacial structures that allow the identification of emotionsand social communication (Haxby et al., 2000). Both therecognition of identity and the recognition of facialexpressions require a visual representation of faces thatis processed in the same area of the brain. Thus, it isimportant to assess possible group differences in thisinitial state of visual processing, as well as for otherpossible cognitive deficits. Fourth, previous studies havenot assessed players’ current mood, which could also becontributing to deficits in emotion recognition (fordepression, see Bourke et al., 2010; and for anxiety, seeSurcinelli et al., 2006) and have been related to chronicvideo game playing (Mentzoni et al., 2011; Starcevic,Berle, Porter, & Fenech, 2011).We used a task with two conditions to measure both
invariable (gender; Haxby et al., 2000) and changeablefacial structures (emotions; Haxby et al., 2000) tocontrol for other perceptual and cognitive changes thatcould also occur, as well as the influence of cognitivechanges produced by violent video games in therecognition of facial expressions. This is a commonlyused design to better isolate specific cognitive processesin populations with mood and cognitive changes (e.g.,schizophrenia and depression; Bediou et al., 2005;Goghari et al., 2011; Macdonald, 2008). The presentstudy evaluated three questions: (i) Is facial emotionrecognition generally altered in violent video gameplayers (i.e., individuals who play two or more hours ofviolent video games a day) versus non-gamers?; (ii) isthere a differential effect of violent video-game play onthe accuracy and speed of recognition of different facialemotions?; and (iii) does the intensity of the facialemotion presented effect recognition differentially inviolent video game players compared to non-gamers?Last, we explored whether the relationship betweenother forms of media consumption (i.e., television andmovies) and mood were associated with group differ-ences in facial emotion recognition. We predicted thatthrough the changes in cognition, emotion processing,and personality induced by chronic violent video game
playing (Buckley & Anderson, 2006), the judgement offacial emotions, but not the identification of identity,would be affected by chronic violent video gameplaying. We also predicted that, compared to non-gamers, violent video gamers would show a differentialpattern of performance on the different facial emotions.
METHODS
Participants
The sample consisted of 152 undergraduate students atthe University of Calgary, consisting of 73 males and 79females, with an average age of 20.45 (SD¼ 3.12) years.Undergraduate students were invited to participate basedon a pre-screen question administered through theDepartment of Psychology’s online research participa-tion system. The Conjoint Faculties Research EthicsBoard at the University of Calgary granted ethicsapproval for this project (Ethics 7133). Informed consentwas obtained from participants prior to participation inthe study.A pre-screen question asked participants about their
frequency of video game playing (0, less than an hour,1–2, 2–3, 3–4, 4–5, and more than 5 hr). These analysesfocused on the subgroup of respondents who did not playvideo games (0 hr; n¼ 69) or played two or more hours aday, including games with violent content, as definedbelow (n¼ 83). Fifty-nine percent of the group ofplayers reported playing only violent content videogames, while the remainder reported playing a mix ofboth violent and non-violent games. A higher percentageof males (63%) than females (50%) reported playingonly violent content games. The games containingviolence reported as most frequently played were: TheElder Scrolls: Skyrim; League of Legends, Diablo III,Call of Duty Black Ops II, and StarCraft II. On average,players self-reported playing 9.34 hr (SD¼ 10.52) perweek of violent video games. We were concerned thatthere might be significant variance in players’ evaluationof the violent content in their video games due todifferent individual definitions of violence (Carnagey &Anderson, 2004) or desensitization to violence fromlong-term play (Ivarsson, Anderson, Akerstedt, &Lindblad, 2013). Therefore, we used standardizedratings of violent content from the EntertainmentSoftware Rating Board (ESRB)—a non-profit, regula-tory body that provides guidance on video game contentfor consumers (ESRB, 2014). Indeed, using ESRB data(see next section for a detailed explanation of theprocedure used), the calculated number of hours perweek that players spent playing games which includedboth fantasy and realistic violence (M¼ 19.55,SD¼ 15.86) or games that included realistic violencealone (M¼ 14.13, SD¼ 13.18) were higher than
players’ self-reported estimates, suggesting that playerswere underestimating their exposure to violent videogame content.
Materials
Questionnaires assessed participants’ demographic,aggression, and mood characteristics. A facial recog-nition task was used to determine the accuracy andreaction time of identification of the emotional expressionor gender of a face (Bediou et al., 2005; Bediou et al.,2007). The presentation order of both the questionnairesand the task was counterbalanced.1
Video Game and Media ConsumptionMeasuresVideo game questionnaire. We developed an
11-item video game usage questionnaire. Participantswere asked to indicate whether or not they have played,how many hours a day they spent playing, how long theyhave been playing, and how many hours they spentplaying in a typical week. To determine participants’ playbehavior and preferences, they were asked to indicatehow many years, as well as how many hours a week theyspent playing video games that contain “torture, blood,and gore, or gratuitous killing of humans, animals, or anyother creatures” (adapted from Sigurdsson, Gudjonsson,Bragason, Kristjansdottir, & Sigfusdottir, 2006). Thenumber of hours reported playing this type of video gamewas used as a self-report measurement of violent gaming.Also, participants listed their three most frequentlyplayed games, the number of times per week they playedeach game, and the duration of each play session. Therating and level of violence of each gamewas determinedusing the rating classification, the rating summary (whenavailable) and content descriptors provided by the ESRB.A gamewas classified as non-violent if the ESRB contentdescriptor of the game did not contain the word violence,and/ or references to blood, gore, torture, and killing orhitting (e.g., Tetris). A game was classified as fantasy/cartoon violence if the content descriptor and/or ratingsummary of the game had the words fantasy violence orcartoon violence on it, even though references to bloodwere presented (e.g., The Legends of Zelda). A gamewasclassified as realistic violence if the ESRB contentdescriptor, and/or rating summary of the game containedthe words violence, intense violence, blood, gore, torture,or killing (e.g., Diablo III). Players’ consumption level ofspecific types of violence in the games most frequentlyplayed (i.e., non-violence, fantasy/cartoon violence, andrealistic violence) was calculated by multiplying the
1Statistical analyses were not run to determine if the order of thepresentation of both, the questionnaires and the task had any significanteffects on the outcome.
Aggr. Behav.
4 Diaz et al.
number of times they reported playing each game perweek by the number of hours played per session. Theseresults were used to objectively classify the gamers asviolent video game players (played either violentgames only, or a mix between violent and non-violentgames).Media consumption questionnaire. Partici-
pants were asked how many hours a day they watchedtelevision (TV) shows, specifying hours and minutes foreach day of the week, how often they watched differentTV genres (i.e., sitcom, series, sports, etc.), and howmany TVepisodes they watched a month that containedtorture or gratuitous killing of humans or animals. Thequestions and definition of violent media were adaptedfrom Sigurdsson et al. (2006). Similar questionsassessed movie consumption.Aggression and mood measures. The Buss
and Perry (1992) Aggression Questionnaire, a 29-iteminventory, was used to assess trait levels of aggressive-ness. Participants rate their level of agreement with eachstatement using a five point Likert scale. The SpielbergerTrait Anxiety Inventory Form Y (STAI-Y; Spielberger,Gorsuch, Lushene, Vagg, & Jacobs, 1983) was used todetermine trait levels of anxiety. The STAI-Y is a20-item inventory that asked participants to rate howthey generally feel on a four point scale (i.e., 1 almostnever to 4 almost always). The Beck DepressionInventory II (BDI-II; Beck, Steer, & Brown, 1996)was used to assess the current severity of participants’depressive symptomatology. The BDI-II consists of 21questions, each containing four statements. Participantsindicated which of the four statements best describedhow they have felt in the past 2 weeks.Facial recognition task. The facial recognition
task, adapted from Bediou et al. (2005) study, consistedof two conditions, the facial emotion recognition, andthe gender (experimental control) condition. The taskwas presented on a computer using E-PRIME version 2.In each condition, a fixation cross was presented in themiddle of the screen for 250ms, then one face waspresented for 1,000ms. In both conditions, the faceswere presented at 10 different levels of intensity. Thelevels of intensity for each emotion were later groupedinto three intensities: low (10–30%), medium (40–70%),and high (80–100%) for further analyses. Neutral faces(0% of an emotion) were not included in analyses. Afterthe presentation of each face, participants chose anemotion or a gender label from a list of choices (i.e., 1anger, 2 disgust, 3 fear, 4 happy, 5 sad, and 6 neutral; or 1male, 2 female depending on the condition). Participantshad unlimited time to respond. Both the order of thefaces and the task condition were randomized.Facial emotion recognition condition. The
stimuli consisted of 220 faces generated by a computer
program that transformed each face from a neutralexpression to a 100% emotional expression, at 10%increments. This condition involved viewing faces withsix different emotions (angry, disgust, fear, happy, sad,or neutral) presented one at a time, and distinguishingbetween these emotions by selecting one of the sixemotions listed on the computer screen. The list choice(i.e., 1 anger, 2 disgust, 3 fear, 4 happy, 5 sad, and 6neutral) was presented until the participant responded.Two male and two female faces were used. The order ofpresentation of the faces at different levels of intensitieswas randomized for each participant.Gender recognition condition (experimental
control condition). The stimuli consisted of 209faces generated by a computer program that transformedeach face from a gender neutral face to a 100% male or100% female face, at 10% increments. This conditioninvolved viewing faces of either a male or a female, andto determine the gender of the face presented on thecomputer screen. The list choice (i.e., 1 male, 2 female)was presented on the screen until the participantresponded. The no-gender face was created by averaging20 male and 20 female faces using a computer graphicalmanipulation. The same two male and two female facesused in the emotion recognition condition were used inthis condition.
Statistical Analysis
Analyses were conducted using SPSS version 22. x2
and t-tests were used to compare groups on sex, race,ethnicity, media consumption, aggression, depression,and anxiety. To determine whether emotion recognitionaccuracy in general differed from gender recognitionaccuracy, a 2 group (non-gamer, violent video gamer)� 2 sex of participant (male, female)� 2 facialrecognition condition (emotion, gender) mixed modelanalysis of variance (ANOVA)was conducted. To assesswhether the individual emotions differed with regards toaccuracy, a 2 group (non-gamer, violent video gamer)� 2 sex of participant (male, female)� 5 emotion(anger, disgust, fear, happy, sad) mixed model ANOVAwas conducted. To determine the influence of intensityon emotion accuracy, a 2 group (non-gamer, violentvideo gamer)� 2 sex of participant (male, female)� 3intensities (low, medium, high) mixed model ANOVAwas conducted on emotions that demonstrated asignificant difference between groups in question 2.To assess the impact of speed of response, reaction timeanalyses for accurate responses were conducted on allthe emotions using a 2 group (non-gamer, violent videogamer)� 2 sex of participant (male, female)� 5emotion (anger, disgust, fear, happy, sad) mixedmodel ANOVA. Significant effects on the ANOVAswere followed up with Bonferroni corrected post hoc
Aggr. Behav.
Violent Video Games and Emotion Recognition 5
testing. As sphericity could not be assumed, theGreenhouse–Geisser correction was reported for Fstatistic scores. To assess the relationship betweenfacial recognition variables that showed a significantdifference between groups and measures of moodand aggression, two sets of analyses were conducted.First, three 2 group (non-gamer, violent video gamer)� 2 sex of participant (male, female) ANOVAs onaggression, anxiety, and depression were conducted todetermine the association between violent video gameplaying and these variables. Then, 2 group (non-gamer,violent video gamer)� 2 sex of participant (male,female) ANOVAs on the emotion recognition variablesthat showed significant difference between groups, usingaggression, anxiety, or depression as covariates, wereconducted.
RESULTS
Participant Characteristics
Participant characteristics and statistics for violentvideo game players and non-gamers are presented inTable I. There were no differences between the groupsfor age or race/ethnicity; however, the two groupsdiffered significantly for the sex of the participant. Thepercentage of male participants in the violent videogame players group was considerably higher than thenon-gamer group (69% vs. 23%), thus, participant sexwas included as a between subjects variable in theanalyses. Violent video game players reported a higherweekly amount of media consumption and higher self-
report levels of aggression, trait anxiety, and depressionseverity than non-gamers.
Accuracy
A2 group (non-gamer, violent video gamer)� 2 sex ofparticipant (male, female)� 2 facial recognition con-dition (emotion, gender) ANOVA evaluated whetherfacial recognition accuracy differed for the emotion thanthe gender recognition condition among violent videogame players and non-gamers. A significant main effectof facial recognition condition was observed whenaveraging across groups, F (1,148)2¼ 1434.78, P<.001,with participants being more accurate at recognizing thegender of a face than the emotion. Furthermore, therewasa main effect of the sex of participant when averagingacross groups, F (1,148)¼ 6.21, P¼.014, hp
2¼ .040,with females being more accurate than males. There wasno main effect of group in the overall performance, whenaveraging across conditions, F (1,148)¼ 0.03, P¼.875,hp
2< .001. The interaction between condition and groupwas not significant, F (1,148)¼ 0.01, P¼.909, hp
2
< .001, demonstrating that both groups were similar inboth the emotion and gender recognition conditions.Furthermore, there were no significant interactionsbetween sex of participant and group, or sex ofparticipant, group, and condition, F’s (1,148)¼ 0.29–0.32, P’s¼ .573–.591.
TABLE I. Demographic Characteristics of Non-gamers and Violent Video Game Players
Non-gamer Gamer Statistic
N 69 83 —Age 20.78 (3.69) 20.17 (2.54) t(150) ¼ 1.21, P ¼.228Sex (% male) 23% 69% x2(1) ¼ 31.23, P <.001Race (% white) 49% 34% x2(5) ¼ 8.37, P ¼.137Aggression 69.51 (13.66) 76.70 (14.86) t(149) ¼ �3.09, P ¼.003Anxiety 40.72 (7.87) 44.66 (10.07) t(149) ¼ �2.64, P ¼.009Depression 8.67 (7.66) 11.91 (9.18) t(147) ¼ �2.32, P ¼.022Total weekly hours of media 12.77 (9.61) 16.58 (11.69) t(150) ¼ �2.17, P ¼.032Total hours played per day — 2.95 (1.31) —Total hours played per week — 15.92 (9.72) —History of video game playing(years)
— 11.46 (4.69) —
Hours per week of violent videogame playing (self-reported)
— 9.34 (10.52) —
Hours per week offantasy/violent video gameplaying (based on ESRB rating)
— 19.55 (15.86) —
Hours per week ofviolent video gameplaying (based on ESRB rating)
— 14.13 (13.18) —
Note. Mean (SD) provided as appropriate.
2The degrees of freedom are from the Greenhouse–Geisser correctionstatistic.
Aggr. Behav.
6 Diaz et al.
To evaluate differences in the recognition of the fiveemotions specifically, a 2 group (non-gamer, violentvideo gamer)� 2 sex of participant (male, female)� 5emotions (anger, disgust, fear, happy, sad) ANOVAwasconducted. A significant main effect of emotions wasfound when averaging across groups, F (3.50,518.18)¼ 257.01, P<.001. There were significantdifferences between all the emotions with each other,P<.001, except for fear and anger,P¼ 1.000. There wasa main effect of the sex of the participant, F(1,148)¼ 18.12, P<.001, with females being moreaccurate than males. There was no main effect of groupon the recognition of emotions when averaging acrossthe five emotions,F (1,148)¼ 0.01,P¼.954, hp
2< .001.Most importantly, there was a significant interaction
between group and emotions, F (3.51, 518.18)¼ 3.51,P¼.011, hp
2¼ .023. Post hoc tests demonstrated that thegroups differed significantly on the accuracy ofidentification of fearful faces, F (1,148)¼ 5.00,P¼.027, hp
2¼ .033, with violent video game playersbeing more accurate at identifying fearful faces thannon-gamers. Also, there was a statistically significantdifference in recognizing disgusted faces, F (1,148)¼5.03, P¼.026, hp
2¼ .033, with violent video gameplayers being less accurate than non-gamers. The groupsdid not differ significantly on accuracy for angry, happy,or sad faces, P¼.240–.734; see Table II, and Figure 1.No significant interaction was found between group andsex of participant; sex of participant and emotions; andemotions, sex of participant and group, F’s¼ 0.02–2.13,P’s¼ .085–.901.To determine if the intensity of the fearful anddisgusted
faces presented (i.e., the two emotions that showed asignificant difference between groups) further affectedemotion recognition, two 2 group (non-gamer, violentvideo gamer)� 2 sex of participant (male, female)� 3emotion intensity (low, medium, high) ANOVAs wereconducted. For fearful faces, the results indicated anoverall effect of intensity when averaging across groups,F (1.89, 280.26)¼ 1174.66, P<.001. As the level ofintensity of the emotion increased, the level of accuracyalso increased. The group� intensity interaction was notsignificant, F (1.89, 280.26)¼ 1.51, P¼.224, hp
2¼ .010.Furthermore, no significant interaction was foundbetween fear intensity and sex of participant; and fearintensity, sex of participant, and group, F’s (1.89,280.26)¼ 1.35–2.07, P’s¼ .131–.260.For disgusted faces, there was an overall effect of
intensity when averaging across groups, F (1.63,240.92)¼ 182.01, P<.001. As the level of intensity ofthe emotion increased, the level of accuracy alsoincreased. The group� intensity interaction was notsignificant, F (1.63, 240.92)¼ 0.50, P¼.567,hp
2¼ .003. Moreover, no significant interaction wasTABLEII.EmotionRecognitionin
Non
-Gam
ers(n
¼69)an
dViolentVideo
Gam
ePlayers
(n¼83)
Accuracy(%
)Reactiontime(m
s)
Non-gam
erGam
erCohen’sd
Non-gam
erGam
erCohen’sd
M(SD)[95%
CI]
M(SD)[95%
CI]
M(SD)[95%
CI]
M(SD)[95%
CI]
Angry
53.21(12.57)[49.71,56.71]
53.99(13.24)[51.09,56.90]
�0.06
996.72
(354.62)
[890.13,
1103.32]
913.95
(396.71)
[825.52,
1002.39]
0.22
Disgust
40.66(15.33)[36.40,44.92]
34.39(15.23)[30.86,37.92]
0.41
1211.37(574.38)
[1041.04,1381.69]
1258.97(634.10)
[1117.66,1400.28]
�0.08
Fear
49.83(15.32)[46.16,53.50]
55.22(11.09)[52.17,58.26]
�0.40
1616.04(787.47)
[1431.17,1800.92]
1216.32(541.03)
[1062.94,1369.70]
0.59
Happy
72.97(7.24)
[70.96,74.97]
74.52(6.90)
[72.85,76.18]
�0.22
959.44
(400.03)
[855.42,
1063.46]
865.36
(341.82)
[779.06,
951.66]
0.25
Sad
26.04(16.03)[22.15,28.14]
24.92(11.77)[21.69,28.14]
0.08
1690.19(749.00)
[1473.14,1907.24]
1530.58(782.78)
[1350.51,1710.65]
0.21
Aggr. Behav.
Violent Video Games and Emotion Recognition 7
found between disgust intensity and sex of participant;and intensity, sex of participant, and group, F’s (1.63,240.92)¼ 0.19–1.30, P’s¼ .272–.786.
Reaction Time
To evaluate the role of speed on the recognition of thefive emotions, a 2 group (non-gamer, violent videogamer)� 2 sex of participant (male, female)� 5emotions (anger, disgust, fear, happy, sad) ANOVAwas conducted. A significant main effect of emotionswhen averaging across groups was found, F (2.94,435.25)¼ 49.48, P<.001. There were significant differ-ences between all the emotions, except for happy andanger, P¼ 1.000, as well as disgust and fear, P¼.092.Furthermore, no main effect of sex of participant wasfound when averaging across all the emotions, F(1,148)¼ 1.57, P¼.212. In addition, the effect of groupon the reaction time of recognition of emotions whenaveraging across the five emotions showed a trend, F(1,148)¼ 3.32, P¼.070, with violent gamers havingfaster responses than non-gamers.Most importantly, the interaction between group and
emotions was significant, F (2.94, 435.25)¼ 3.77,P¼.011, hp
2¼ .025. Pair-wise post hoc analysesindicated that the groups differed significantly on thereaction time for fearful faces, F (1,148)¼ 10.81,P¼.001, hp
2¼ .068, with violent video game playersbeing significantly faster at identifying fearful faces thannon-gamers. The groups did not differ significantly onthe reaction time for recognition of angry, disgust,happy, or sad faces, P¼.171–.671; see Table II, andFigure 2. There were no significant interactions betweengroup and sex of participant; sex of participant and
emotions; and sex of participant, group, emotions,F’s¼ 0.56–2.33, P’s¼ .075–.455.
Association Between Emotion Recognitionand Aggression, Mood, and MediaConsumption
We conducted three 2 group (non-gamer, violent videogamer)� 2 sex of participant (male, female) ANOVAson aggression, anxiety, and depression to determine theassociation between violent video game playing on thesevariables. The results showed that compared to non-gamers, violent video game players have higher scoresof aggression (F (1,147)¼ 6.08; P¼.015; hp
2¼ .040),depression (F (1,145)¼ 8.42; P¼.004; hp
2¼ .055), andtrait anxiety (F (1,147)¼ 11.39; P¼.001; hp
2¼ .072).The effect of sex of participant was significant only fortrait anxiety (F (1,147)¼ 5.42; P¼.021; hp
2¼ .036).Females seemed to have higher scores of trait anxietycompared to males. The group� sex of participantinteraction was not significant in any of the ANOVAs (F(1,145/47)¼0.08–0.49; P¼.486–.784). Covarying for media con-sumption did not change the results.Trait anxiety has been associated with abnormal
recognition of fearful faces (Surcinelli et al., 2006).Therefore, we conducted two 2 group (non-gamer,violent video gamer)� 2 sex of participant (male,female) ANOVAs for accuracy and reaction time offearful faces including trait anxiety as a covariate. Theanxiety covariate was non-significant in both contrasts.(F (1,146)¼ 1.04; P¼.309; h2p¼ .007 for accuracy; andF (1,146)¼ 0.26; P¼.610; h2p¼ .002 for reaction time).The results for group on accuracy (F (1,146)¼ 6.65;
Fig. 1. Facial emotion recognition accuracy in non-gamers (n¼ 69)and violent video game players (n¼ 83). �Statistically significantdifference between groups (P< .05). Errors bars represent onestandard error of the mean.
Fig. 2. Facial emotion recognition reaction times in non-gamers(n¼ 69) and violent video game players (n¼ 83). �Statisticallysignificant difference between groups (P< .05). Errors bars representone standard error of the mean.
to the results obtained without trait anxiety as acovariate. Furthermore, no significant interactionswere found between sex of participant and group (F’s(1,146)¼ 2.69–2.79; P’s¼ .097–.103.
DISCUSSION
The aim of the current study was to determine theeffects of violent video game playing on the accuracyand reaction time of specific emotions while controllingfor general cognitive processing through the use of agender recognition condition. To date, only three studieshave investigated the effects of video game playing onthe recognition of angry and happy faces (Bailey &West, 2013; Kirsh et al., 2006; Kirsh & Mounts, 2007);therefore, there is a need to evaluate the effects of violentvideo game playing on the recognition of broaderemotions. Moreover, the previous literature has notinvestigated the effects associated with chronic violentvideo game playing on the identification of facialemotions. In the present study, violent video gameplayers reported playing for approximately 3 hours aday, and had been playing for an average of 11 years.Given the prevalence and popularity of violent videogames, chronicling the effects of chronic violent videogame playing on facial emotion recognition, oneimportant component of social cognition, is critical.Thefirst objective of the present studywas to determine
whether facial emotion recognition in general wasdifferent from the experimental control (i.e., genderrecognition) in chronic high-frequency violent videogame players (i.e., two or more hours of violent videogame playing a day) versus non-gamers. Contrary to ourhypotheses, our results demonstrated that violent videogame players and non-gamers did not differ significantlyin their ability to differentiate emotions overall. However,as expected, high-frequency violent video game playersand non-gamers did not differ in gender recognition—inother words, they did not differ in their ability to identifythe invariant aspects of the faces (i.e., gender recog-nition). This indicates that differences found in this studyrelating to the recognition of specific facial emotionexpressions (discussed below) are likely to be due toimpairments in the recognition of the changing aspects ofthe face to depict the specific emotion, and not to othercognitive abilities related to the recognition of faces(Bediou et al., 2005; Haxby et al., 2000).Second, we explored the effects of chronic violent
video game playing on the recognition of five basicemotions: anger, disgust, fear, happiness, and sadness.We found that participants who played two or morehours of violent video games a day were more accurate
and faster at recognizing fearful faces than participantswho did not play video games. Fear (Jansz, 2005; Weberet al., 2006) and anxiety (Anderson & Ford, 1986;Ravaja et al., 2008) produced by violent video game playcould explain these effects. Weber et al. (2006) foundthat during game playing, in situations where the playeris at risk, violent video game players showed a pattern ofbrain activation similar to that found in real fearfulsituations. Even though players experience fear in thegame, they do not avoid or ignore it; instead, they seemto appraise the situation, and to learn to be alert toquickly identify the virtual dangers they are exposed toin order to reach their in-game goals. According to theGLM (Buckley & Anderson, 2006), through repeatedexposure to violent video game playing, this behaviorcould become automatic and could occur unconsciously.Since facial expressions of fear signal to the viewer that apossible danger/threat is imminent (Adolphs, 2008), thisnew learned behavior may explain why violent videogame players are faster and more accurate at identifyingfearful faces. Furthermore, research has demonstratedthat individuals high in trait anxiety tend to identify fearmore accurately than those low in trait anxiety(Surcinelli et al., 2006). Hurting virtual opponentsduring violent video game play can cause high levels ofanxiety (Anderson & Ford, 1986; Ravaja et al., 2008).Repeated exposure to violent video game play mightincrease gamers’ anxiety over time (Buckley &Anderson, 2006) while making them more accurate atidentifying fear.Violent video game players were less accurate than
non-gamers at identifying disgusted faces. This mightrelate to previous findings that demonstrated: (i)desensitization of high frequency gamers to real lifeviolence (Bartholow et al., 2006; Carnagey et al., 2007;Engelhardt, Bartholow, Kerr, & Bushman, 2011); and(ii) habituation of violent game players to unpleasantstimuli (Montag et al., 2012). First, research suggeststhat repeated exposure to the execution of violence ingames can cause players to create new cognitiveschemas that help them desensitize to violence, andact more aggressively to more effectively completeviolent in-game goals (Anderson & Bushman, 2002;Bartholow et al., 2006; Buckley & Anderson, 2006;Carnagey et al., 2007). Lower sensitivity to disgust(moral, sexual, or pathogenic) appears to be associatedwith higher trait aggressiveness and aggressive behavior(Pond et al., 2012). Players could also conceivably createnew cognitive schemas to attenuate their experience ofdisgust to help them more effectively complete violentin-game goals (Buckley & Anderson, 2006). Secondly,violent video game players are constantly exposed tounpleasant scenes (e.g., blood and gore) in games(Frome, 2007; Jansz, 2005; Montag et al., 2012). Even
though disgust is considered an avoidance emotion thatmakes individuals avoid certain types of behavior orsituations (Olatunji, Sawchuck, Lohr, & de Jong, 2004),violent video game players do not remove themselvesfrom the game, but rather they seem to supress thefeeling of disgust to continue playing. The rehearsal ofthis behavior could produce, in the long-term, newcognitive representations of the emotional response tounpleasant scenes (Buckley & Anderson, 2006), leadingto desensitization and further habituation to those scenes(Bartholow et al., 2006; Buckley & Anderson, 2006;Carnagey et al., 2007). The findings by Montag et al.(2012) that violent video game players had diminishedneural response in the lateral prefrontal cortex whenexposed to unpleasant stimuli support the possiblehabituation to disgust on violent video game players.The lateral prefrontal cortex is involved in theintegration of unpleasant emotion and cognition (Gray,Braver, & Raichle, 2002), as well as the evaluation andlabeling of emotions (Lieberman et al., 2007). Thereduced activation of the lateral prefrontal cortex mightbe indirectly affecting the evaluation and labeling ofdisgusted facial expressions, which are believed to beproduced as a response to unpleasant stimuli, amongothers (Ekman, 1977; Ekman & Friesen, 2003).Alternatively, it is possible that other factors not
considered in this study could be contributing to theseeffects. It could very well be that these emotionrecognition deficits could be pre-existing and lead theseindividuals to prefer certain activities, including playingviolent video games. Further research needs to beconducted to clarify the effects of chronic violent videogame playing on the processing of facial emotionsthrough the use of different experimental designs.Additionally, many of the models we draw on tounderstand our results are theoretical and require moreempirical testing.In this study, we did not replicate previous findings on
the recognition of angry and happy faces. We found thatviolent video game players and non-gamers wereequally accurate and quick at identifying angry andhappy facial expressions. Our results complement thoseof Bailey and West (2013) who found no difference inthe recognition of angry faces between violent videogame players and non-gamers; however, the results ofthis study contradict those of Kirsh et al. (2006, 2007).Both of those studies found that violent media exposureand short term exposure to violent video games wereassociated with greater recognition of angry faces. Inaddition, all three studies found violent video gameplaying was associated with reduced identification ofhappy faces, which was also not observed in our study(Bailey & West, 2013; Kirsh et al., 2006; Kirsh &Mounts, 2007). One possible explanation for the
discrepancy in results between the two studies mayrelate to the nature of the task used. In our study, severalfaces at different levels of intensities were presented, oneat the time, and participants were asked to label the facialemotion, while in other studies, a change detection taskwas used. Furthermore, our study investigated chronicvideo game playing, whereas the Kirsh et al. (2006,2007) studies investigated short-term exposure toviolent video game playing along with the long-termexposure to violent media including video games.This study also explored the effect of intensity of an
emotion on recognition. We found that the intensity ofthe fear or disgust emotion depicted did not further affectfacial recognition in violent video game players. Ourstudy also explored the influence of group differences inlevels of depression, anxiety, and aggression on therecognition of facial expressions. We found that violentvideo game players reported higher levels of aggression,anxiety, and depression than non-gamers. This differ-ence persisted after controlling for other mediaconsumption and is in line with previous research,which indicates that high frequency video game playingis associated with higher levels of depression andanxiety (Mentzoni et al., 2011; Starcevic et al., 2011).We also found no association between trait anxiety andthe accuracy and the reaction time of recognition offearful faces, suggesting that the effects found could beassociated with unique characteristics of being anindividual who plays violent video games.One interesting observation was that self-reported
estimates of the exposure to video game violence werelower than objective estimates calculated using ESRBviolent content ratings. This underestimation by playerscould be occurring due to a subjective desensitization toviolence after chronic exposure (Ivarsson et al., 2013),difficulty categorizing simulated video game violence as“real” violence (Carnagey & Anderson, 2004), or adistorted sense of time (Rau, Peng, & Yang, 2006).However, if insight regarding exposure to violent contentis reduced for players, monitoring all types of video gameplaying may be necessary, especially for children.This study has several limitations. First, our sample
consisted of university undergraduate psychologystudents; therefore, these findings are not generalizableto the entire age range of violent video game players.Second, although our sample size was sufficient to detectdifferences between violent video game players and non-gamers, it did not allow the examination of the effects ofdifferent types of violent video games (i.e., fantasy/cartoon violence and realistic violence) on the recog-nition of facial expressions. Theoretically, the content ofthe game influences the knowledge structures that aplayer could develop (Buckley & Anderson, 2006;Maier & Gentile, 2012), which in turn could influence
player’s behavior and emotion processing (Buckley &Anderson, 2006; Maier & Gentile, 2012). These differ-ent schemasmight potentially create differential patternsof facial emotion recognition in players based on thetype of violent video game they play. Thus, furtherresearch should explore these possible differences.Third, we did not include a group of non-violent videogame players as a comparison group, as we found in oursample using the objective ESRB criteria that very fewgamers played only non-violent games. Therefore, wecannot determine whether the effects are relatedexclusively to long-term violent game play, or simplyto playing video games.Despites these limitations, this study has several
strengths. First, our study included a gender recognitioncontrol condition to assess for general visual or cognitivedeficits that can also be found in video game players.Second, we studied the effects of chronic violent videogame playing on the recognition of facial emotions, anarea that has not been studied before. Third, we includeda task that allows the measurement of the accuracy andreaction time of five facial emotions, thereby, assessingemotions not investigated before. Last, we were able toassess violence in the video game playing using anestablished and objective measure.
CONCLUSIONS
Our findings suggest that chronic exposure to violentvideo game playing might affect the recognition offearful and disgusted faces, but not the recognition ofangry, happy, or sad faces. More research needs to beundertaken to understand the effects of violent videogame playing on emotion recognition, and to determinethe underlying contributing factors that lead to anassociation between the two.
ACKNOWLEDGMENTS
The authors thank Dr. Benoit Bediou for providing theimages for the facial recognition task. We also thankDr. Tak Fung for his statistical consultation, Dr. MelissaBoyce for facilitating recruitment, and Dr. ChristopherSears for his feedback on an earlier version of the study.Lastly, we thank Jennifer Prentice, Andrea Moir, andMelissa King-Hope for their help with data collectionand analyses. This study was funded by a NaturalSciences and Engineering Research Council grant ofCanada to Dr. Goghari.
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