Atrocity Propaganda in Military-Themed First-Person-Shooter Video Games
9th of July 2020 Word count: 8209
Author: Sean Steven Magee 12490067
Supervisor: Tom Dobber
Master’s Thesis Graduate School of Communication Master’s programme Communication Science University of Amsterdam
Abstract The present study examines whether military-themed First Person Shooter (FPS) video games can be used as a tool of atrocity propaganda in eliciting certain emotional states and perceptions of the in- and outgroups that their narratives project. Call of Duty: Modern Warfare (2019) was the key subject of this investigation, and two of its campaign levels were employed to gauge the effects of an atrocity-driven and non-atrocity-driven narrative on participants’ emotional states, and their humanization, animalistic dehumanization and mechanistic dehumanization of the narratives’ projected Arab ingroup and Russian outgroup. The results showed that exposure to the atrocity- driven narrative had a significant and substantial effect on increasing participants’ levels of self- reported Fear, Disgust and Anger, in addition to other negative emotions, while also significantly lowering positive emotions. However the results did not show atrocity-driven FPS narratives to have any significant impact on manipulating individuals’ humanization, animalization and mechanization of the groups portrayed. Instead, the analysis suggests that simple visual exposure to FPS gameplay with a certain ingroup projected as the player or the player’s allies has a significant and moderate effect on humanizing the relevant ethnicity for viewers.
Introduction The capacity of video games to trigger aggressive behavior and cognition in both the short- and long-term has been evidenced in numerous studies (Anderson & Dill, 2000; Bushman & Anderson, 2002; Hollingdale, Greitemeyer, & McCormick, 2014); particularly when an effort is made to convey violence as realistically as possible to players (Jeong, Biocca, & Bohil, 2012). Although violent interaction through video gameplay has received ample attention over the years, the effects of violent video game story-narratives remain less well understood; a troublesome fact when one considers that violence-justifying narratives traditionally also tend to increase the probability of aggressive behavior (Geen & Stonner, 1974; Paik & Comstock, 1994). The question of game narrative is particularly relevant in light of the increasingly popular and realism-oriented military themed-FPS (First Person Shooter) genre of video games, which as its name suggests, often assumes narratives that mirror present-day armed conflicts. This novel entertainment medium also offers an increasingly unambiguous space in which we are unable to escape our role as victim or perpetrator; thereby assigning such FPS narratives an unprecedented level of power in communicating messages of deep political significance. As with violence mediated through more traditional channels like TV and movies, justified violence has been found to drive about 77% of video game narratives (Smith, Lachlan, & Tamborini , 2003); accounting for the frequency with which players adopt roles of moral agency in defending universal values in their struggle against enemies (Schneider, Lang, Shin, & Bradley, 2004).
There has consequently also been ample debate over how FPS narratives might be used as a tool of one-sided political communication; albeit almost entirely in a theoretical context and supported only through anecdotal evidence (Der Derian, 2001; Halter, 2002; Leonard, 2004; Stahl, 2006; Gagnon, 2010; Hitchens, Patrickson, & Young, 2013). Central to this discussion however, is the untested assumption that this game genre offers a new medium in which players’ attitudes towards certain real-life outgroups might be affected by explicitly depicting active belligerents of ongoing conflicts in a certain way. Call of Duty: Modern Warfare; arguably the most successful and popular military-themed FPS game in recent times, features an atrocity-laden narrative that mirrors the ongoing war in Syria with Russian enemies and Arab allies. Having sold 4.75 million digital units during the month of its launch in 2019 (Valentine, 2019), and having passed $1 Billion in global sales by December of the same year, Call of Duty: Modern Warfare has become the most played edition of its generation (Bhat, 2019); reportedly receiving over 3 hours of gameplay from the average player per week (Gaston, 2011). The game also serves as a most recent example of how some media outlets (Call of Duty: Modern Warfare faces Russian backlash, 2019) have used the term ‘propaganda’ to describe yet another supposed soft power mechanism by which the USA and Russia contest each other culturally. This study therefore seeks to examine whether the game’s campaign can actually be used as a tool of atrocity propaganda in effectively influencing players to achieve desired emotional states and perceptions of the enemy, and poses the following research question:
RQ: How do atrocity stimuli attributed to outgroups in First Person Shooter (FPS) video games affect players’ emotional states, and their perceived humanization or dehumanization of the in- and outgroups projected by these FPS narratives?
Theoretical Framework
Propaganda As with propaganda more generally, atrocity propaganda’s presumed effects have been the subject of considerable speculation but scant empirical research. This is perhaps due in part to the fact that no robust and widely recognized definition or theoretical framework for atrocity propaganda has ever existed (Morrow, 2018), and that the function of ‘propaganda’ has also been reformulated numerous times and by numerous scholars since its inception (Jowett & O′Donnell, 2012). Lasswell wrote that the overarching cultural approach of propaganda involved the “presentation of an object… in such a manner that certain cultural attitudes will be organized toward it” (1927, p. 629 - 630), whereas Ponsonby believed the more specific aim of atrocity propaganda was the “stimulation of indignation, horror, and hatred” against an enemy, in a manner “assiduously and continuously pumped into the public mind” (1929, p. 1). Military-themed FPS video games would therefore offer an unexplored but highly engaging avenue for the stimulation of such attitudes towards groups they depict as adversaries.
More lacking than a common formulation of propaganda however, is a concise definition of what constitutes atrocity stimuli; in light of the quite fractured theoretical approach it has received in social science research. Bromley, Shupe and Ventim concluded that false stories of moral indignation or atrocity tales primarily functioned by evoking “outrage,” authenticating preconceived notions of “evil,” and thereby bolstered a form of “social control” over communities (1979, p. 1). This is a similar observation to Bar-Tal’s later claim, that by placing adversarial groups in “extreme negative social categories which are excluded from human groups… considered as acting within the limits of acceptable norms and/or values,” through a process involving “intense negative emotions that derive from… extremely negative contents,” nation states had found an effective soft power instrument to delegitimize their enemies (1989, p. 171-172). Effective atrocity propaganda would therefore seem to trigger some emotional heuristic through morally or normatively outrageous stimuli, reflective of the “‘inhuman’ character of a perceived enemy’s conduct” (Morrow, 2018, p. 51); consequently inducing a broader and negative attitudinal change towards said outgroup. To the best of our knowledge however, the heuristics and attitudinal effects ascribed to atrocity stimuli have remained empirically untested; underscoring a substantial gap in the academic literature this study hopes to fill.
FPS narratives as a mediated experience The difference in how narratives are mediated via video games versus TV and movie mediums have important implications for how messages are received and integrated. As Thompson has pointed out, the ‘hypodermic needle’ conceptualization of media affects; highly popular in the traditional propaganda framework, doesn’t accurately capture the “processes of encoding and decoding” that occur between a game narrative’s message and the players who actively interact with it (2008, p.20). Tilo Hartmann argues that video game narratives operate through one of two “mutually exclusive modes of perception;” the experiential over the rational processing system (Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 110). If we assume a rational processing system, the player is completely aware of the “mediated origin of the representations they encounter” while playing the game (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 110), and is therefore always aware that in terms of story-narrative and interaction, the game ‘is not real’ (p. 110; Klimmt, Schmid, Nosper, Hartmann, & Vorderer, 2006). In this sense, the player would therefore feel neither any emotional connection to virtually-mediated humanoid representations, nor any attitudes about his or her interaction with them. Hartmann however argues that because narratives “feel apparently real to the players’ senses” via the interactive experience video game mediation offers, players may nonetheless be under “intense illusions of violence,” in which their actions seem “morally significant,” and therefore perceive games via an experiential rather than rational processing system (Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 109).
Key to this experiential process is a sense of ‘presence,’ by which the player feels that his or her experience is actually one of non-mediation (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012; Tamborini & Skalski, 2006). Many scholars argue that a sense of ‘whole’ or at least ‘partial’ self- presence occurs (Lee, 2004), in which players identify with their virtual selves during gameplay; particularly when an experience is mediated in the “first person” view of FPS games (Tamborini & Bowman, 2010, p. 89). Schneider and colleagues (2004) found that this type of involvement with the game-self was also far greater in games driven by a story than those that weren’t. We should therefore also note that encoding messages through story-narratives is yet another hallmark of experiential processing (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 111).
There is also ample empirical evidence to back up the claim that players process FPS narratives in an experiential way. Hartmann, Toz and Brandon (Just a Game? Unjustified Virtual Violence Produces Guilt in Empathetic Players, 2010) found that players actually developed negative emotions for unjustifiably shooting people in FPS games, and a number of other studies have also indicated that players develop guilt for shooting innocent FPS projections (Klimmt et al., 2006; Lin, 2011). Additionally, interviews with gamers have shown that certain moral taboos also apply when navigating video game narratives (Goodings, Young, & Whitty, 2011). According to Hartmann, him and his colleagues also found that some players felt irritated and guilty over the fact the Call of Duty 4: Modern Warfare narrative forced them to execute sleeping soldiers (Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012; Soto, Hartmann, & Prins, 2010). If we were to assume a rational processing system at work here, it is unlikely that players would intuit moral objections to actions that aren’t real. In this sense, video games actually offer a far greater impetus to present players with a morally justifiable narrative than viewers of TV or movie violence, as the latter can more easily disengage from visual narratives through their limited role as ‘onlookers,’ and the former actually perform as active agents that move the narrative forward (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 121; Lee, 2004; Tamborini & Skalski, 2006).
The heuristic nature of this experiential mode of processing however also means that it lends itself as the one best suited for the reception of propaganda. Assuming an expressive mode of perception also means assuming moral judgements based on intuition rather than logic or reflection (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012; Haidt, 2001), which would serve the purpose of attributing atrocities to an outgroup very well. While the long-term stability of such a change in attitude is also undermined by processing messages experientially rather than rationally (Chaiken, 1980), the allusion of game narratives to real-world happenings may also interact with such experiential processes in a manner that isn’t entirely clear cut, and might at minimum have a significant effect in the short-term.
Dehumanization Bar-Tal was one of the first scholars to ascribe the term dehumanization to the delegitimizing process of “categorizing a group as inhuman” (1989, p. 173). Since then, dehumanization has been studied as it relates to instances of genocide (Chalk & Jonassohn, 1990; Kelman, 1973), moral exclusion (Opotow, 1990), evil (Morera, Quiles, Correa, Delgado, & Leyens, 2018), and approval for various foreign policy practices (Viki, Fullerton, Raggett, Tait, & Wiltshire, 2012; Jackson & Gaertner, 2010; Maoz & McCauley 2008). Dehumanization has also been shown to evoke a process in which guilt for aggressive behavior is alleviated (Bandura, 2002), and intergroup aggression is made more likely (Struch & Schwartz, 1989); underscoring its practicality as a means of propaganda.
More recently however, the framework for studying how outgroups are explicitly made to seem less human than ingroups has given way to the increasingly popular infrahumanization approach (Leyens, et al., 2000), which emphasizes the implicit and “more subtle forms of bias” inherent to how we see outgroups as less human than ingroups in everyday life (Vaes, Paladino, Luigi, Leyens, & Giovanazzi, 2003, p. 1016). For this reason, we turn to Haslam’s integrated dual model of dehumanization approach (Dehumanization: An Integrative Review, 2006), which conceives of infrahumanization as just one expression of the broader dehumanization framework (Haslam & Loughnan, Dehumanization and Infrahumanization, 2014). By this conceptualization, dehumanization may refer to how outgroups are made to seem less than human in one of two explicit manners; mechanistically or animalistically1. In short, to mechanize someone, is to see them more as machine than human; denying them a ‘uniquely human nature’ in terms of for instance “depth,” “emotional responsiveness and agency,” or “individuality” (Haslam, Dehumanization: An Integrative Review, 2006, p. 257), whereas animalizing someone implies seeing them as more animal than human; thereby denying them a ‘human uniqueness’ in terms of “rationality,” “moral sensibility” and “restraint” (p. 257), to name just a few. Thus, animalization lends itself as the more convenient means by which to dehumanize an enemy, as this process is more easily associated with inducing contempt and disgust for an outgroup (Haslam, 2006, p. 254, 258, 262), and has also been shown to operate via a process of fear-based mediation (p. 261). It is therefore no coincidence that the invocation of these emotions is associated with Bar-Tal’s (1989) delegitimization approach, which I employ for the current theoretical framework of propaganda. Since this study gauges participant’s combined levels of Anger and Disgust to estimate the broader sentiment of Contempt (Plutchik, 1962), I arrive at the following hypothesis:
H1: Participants in the two FPS game conditions will measure at higher levels of Fear, Disgust and Anger than participants in the non-FPS game condition, however participants in the ‘FPS game with an atrocity-driven narrative’ condition will measure at higher levels of Fear, Disgust and Anger than participants in any other condition.
Mechanization on the other hand lends itself as a more accurate description of what players view Non-Player Enemies (NPCs) as in narratives only weakly driven by a sense of moral agency. Machin and Suleiman (2006) observe how enemies in these games often become uniform to the player; a process that Young, Patrickson, and Hitchens (2013, p. 10) argue is best captured by Van
1 The more succinct terms ‘mechanize’ and ‘animalize’ will be used to refer to mechanistic dehumanization and animalistic dehumanization, respectively. Leeuwen’s term “collectivization” (1996, p. 49). The framework I employ however, describes this process as a mechanization of the outgroup projected through enemies; robbing them of the qualities Haslam (2006) associates with ‘human nature,’ and consequently portraying them as virtual robots to the players who interact with them. With this in mind, I arrive at the following hypothesis:
H2: Participants in the two FPS game conditions will humanize the projected outgroup less so than participants in the non-FPS game condition, however participants in the atrocity-driven narrative condition will measure highest in Animalization of the projected outgroup compared to those in all other conditions, and participants exposed to the non-atrocity-driven narrative will measure highest in Mechanization of the projected outgroup compared to those in all other conditions.
We should nonetheless note that this dual model of dehumanization also isn’t entirely clear cut as it relates to propaganda. Haslam argues that his model parts with that of Bar-Tal’s (1989) in making no distinction between dehumanization by way of sub- or superhuman projections (Dehumanization: An Integrative Review , 2006, p. 259). À la Bar-Tal’s (1989) delegitimization through dehumanization approach, it is advantageous not just to animalize but also demonize the enemy. This means attributing the superhuman powers of “monsters and satans” to outgroups (1989, p. 173); thereby better serving the purpose of evoking not just contempt, but also fear for the enemy. Again, given that no coherent framework for understanding atrocity propaganda exists, we may in this case need to conceive of Haslam’s dual model of dehumanization as the more updated and methodologically-oriented framework for outgroup dehumanization, and Bar-Tal’s more explicit delegitimization approach as the mechanism that motivates and instructs the process itself. This is because the delegitimization approach best captures the more traditional and politically-oriented framework of propaganda. It is also because Bar-Tal’s ‘demonization’ view (Haslam, Dehumanization: An Integrative Review, 2006) is precisely the one most commonly associated with the type of interethnic struggle depicted in Call of Duty: Modern Warfare’s campaign narrative, and the one that has manifested itself in how enemies were often depicted in atrocity propaganda historically.
While we might therefore conclude that the primary aim of atrocity propaganda is to animalize the projected outgroup, a secondary purpose not addressed by Bar-Tal might also be to humanize the projected ingroup; since atrocities by definition necessitate both a perpetrator and victim. This is well depicted in works like “Destroy this Mad Brute: Enlist” (Hopps, 1917), where the evocation of fear and contempt for the animalized German is augmented by humanizing and evoking sympathy for the presumably Belgian victim (see Appendix 1.a.). The particular power outlined in previous research by which players self-identify with the protagonists of their video game narratives suggests that putting someone in the victim role of such an atrocity-driven FPS narrative would therefore also force the player to self-identify with the protagonists’ ingroup; possibly leading to a greater humanization of said ingroup post-exposure. This leads me to the following hypothesis:
H3: Participants in the FPS game conditions will humanize the projected ingroup more so than participants in the non-FPS game condition, however participants in the ‘FPS game with an atrocity- driven narrative’ condition will measure highest in Humanization of the projected ingroup and lowest in Animalization as well as Mechanization of the projected ingroup compared to participants in the other two conditions.
Method
Participants The Qualtrics survey-experiment was disseminated via Amazon Mechanical Turk between May 14th and June 1st of 2020. The age parameters of 18 to 30 were set as a rough estimation of the younger demographic associated with violent video games2 (Blocker, Wright, & Boot, 2014), with participants receiving $1.50 each for completing the survey. The recruitment text led participants to believe they were participating in a study relating to attention-span rather than atrocity propaganda, as not to prime them to the sensitive nature of what was being measured. Responses that failed manipulation checks were removed during data collection and were not counted as paid and successful completions. Of an initial sample of 126 respondents, two were removed for falling outside the targeted age and gender demographic, with a further nine being removed for giving incongruent or random responses on the emotional measures3. This left a final sample of 115 responses adequate for testing means across all three conditions with ANOVAs. Nationality was limited to U.S. participants in order for the ingroup to remain constant, and for the implicit dehumanization task to function. All participants fell between the ages of 18 and 28 (M = 23.89, SD = 1.56), with 51 being male (44%) and 64 being female (56%).
2 While the age criteria entered into Amazon Mechanical Turk specified 18-25 as the requisite age range, individuals over 25 but under 30 did take part as well, and their responses were retained. 3 Cases 126 and 103 were removed for falling outside of the target age and gender demographic respectively. Cases 60 and 52 were removed for entering the same digit across all emotion responses. Cases 41, 62, 69, 86, 98, 110 and 111 were also removed for displaying patterns of obvious incongruence across their emotional responses (see supporting sytanx script for details).
Measures
Emotional Experience
Participants’ self-reported levels of Fear, Disgust, and Anger were measured as dependent variables, in addition to Sadness, Happiness, Relaxation, Anxiety, and Desire as control variables (see Appendix 2.s. for items and scale-coding). To measure these, the framework of the Discrete Emotions Questionnaire was employed (Harmon-Jones, Harmon-Jones, Aleman, & Bastian, 2016), as this was both an up-to-date and reliable emotional measurement scale (Harmon-Jones, Harmon- Jones, Aleman, & Bastian, 2016). This questionnaire used four synonyms per emotion to establish consistency in one’s self-reported emotions on a scale of how much it applied to the respondents’ experience from 0; ‘not at all,’ to 6; ‘an extreme amount.’ For instance, “fear,” “terror,” “panic” and “scared” were all found to be reliable measures of the emotion Fear (Harmon-Jones, Harmon-Jones, Aleman, & Bastian, 2016). All items were also presented in a randomized order. Since the DEQ didn’t measure Contempt, the combination of Anger and Disgust were used as a broader estimate for the emotion of Contempt (Plutchik, 1962). All DEQ measures for Fear (α = 0.96), Disgust (α = 0.93), Anger (α = 0.95), Sadness (α = 0.81), Happiness (α = 0.92), Relaxation (α = 0.93), Anxiety (α = 0.92), and Desire (α = 0.87) proved reliable scales.
Humanization, Mechanization and Animalization
Although several measures have already been developed to measure dehumanization (Haslam & Loughnan, Animals and Androids: Implicit Associations Between Social Categories and Nonhumans, 2007; Čehajić, Brown, and González, 2009; Haslam & Bastian, Excluded from humanity: The dehumanizing effects of social ostracism, 2010; Greitemeyer & McLatchie, 2011; Rudman & Mescher, 2012; Morera, Quiles, Correa, Delgado, & Leyens, 2018), there were several reasons I decided to retool Martínez and colleagues’ (2012) measure for this study4. Firstly, none of the implicit association measures used previously have been employed outside a lab setting, and were therefore no more suitable for an online Qualtrics survey than Martínez and colleagues’ (2012).
4Initially, a paper and pencil procedure developed and tested by Martínez and his colleagues (2012) was the intended procedure for measuring dehumanization of the projected outgroup and projected ingroup, however when a lab study was no longer possible due to the COVID-19 pandemic, it was also no longer possible to fully translate this paper and pencil task into an online Qualtrics survey format. Secondly, given the new online setting for the experiment, there was a greater incentive for retaining respondents’ attention and keeping the measure simple. Thirdly, in order to make the measurement more suitable for later ANOVAs, the inability to translate the initial methodology of Martínez and his colleagues’ (2012) paper and pencil task to Qualtrics also served as an opportunity to revise their essentially nominal measure into a continuous one.
The initial measure (Martínez, Rodríguez-Bailón, & Moya, 2012) posits ten names associated with a certain in- or outgroup, in addition to 21 words to the participant; seven that are essentially characteristic of animals, seven of humans, and seven of machines. Based off the aggregation of word-types chosen by participants for each group, differences in how much participants animalize, mechanize and humanize certain surnames and their associated in- or outgroups can therefore be established. To retool the measure, five of Martinez and colleagues’ (2012) seven words were retained for the animal- (“wild,” “irrational,” “breed,” “pet,” and “animal”), machine- (“mechanical,” “calculating,” “technological,” “device,” and “tool”) and human- (“single,” “rational,” “individual,” “people,” and “passive”) related measurement scales. Accordingly, five Russian (“Petrov,” “Sokolov,” “Vasiliev,” “Ivanov,” and “Mikhailov”), five Arab (“Ahmad,” “Basara,” “Nassar,” “Rahal,” and “Mohamed”) and five American (“Johnson,” “Williams,” “Smith,” “Thompson,” and “Miller”) surnames were posed to participants5. These were attained via a google search of the most common surnames for each group, and participants were presented with a randomly selected animal- related word, human-related word and machine-related word for each (see Appendix 2.r.). Participants had to select how much each of the three words spoke to them when reading each surname, on a scale of 0; ‘not at all,’ to 7; ‘a lot.’ In this manner the humanizing, animalizing and mechanizing measures could run concurrently for each surname. All words and surname items were also displayed randomly for participants. For the purposes of this study, the measures relating to the projected Russian outgroup and Arab ingroup were computed by averaging humanizing, mechanizing or animalizing responses to the five Russian or Arab name items6 (see Appendix 2.r. for coding). While the variables Animalization of the projected outgroup (α = 0.72), Mechanization of the projected outgroup (α = 0.67), Mechanization of the projected ingroup (α = 0.61), and Animalization of the projected ingroup (α = 0.77) proved reliable measurement scales, the variables Humanization of the projected outgroup (α = 0.52), and Humanization of the projected ingroup (α =
5 Words were eliminated on the basis of alliteration or similarity; (e.g. “machine” and “mechanical”) or because they too closely hinted at the characters depicted in the video game (e.g. “inhabitant”). Surnames were selected on the basis of internal phonetic diversity, (e.g. “Ahmad and Basara” rather than “Ahmad and Ali”). 6 While the American surname items can also be computed into scales to measure mechanization, animalization and humanization of the real-life ingroup for future studies, this did not fall under the scope of the current investigation. 0.54) did not. The unreliability of these latter two scales should therefore be taken into account when considering findings.
Control variables and manipulation checks
Gender was reported with either ‘male,’ ‘female,’ or ‘other’ (see Appendix 2.b. for items and Appendix 3.a. for randomization check), while Age was measured with an open-answer question during the closing questionnaire (see Appendix 2.x. for items and Appendix 3.b. for randomization check). Proficiency in the Arabic or Russian language, and Arabic or Russian familial origin was measured with ‘yes’ and ‘no’ (see Appendix 2.w. for items and Appendix 3.c. to 3.d. for randomization checks). Prior exposure to the game narrative was measured on a scale of 0; ‘never’ to 7; ‘many times’ (see Appendix 2.t. for items and Appendix 3.e. for randomization check). The survey controlled for the Type of device it was completed on, with the options ‘computer,’ ‘smart phone,’ ‘tablet,’ and ‘other’ (see Appendix 2.x. for items and Appendix 3.f. for randomization checks). Participants were also asked what ethnicity they believed the enemies and allies in the game to be with an open question, in order to gauge whether they had registered the narratives’ projected in- (allies) and outgroups (enemies). Open answers for this question were recoded accordingly into the nominal level variable Recognition of the projected in- and outgroups (see Appendix 2.v. for items or coding, and Appendix 3.g. for randomization checks). Recognition of an atrocity having occurred was measured by asking participants whether they had perceived the actions of their enemies and allies as “justified” under the Geneva Convention, and as “extremely brutal and cruel,” on a seven-point Likert scale from 0; ‘completely disagree’, to 6 ‘completely agree’. While it should be noted the scale computed from averaging these items (see Appendix 2.u. for reverse coding), did not meet the standards of reliability (α = 0.49), a one-way ANOVA did show that overall Recognition of an atrocity having occurred was significantly higher in the atrocity-driven FPS narrative (M = 4.55, SD = 1.07) than the non-atrocity-driven FPS narrative condition (M = 3.09, SD = 0.59), by almost one and a half points, F(1, 80) = 56.05, p < .001, and that the manipulation of narratives for participants could therefore be considered successful.
Research Design
Game Narrative
The independent variable Game Narrative (see Table 1 for visualization of Game Narrative research design and Appendix 2.z. for summarization of broader questionnaire design) was manipulated by allocating participants to one of three possible conditions; the ‘non-FPS narrative’ (control) condition (see Appendix 2.m. & 2.o.), which entailed watching the car racing game Forza Horizon, the ‘FPS narrative with a non-atrocity-driven narrative’ condition (see Appendix 2.i. & 2.k.), which entailed watching a walkthrough of the Call of Duty: Modern Warfare mission “Proxy War,” and the ‘FPS narrative with an atrocity-driven narrative’ condition (see Appendix 2.e. & 2.g.), which entailed watching a walkthrough of the Call of Duty: Modern Warfare mission “Hometown.” In “Proxy War,” the player must attack a Russian airbase with Arab-speaking allies in the fictional country of ‘Urzikstan.’ Within this struggle between the ‘Urzikstani’ militia fighters and Russian soldiers no apparent atrocities occur, and a strong sense of moral agency is therefore lacking for the player. In the “Hometown” mission, the player is a young ‘Urzikstani’ girl who must escape Russian soldiers that attempt to exterminate her village with chemical gas. The video ends when the girl finally overpowers a Russian soldier whose face is obscured by a gas mask, and who is also depicted as a muscular and monstrously unrestrained enemy, after more than 4 different stabbing attacks from her and her family. The execution of civilians by indiscriminate gunfire and aerial bombing also features heavily throughout this narrative, and the power disparity depicted between the Russian soldier and girl deeply echoes the superhuman animalization associated with Bar-Tal’s ‘demonization’ view of the dehumanization approach (1989).
Table 1 Game narrative Non-FPS narrative FPS game with a FPS game with an (N = 34) non-atrocity related atrocity-related narrative narrative (N = 38) (N = 43)
Findings
When considering these findings, one should note that the groups for the atrocity-driven FPS narrative (N = 43), non-atrocity-driven FPS narrative (N = 38), and non-FPS narrative (N = 34) conditions were not more or less equal in size, and that Levene's tests for Desire, F(2, 112) = 4.14, p = .018, Relaxation, F(2, 112) = 4.59, p = .012, Anxiety, F(2, 112) = 3.17, p = .046, and Anger, F(2, 112) = 3.38, p = .038, showed that equal variances in the population could not be assumed in their respective one-way ANOVAs. One should also note that Russian familial origin, Proficiency in the Russian language, and Recognition of the projected in- and outgroups were initially entered into ANCOVAs for all relevant dependent variables as covariates7 (see Appendix 3.h., 3.i., 3.j., 3.k., 3.l., and 3.m.).
Emotional Experience
Table 2 Means, Standard Deviations, and One-Way Analyses of Variance for Self-Reported Emotions Measure Atrocity-driven Non-atrocity- Non-FPS narrative F(2,114) η2 FPS narrative driven FPS narrative M SD M SD M SD Anger 3.49 1.48 1.10 1.63 0.66 0.99 46.73*** .45 Disgust 3.12 1.57 1.26 1.68 0.81 1.42 24.26*** .30 Fear 4.13 1.59 1.36 1.58 0.99 1.39 50.32*** .47 Sadness 2.87 1.17 1.18 1.36 0.86 1.25 29.29*** .34 Anxiety 4.08 1.64 1.89 1.74 1.54 1.26 30.58*** .35 Relaxation 0.87 1.26 2.80 1.85 3.18 1.49 25.52*** .31 Happiness 1.38 1.43 2.65 1.81 3.53 1.56 17.64*** .24 Desire 1.08 1.26 1.71 1.71 2.16 1.51 5.10** .08 Note: N = 115, *** p < 0.001, ** p < 0.01, * p < 0.05
7 Recognition of the projected in- and outgroups were found to improve the model for Humanization of the projected outgroup and Mechanization of the projected ingroup, and the results for the subsequent ANCOVAs are reported accordingly in the following section.
Graph 1 Note: A one-way ANOVA showed that the effect of Game Narrative on self-reported levels of Disgust was statistically significant, F(2, 114) = 24.26, p < .001. Game Narrative had a large effect on Disgust, with the factor explaining 30% of the variance in participants’ experience of this emotion, η2 = .30.
Among those exposed to the atrocity-driven FPS narrative, the average level of Disgust was higher than among those exposed to the non-atrocity driven FPS narrative and non-FPS narrative (as shown in Table 2 and visualized in Graph 1). A post-hoc test showed that the difference in self- reported levels of Disgust between those in the atrocity-driven FPS narrative and non-atrocity-driven
FPS narrative conditions (Mdifference = 1.86, p < .001, 95% CI [1.01, 2.71]), as well the levels of Disgust reported between those exposed to the atrocity-driven FPS narrative and non-FPS narrative
(Mdifference = 2.31, p < .001, 95% CI [1.43, 3.18]), were both statistically significant. This would therefore partially support Hypothesis 1. There was however no significant difference in the levels of
Disgust reported by those in the non-FPS and non-atrocity-driven FPS narrative conditions (Mdifference = 0.45, p = .686, 95% CI [-0.45, 1.35]); showing that only exposure to the atrocity-driven narrative had a significant effect on increasing Disgust.
Graph 2 Note: A one-way ANOVA showed that the effect of Game Narrative on self-reported levels of Anger was statistically significant, F(2, 114) = 46.73, p < .001. Game Narrative had an extremely large effect on Anger, with the factor explaining 45% of the variance in participants’ reported experience of the emotion, η2 = .45.
Among those exposed to the atrocity-driven FPS narrative, the average level of Anger was higher than among those exposed to the non-atrocity-driven FPS narrative and non-FPS narrative (as shown in Table 2 and visualized in Graph 2). Once again, a post-hoc test showed that the difference in reported levels of Anger between those shown the atrocity-driven FPS narrative and non-atrocity- driven narrative (Mdifference = 2.40, p < .001, 95% CI [1.63, 3.16]), as well the levels of Anger reported between those shown the atrocity-driven FPS narrative and non-FPS narrative (Mdifference = 2.83, p < .001, 95% CI [2.05, 3.62]), were both statistically significant; thereby partially supporting Hypothesis 1. There was however no significant difference in the levels of Anger reported by those in the non-FPS and non-atrocity-driven FPS narrative conditions (Mdifference = 0.44, p = .575, 95% CI [-0.37, 1.25]); showing that exposure to the atrocity-driven narrative had the greatest effect on increasing the levels of Anger experienced by participants, but that exposure to the non-atrocity- driven FPS narrative had no significant effect on manipulating Anger from the levels reported by those exposed to the non-FPS condition. Additionally, given how closely the levels of Anger and Disgust experienced by participants resemble each other in Graphs 1 and 2; both peaking in the atrocity-driven FPS narrative condition, we can see that participants also experienced greater levels of the broader sentiment Contempt while viewing the atrocity-driven FPS narrative; as predicted in Hypothesis 1.
Graph 3 Note: A one-way ANOVA also showed that the effect of Game Narrative on self-reported levels of Fear was statistically significant, F(2, 114) = 50.32, p < .001. Game Narrative had an extremely large effect on Fear, with the factor explaining 47% of the variance in participants’ reported experience of the emotion, η2 = .47.
Among those exposed to the atrocity-driven FPS narrative, the average level of Fear was higher than among those exposed to a non-atrocity-driven FPS narrative and non-FPS narrative (as reported in Table 2 and visualized in Graph 3). As with the previous two ANOVAs, a post-hoc test showed that the difference in self-reported levels of Fear between those in the atrocity-driven FPS narrative and non-atrocity driven FPS narrative conditions (Mdifference = 2.77, p < .001, 95% CI [1.94, 3.59]), as well between those in the atrocity-driven FPS narrative condition and non-FPS narrative condition (Mdifference = 3.14, p < .001, 95% CI [2.29, 4.00]), were both statistically significant. While these observations would partially support Hypothesis 1, there was nonetheless no significant difference in the levels of Fear reported by those in the non-FPS and non-atrocity-driven FPS narrative conditions (Mdifference = 0.38, p = .899, 95% CI [-0.50, 1.25]); showing once again that exposure to the simple non-atrocity-driven FPS narrative had no effect on increasing the relevant emotion participants experienced when compared with those experienced by participants in the non- FPS condition.
Animalization, Mechanization and Humanization of the Projected Outgroup
Graph 4 Notes: A one-way ANOVA showed that the effect of Game Narrative on Animalization of the projected outgroup was not statistically significant, F(2, 114) = 0.00, p = .999.
Among those exposed to the atrocity-driven FPS narrative (M = 2.29, SD = 1.49), non- atrocity-driven FPS narrative (M = 2.28, SD = 1.51), and non-FPS narrative (M = 2.29, SD = 1.22) the average level that participants animalized Russian surnames was more or less equal; as shown in Graph 4. Hypothesis 2 was therefore not supported.
Graph 5 Note: A one-way ANOVA showed that the effect of Game Narrative on Mechanization of the projected outgroup was not statistically significant, F(2, 114) = 0.70, p = .501.
Among those exposed to the atrocity-driven FPS narrative (M = 2.84, SD = 1.32), non- atrocity-driven FPS narrative (M = 3.11, SD = 1.15), and non-FPS narrative (M = 3.16, SD = 1.49), the average level that participants mechanized Russian surnames did not differ in any significant fashion (see Graph 5 for visualization), and Hypothesis 2 was therefore not supported.
Graph 6 Note: A single-factor ANCOVA showed that the effect of Game Narrative on Humanization of the projected outgroup was not statistically significant, F(2, 114) = 0.29, p = .751.
While those exposed to the non-atrocity-driven FPS narrative (M = 3.37, SD = 1.05) humanized Russian surnames more so than those in the atrocity-driven FPS narrative (M = 3.16, SD = 1.33), and non-FPS narrative (M = 2.82, SD = 1.07), this effect was not found to be statistically significant; as seen in Graph 6. While Hypothesis 2 was therefore once again not supported by these findings, the ANCOVA did show that Recognition of the projected in- and outgroups had a significant and moderate effect on how much participants humanized the projected outgroup, F(1, 114) = 6.20, p = .014, η2 = 0.05, and explained approximately 5% of the variance in participants’ humanization of Russian surnames.
Animalization, Mechanization and Humanization of the Projected Ingroup
Graph 7 Note: A one-way ANOVA showed that the effect of Game Narrative on Animalization of the projected ingroup was not statistically significant, F(2, 114) = 0.34, p = .711.
Among those exposed to the atrocity-driven FPS narrative (M = 2.06, SD = 1.62), non- atrocity-driven FPS narrative (M = 1.91, SD = 1.51), and non-FPS narrative (M = 2.19, SD = 1.27) the average level that participants animalized Arab surnames was more or less equal (as shown in Graph 7), and Hypothesis 3 was therefore not supported.
Graph 8 Note: A single-factor ANCOVA showed that the effect of Game Narrative on Mechanization of the projected ingroup was not statistically significant, F(2, 114) = 0.31, p = .732.
While those exposed to the non-atrocity-driven FPS narrative (M = 3.00, SD = 1.23) mechanized Arab surnames more so than those in the atrocity-driven FPS narrative (M = 2.56, SD = 1.30), and non-FPS narrative (M = 2.59, SD = 1.24), this effect was not found to be statistically significant; as seen in Graph 8. The lack of any significant variation in our dependent variable therefore rendered Hypothesis 3 unsupported yet again. The ANCOVA however did show that Recognition of the projected in- and outgroups had a significant and moderate effect on how much participants mechanized the projected ingroup, F(1, 114) = 8.42, p = .004, η2 = 0.07, and explained approximately 7% of the variance in participants’ mechanization of Arab surnames.
Graph 9 Note: A one-way ANOVA showed that the effect of Game Narrative on Humanization of the projected ingroup was statistically significant, F(2, 114) = 3.26, p = .042. Game Narrative had a moderate effect on humanizing the projected ingroup, with the factor explaining 5% of the variance in how much participants humanized Arab names, η2 = .05.
Lastly, among those exposed to the atrocity-driven FPS narrative, the average level of ingroup humanization was lower (M = 3.40, SD = 1.36), than among those exposed to the non- atrocity-driven FPS narrative (M = 3.56, SD = 1.07), but higher than those exposed to the non-FPS narrative (M = 2.86, SD = 1.14). As seen in Graph 9, these means did in fact vary by a statistically significant margin. A post-hoc test showed that the difference in Humanization of the projected ingroup between those watching the non-atrocity-driven FPS narrative and non-FPS narrative
(Mdifference = 0.70, p = .047, 95% CI [0.01, 1.39]), was statistically significant. There was however no significant difference between how much those in the non-FPS and atrocity-driven FPS narrative conditions (Mdifference = 0.54, p = .168, 95% CI [-0.14, 1.21]), as well as those in the non-atrocity- driven FPS narrative and atrocity-driven FPS narrative conditions (Mdifference = 0.16, p = 1.000, 95% CI [-0.49, 0.82]), humanized the projected ingroup; showing that exposure to the non-atrocity-driven narrative actually had the greatest effect on humanizing the projected Arab ingroup, compared with exposure to any of the other narratives. The findings therefore once again do not support Hypothesis 3.
Discussion
Emotional Experience Although the non-atrocity-driven narrative seemingly had no significant effect on manipulating relevant emotions among participants, the fact that exposure to the atrocity-driven narrative did have a significant and substantial effect on increasing participants’ levels of Disgust, Anger, Fear and Contempt means that the main thrust of Hypothesis 1’s prediction relating to the preeminence of atrocious narratives in fostering these emotions seems to have been supported. Only atrocious content therefore has a serious effect on evoking Fear, Disgust, Anger, and Contempt in viewers, and not FPS gameplay itself; as is evidenced in Graphs 1, 2, and 3. The occurrence of an identical trend for other negative emotions like Sadness and Anxiety (see Table 2 for reporting and Appendix 3.n. to 3.o. for visualizations) echoes the findings of previous studies. One can for instance infer that the experiential system of information processing; i.e. the one “closely tied to intuitions and gut feelings” (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 112), was at work during exposure to the atrocity-driven narrative, given that this was also the operative narrative in manipulating all key negative emotions. By extension, the findings therefore also seem to build on the understanding that players, and in our case viewers, do intuit moral judgements from game narratives that are emblematic of character-centric portrayals of justified violence and empathetic story-telling (Hartmann, Toz, & Brandon, Just a Game? Unjustified Virtual Violence Produces Guilt in Empathetic Players, 2010, p. 355; Haidt, 2001). The particularly high levels of Contempt experienced throughout the morally outrageous narrative are particularly evident of this process having taken place for instance. Previous studies found that “users automatically feel empathy with virtual characters” (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p.114; Morrison & Ziemke, 2005) or for any “social entities once they have detected biological motion” (Hartmann, Toz, & Brandon, Just a Game? Unjustified Virtual Violence Produces Guilt in Empathetic Players, 2010, p. 342; Ahlström, Blake, & Ahlström, 1997; Morewedge, Preston, & Wegner, 2007). The present study however suggests that if such a base level of empathy for anthropomorphized FPS projections does occur, it is extremely limited. If participants were to have empathized with the projected Russian enemies or Arab allies in the non-atrocity-driven and ‘morally-neutral’ FPS narrative for instance, one would also have expected their deaths to have driven significantly higher levels of self-reported Sadness or even Anxiety (see Appendix 3.n. and 3.o. for visualization) compared to those reported by participants who simply watched a car-racing game, which was not the case.
Whether the atrocity-driven narrative in question should be viewed more as an item of atrocity propaganda than entertainment is a harder question to answer however. For example, inducing Fear and Anxiety has been shown to heighten audience’s enjoyment of horror movie narratives (Hoffner & Levine, 2005), and it has also been argued that Sadness is often “perceived as gratifying” by audiences in some way (Hoffner & Levine, 2005, p. 231; Oliver, 1993, p. 319). Other negative emotions like Disgust can also be enjoyed when they are “framed as unreal,” and in “a protective frame or distance from the aversive material” in question (Clifford & Jerit, 2018, p. 219; Woody & Teachman 2006, p. 293). The latter point is key, and begs the question of whether the violence depicted throughout the atrocity-driven FPS narrative was really perceived as “unreal” by participants, given its parallel of a real and active conflict in Syria.
Klimmt, Schmid, Nosper, Hartmann and Vorderer (2006) apply Bandura’s theory of moral disengagement (2002) in order to explain the recreational appeal of violent video games; “in order to maintain their enjoyment of game violence, players find effective strategies to avoid or cope with the moral conflict related to their violent behaviors in the game world.” Vividly depicting atrocities in violent video games that mirror atrocities occurring in real life, and which players are also likely cognizant of, is something these authors did not consider in their application of Bandura’s framework however, and could therefore also inhibit strategies that are geared towards moral disengagement with such pseudo-fictional narratives. If we consider for instance that 72% of those exposed to the atrocity-driven narrative were able to accurately describe who the projected in- and outgroups were, and that this number fell to only 26% for those in the non-atrocity-driven narrative condition, we might conclude that those exposed to the former were likely far more cognizant of how the narrative’s depiction reflected an ongoing conflict than those in the latter. This could also explain why the error bars for Desire, Happiness and Relaxation were always lowest and comparatively narrower in the atrocity-driven narrative (see Appendix 3.p., 3.q. and 3.r. for graphic visualizations); suggesting a higher certainty of these emotions among participants in this condition. Although decreasing the levels of Relaxation experienced by gamers can certainly be beneficial for certain gaming experiences, the fact that atrocity-driven participants showed such a narrow range of Happiness and Desire, and at such a low level, suggests that atrocity-driven narratives may not be particularly enjoyable (at least to the general population); given that Happiness was measured with items closely associated with ‘enjoyment’ (see Appendix for 2.s. for measurement items), and that the levels of Desire reported by participants were presumably indicative of participants’ desire to participate in the game narrative. Future studies that take into account how players react to an atrocity-driven narrative with no real-world parallel would clarify how much moral disengagement with the violence of a game narrative is also incumbent on its reflection of real-world happenings. A study geared towards a more narrow measurement of enjoyment would also help in elucidating whether such atrocity-driven narratives do in fact offer any entertainment value to viewers or players.
Animalization, Mechanization and humanization of the projected in- and outgroups
The absence of any significant variation in how much participants animalized and mechanized the game narratives’ projected in- and outgroups could be indicative of Game Narrative having had no impact on dehumanizing these various groups, or it could just as easily be characteristic of a type II error resulting from the visual mediation of the stimulus and operationalization of relevant measures. The fact that COVID-19 forced the research design to move from 18 minutes of active gameplay to 12 minutes of gameplay-viewing meant one’s ability to generalize findings to actual gaming would always be significantly limited. The key difference affecting one’s ability to generalize these results to gaming was the fact that participants no longer actively took part in the game narrative, which likely affected participant’s experience and reactions in a number of ways. When considering dehumanization for instance, the absence of gameplay likely had a substantial impact on participants’ impetus to morally disengage. Involvement in virtual killing motivates morally disengaging with one’s virtual enemies (Klimmt, Schmid, Nosper, Hartmann, & Vorderer, 2006), which by my initial prediction meant seeing such an enemy as either more machine or animal depending on the narrative one followed. Since participants did not participate in any virtual killing, this lack of involvement also meant participants had far less of a cognitive stake in dehumanizing the projected enemy. When considering humanization on the other hand, the issue of ‘presence’ (Lee, 2004) and identification with the player is most at issue. In order for an “illusion of non-mediation” to occur (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 110; Tamborini & Skalski, 2006), a viewer of the narrative would need to attain a sense of ‘spatial’ presence via virtual mobility, ‘social’ presence via virtual interaction with projections, and ‘self-presence’ via virtual self-action (Tamborini & Skalski, 2006, p. 88-90). The extent to which a sense of perhaps partial self-presence (Lee, 2004) and identification with the game-self (Schneider, Lang, Shin, & Bradley, 2004) might have occurred just through the first-person framing of the narrative’s visual mediation is once again impossible to establish, and can only be elucidated by a future lab-based study.
With this in mind, the findings do show a trend by which simple exposure to either FPS narrative moderately raised participants’ humanization of the projected ingroup more so than that of those in the non-FPS condition; which presented no ingroup whatsoever (as seen in Graph 9). This might push us to conclude that a sort of self-identification with the player in either FPS walkthrough mission did occur, and in turn heightened viewer’s humanization of the projected ingroup. A number of caveats also present themselves when considering these findings however. We should firstly note that statistical significance was only barely achieved; with the lower bound of the confidence interval relating to the difference in humanization featured between the non-FPS narrative and non-atrocity- driven narrative barely scraping past zero; (Mdifference = 0.70, p = .047, 95% CI [0.01, 1.39]). In addition to this, the fact that both our variables for Humanization of the projected in- and outgroup proved unreliable measurement scales could present a further type II error resultant of the novel and abstract nature of this study’s dehumanization measure. While this could of course be mitigated by using Martínez, Rodríguez-Bailón, and Moya’s (2012) original implicit association task in a future lab setting, the inconsistency of the present measure certainly doesn’t indicate its suitability for future replication. We should finally also note that a very similar trend to the one shown in Graph 9 also occurs with the projected outgroup in Graph 6; Participants in the two FPS conditions also humanized the projected Russian outgroup more so than participants who weren’t even presented with any outgroup in the non-FPS condition, albeit not quite by a statistically significant margin.
This final consideration begs the question of whether either in- or outgroup may have been humanized simply by their mere presence throughout the narrative? According to Young, Patrickson, and Hitchens, enemies are perceived in a ‘collectivized’ manner by players (2013, p. 10; Van Leeuwen, 1996, p. 49), especially when their depictions are characterized by behavioral and appearance-related uniformity (2013, p. 10; Machin & Suleiman, 2006). In the non-atrocity-driven narrative both allies and enemies appear interchangeable within their groups by way of their uniforms and behavior, yet we see a far more personal and non-identical dynamic appear between the girl, her family, and the demonized Russian soldier throughout the atrocity-driven narrative. The fact that none of these factors seemed to have had any significant impact on differentially humanizing either the in- or outgroup suggests that individuals are quite resistant to integrating visually-mediated FPS portrayals of ethnic groups, whether these are “collectivized” in their depiction or not.
Overall ‘resistance’ to the game narrative’s political messaging therefore seems to best characterize the results attained. Given that Hypothesis 1 was supported, and that participants did in fact experience the atrocity-related material in quite an emotionally shocking manner, we would reasonably also expect some difference in the mode by which participants processed either narrative. Since participants found the atrocity-driven narrative far more emotionally impactful than the non- atrocity-driven narrative; often by a margin of more than two to three points (See Graph 1, 2 and 3), we can conclude that participants in the former condition must have therefore been processing at least parts of the narrative in an experiential manner. If the atrocity-driven narrative was therefore emotionally impactful but uncompelling in its messaging, then it would be reasonable to conclude that the experiential process initially laid out for triggering an emotional heuristic through morally outrageous “images, metaphors and narratives” does not necessarily make for effective visual propaganda (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 110). We should nonetheless also note that with a change in mediation via gameplay, the level of “psychological attachment” users would have felt with the narrative’s “depicted action” would presumably have been far greater (Hartmann, Moral Disengagement During Exposure to Media Violence: Would It Feel Right to Shoot an Innocent Civilian in a Video Game?, 2012, p. 110; Cupchick, 2002), and the integration of the narrative’s messaging likely different.
Conclusion
The analysis has shown that visual exposure to atrocity stimuli in FPS games has a large and significant effect on heightening individuals’ experience of the negative emotions most associated with atrocity propaganda; namely Fear, Anger, Disgust, and Contempt more broadly. The fact that the atrocity-driven narrative was shown to lower positive emotions corroborates previous evidence that gamers often disapprove of morally outrageous depictions of violence (Soto, Hartmann, & Prins, 2010; Whitty, Goodings, & Young, 2011), and begs the question of why many game developers might continue to go out of their way to include them in game narratives nonetheless. Previous studies have suggested that habitual FPS gameplay may be associated with a lower activation of the lateral prefrontal cortex and a higher Behavioral Inhibition System (BIS) in response to troublesome content (Montag, et al., 2012), which consequently disinhibits the typical kind of behavior one would expect towards such stimuli, and decreases the levels of empathy elicited for violence against third parties (Montag, et al., 2012). Whether increasing the shock value of FPS narratives with material that mimics real and contemporary atrocities should therefore be attributed to game developers trying to emotionally stimulate emotionally disinhibited players, or to the simple purpose of generating media hype for their new product is more difficult to discern. We can however conclude that there is no evidence to indicate that visual exposure to atrocities in FPS narratives has any significant effect on humanizing, animalizing or mechanizing projected in- or outgroups; at least in the short-term. Instead, exposure to FPS gameplay more generally seems to serve a function in moderately humanizing the ingroup projected by the game’s narrative. This has deep practical implications for society at large. Call of Duty and other military themed-FPS games have received ample attention in the past two decades for projecting uniformly Arab enemies (Machin & Suleiman, 2006; Šisler, 2008) and “neo-orientalist themes” via their depiction of Middle Eastern battleground settings (Mantello, 2013, p. 638), yet the present study also shows that simple visual exposure to gameplay featuring would-be outgroups as projected ingroups can also have a significant effect on individual’s humanization of these ethnicities. This suggests that FPS video games could also serve a positive role in lowering negative perceptions of every-day outgroups if the protagonists offered by game narratives are sufficiently diversified in the future.
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Appendix 1.a.
World War I propaganda poster developed by H. R. Hopps (2017) for the U.S. Army. Retrieved from iwm.org.uk: https://www.iwm.org.uk/collections/item/object/13610.
2.a.
Are you a U.S. citizen? • Yes • No
2.b.
What is your Gender? • Male • Female • Other
2.c. (Mathematical reasoning task - 34 questions)
The next page consists of a mathematical reasoning task. For each question you will be presented with a number and four arithmetic equations to choose from. One of the four equations correctly delivers the number listed on the question as an outcome. Choose the correct equation for each question's outcome number.
You will have a total of 3 minutes to complete this task. Don't worry if you can't finish. Just try to correctly answer as many questions as possible.
Once you click the next button, the timer will start.
• e.g. Q33: 8 o 3 + 5 = o 9 – 2 = o 2 + 1 + 3 = o 5 + 4 =
2.d.
Task 2
On the next page you will be presented with a video. Please watch the video once, from start to finish. You will then need to answer some questions about the video you watched so please pay close attention. After this you will need to watch a second video and answer some questions again.
If possible, please view the video in a full-screen format. Please also ensure that the audio is turned on.
2.e. (FPS-Atrocity narrative video 1)
URL: https://youtu.be/rnnsk25RKwY
2.f.
Q118: What prompt was displayed on the screen at the end of the video? • Kill the soldier • Escape from the soldier • Hide from the soldier • Protect Hadir (the youngest brother) from the soldier
Q119: Where did the father suggest they escape to? • The mountains • The caves • The border • The forest
2.g. (FPS-Atrocity narrative video 2)
URL: https://youtu.be/ENwSkvHN8Es
2.h.
Q129: Please select all the places the player hid during the video. • Under a table • Under a bed • In a vent • Under a couch • Behind a door • In a closet
Q130: How many stabbing attacks were needed to bring the soldier to the ground? • 2 • 3 • 4 • 5
2.i. (FPS non-Atrocity Narrative video 1)
URL: https://youtu.be/cRYdnGSxDCQ
2.j.
Q120: How many times did the player fly an RC plane? • 6 • 5 • 4 • 7
Q122: What was the name given to mission at the beginning of the video? • “Proxy War” • “Going Dark” • “Fog of War” • “Embedded”
2.k. (FPS non-Atrocity Narrative video 2)
URL: https://youtu.be/f199--Co41Q
2.l.
Q131: At the beginning of the video, the player had to enter and secure an armory in the shape of a container, with two enemies in it. What was the color of this armory? • Red • Brown • White • Blue
Q132: How many helicopters did the player need to bring down in this video? • None • 1 • 2 • 3
2.m. (Non-FPS Narrative 1)
URL: https://youtu.be/zwewSjnbbEg
2.n.
Q123: What was the color of the player’s car? • Silver and black • Black and blue • White and silver • Black and white
Q124: Please select all the scenery that the player drove past in the game. • A castle • A lake/pond • A beach • A mountain • A playground • A golf course • A vineyard
2.o. (Non-FPS Narrative 2)
URL: https://youtu.be/UTltnqMvlVs
2.p.
Q133: How many times does the player's car smash through a stone wall on the side of the road? • 1 time • 2 times • 3 times • Never
Q134: At the end of the video there is one last car the player must pass. What is the colour of this last car? • Red • Black • Silver • Blue
2.q.
Task 3
The next task deals with one's perception of words. You'll be presented with 15 surnames. While this task has no time limit, you will still be timed from start to finish. Please try to spend no more than 30 seconds per question.
For each surname you'll have to rate how much you think of each of the three words when reading the surname. If you're unsure, rely on your intuition; rate how much the word speaks to you after having reread the surname.
2.r. Note: Five subitems were averaged into measurement scales for the dependent variables Animalization of the projected outgroup (Petrov – Breed, Sokolov – Pet, Vasiliev – Animal, Ivanov – Irrational, Mikhailov - Wild), Mechanization of the projected outgroup (Petrov – Device, Sokolov – Mechanical, Vasiliev – Technological, Ivanov – Tool, Mikhailov - Caluclating), Humanization of the projected outgroup (Petrov – Passive, Sokolov – Individual, Vasiliev – People, Ivanov – Rational, Mikhailov - Single), Animalization of the projected ingroup (Ahmad – Wild, Basara – Irrational, Nassar – Breed, Rahal – Pet, Mohamed – Animal), Mechanization of the projected ingroup (Ahmad – Calculating, Basara – Tool, Nassar – Device, Rahal – Technological, Mohamed – Mechanical), and Humanization of the projected ingroup (Ahmad – People, Basara – Rational, Nassar – Single, Rahal – Passive, Mohamed – Individual).
Please rate how much each of the three words speaks to you, when reading the surname. If you're unsure, trust your initial intuition.
Q34: Ahmad (for each: 0 not at all – 7 a lot) • Calculating • People • Wild
Q93: Basara • Tool • Rational • Irrational
Q95: Nassar • Device • Single • Breed
Q96: Rahal • Technological • Passive • Pet
Q97: Mohamed • Mechanical • Individual • Animal
Q101: Johnson • Calculating • Passive • Breed
Q102: Williams • Technological • People • Pet
Q103: Miller • Mechanical • Individual • Irrational
Q105: Thompson • Device • Rational • Animal
Q107: Smith • Tool • Single • Wild
Q110: Petrov • Device • Passive • Breed
Q111: Sokolov • Mechanical • Individual • Pet
Q112: Vasiliev • Technological • People • Animal
Q113: Ivanov • Tool • Rational • Irrational
Q114: Mikhailov • Calculating • Single • Wild
2.s. Note: Scale variables were recorded by averaging four items for each of these relevant emotions, as is common practice for the DEQ (Harmon-Jones, Harmon-Jones, Aleman, & Bastian, 2016); Anger (Anger, Rage, Pissed off, Mad), Disgust (Grossed out, Nausea, Sickened, Revulsion), Fear (Terror, Scared, Panic, Fear), Anxiety (Anxiety, Dread, Worry, Nervous), Sadness (Sad, Grief, Lonely, Empty), Desire (Wanting, Desire, Craving, Longing), Relaxation (Relaxation, Chilled out, Calm, Easygoing) and Happiness (Happy, Satisfaction, Enjoyment, Liking).
Task 4
There is no time limit for this task. Please reflect on how you felt while watching the video earlier, and answer the questions as honestly as possible.
Q76: Please rate how much these emotions applied to your experience while watching the video, on a scale of 0 (not at all) to 6 (an extreme amount). • Calm • Nausea • Sickened • Grossed out • Anxiety • Rage • Mad • Panic • Desire • Lonely • Wanting • Pissed off • Chilled out • Revulsion • Enjoyment • Grief • Fear • Easygoing • Empty • Anger • Happy • Worry • Dread • Longing • Relaxation • Sad • Nervous • Satisfaction • Craving • Terror • Scared • Liking • Attention Check – please select ‘2’
2.t.
Q12: Please answer the following question:
How often have you been exposed to this particular video game mission, prior to this survey?
0 (never) – 7 (many times)
2.u. Note: The Recognition of an atrocity having occurred was computed by reverse coding Q13a and Q13d and averaging these with Q13b and Q13c into a scale variable. Thus, for both Q13a and Q13d ‘0’ became ‘6,’ ‘1’ became ‘5,’ ‘2’ became ‘4,’ ‘3’ stayed as ‘3,’ ‘4’ became ‘2,’ ‘5’ became ‘1,’ and ‘6’ became ‘0’.
Q13: On a scale of 0 (Completely disagree) to 6 (completely agree), how much do you agree with the following statements:
Q13a: The actions portrayed by my enemy in this game would be justified under the laws of the Geneva Convention on Armed Conflicts
Q13b: The actions portrayed by the player and (if applicable) the player’s allies in this game would be justified under the laws of the Geneva Convention on Armed Conflicts.
Q13c: The actions portrayed by the enemy in this game were extremely brutal and cruel.
Q13d: The actions portrayed by the player and (if applicable) the player’s allies in this game were extremely brutal and cruel.
2.v. Note: Recognition of the projected in- and outgroups was recoded into three nominal groups; ‘No recognition’ (1), ‘partial recognition’ (2), and ‘full recognition’ (3). ‘No recognition’ refers to when a participant was able to accurately identify neither the enemies nor the allies in the game (e.g. Q14: “Black”, Q15: “American”). ‘Partial recognition’ refers to when a participant was able to accurately identify either only the enemies or allies in the game (e.g. Q14: “Hispanic”, Q15: “Russian” or Q14: “Arab”, Q15: “Spanish”). ‘Full recognition’ refers to when a participant accurately recognized both the enemies and allies in the game narrative (e.g. Q14: “Arab”, Q15: “Russian”). Attributes that run parallel or are characteristic of the fictional and vaguely Arab ingroup are acceptable, such as: “Muslim,” Central Asian”, “Ubzbeck”, “Urzekstani”, “Iranian,” “Syrian.” This is because the game narrative never explicitly mentions the projected ingroup’s ethnicity, but only does so implicitly through the Arab names and language characters have and Islamic faith attributed to characters that live in the fictional and vaguely Central Asian geographical setting of “Urzekstan.” For the Russian outgroup, only some variation of “Russian” or “Eastern European” was accepted. For use in ANCOVAs this variable must be recoded with all those in the non-FPS condition receiving a value of ‘3’ (full recognition), since there were no in- or outgroups in for this narrative, and so that those in this condition are not excluded from the analysis.
Q14: To the best of you knowledge, which ethnic group was the character playing alongside in the video you watched? (Open Answer)
Q15: To the best of you knowledge, which ethnic group was the character playing against in the video you watched? (Open Answer)
2.w.
Q16: Please answer the following questions with Yes or No.
Q16a: Are you proficient in the Arabic language? Q16b: Are you proficient in the Russian language? Q16c: Is anyone in your family of Russian origin? Q16c: Is anyone in your family of Russian origin? 2.x.
Q17: What is your age in years? (e.g. 26)
Q82: What device did you complete this survey on • Computer • Smart phone • Tablet • Other
2.y. For this final task, please watch this 1 minute video from start to finish.
URL: https://www.youtube.com/watch?v=HVLwaTs4sFQ&feature=youtu.be
2.z. Broader questionnaire design
Initially, the experiment was designed for the purpose of a lab study in which approximately 18 minutes of gameplay would be preceded and followed by a number of paper and pencil tasks. However, due to the COVID-19 pandemic, lab studies at the University of Amsterdam were no longer possible, and the design was translated into an online survey-experiment using Qualtrics. Participants were first asked two demographic questions; whether they were U.S. citizens and their gender (see Appendix 2.a. & 2.b.). They were then tasked with completing a three-minute mathematical reasoning task (see Appendix 2.c.), with the intention of providing them with the sense that the experiment did in fact measure attention span. Next, Participants watched two YouTube videos of gameplay split into two blocks, and were instructed that they’d need to answer some questions after each viewing (see Appendix 2.f., 2.h., 2.j., 2.l. 2.n. & 2.p.). This was done for a number of reasons. Firstly, by giving participants an incentive to pay attention, it was hoped that reception of the stimulus in the video would match the involvement players have during game play as much as possible. Secondly, it also reinforced the narrative that this experiment measured attention span. The first video lasted approximately six and a half minutes and the second lasted five and a half minutes; totaling 12 minutes of total exposure. Immediately following exposure to one of the three conditions, participants completed the word-association task that measured implicit humanization and dehumanization of the groups involved (see Appendix 2.r.). Participants were instructed that although the task had no time-limit, they were nonetheless being timed and that they should spend no less than 30 seconds on each name. After the word association task was completed, participants completed the full DEQ (see Appendix 2.s.), with no time-limit set. Control and manipulation checks were then posed in the final part of the survey. Lastly, participants viewed a one-minute long nature video as a relaxation task (see Appendix 2.y.). Participants in the non-FPS condition skipped the atrocity-related control and manipulation check questions, as well as the relaxation task.
3.a. To check if participants’ Gender distribution was comparable across all three conditions a crosstabulation was conducted and found no significant effect X2 (2, N = 115) = 2.2, p = .335; suggestion randomization was successful.
3.b. In order to check if participants’ Age was comparable across all conditions, a one-way ANOVA was conducted, which showed that participants’ mean Age in the non-FPS condition (M = 24.24 years, SD = 2.05), non-atrocity-driven narrative condition (M = 23.76 years, SD = 2.24) and atrocity-driven narrative condition (M = 23.72 years, SD = 1.92) did not differ significantly, F(2, 114) = 0.69, p = .503; suggesting that randomization across all conditions was successful.
3.c. To check if Proficiency in the Arabic language was comparable across our three conditions a crosstabulation was conducted and found no significant effect X2 (2, N = 115) < 0.0, p = .986; suggesting randomization of the variable was successful. An identical randomization check found there to be a medium-to-strong and statistically significant association between Proficiency in the Russian language and its distribution across all three conditions however X2 (2, N = 115) = 7.3, p = .025, V = .25, leading us to include it as a covariate during the final analysis. All three of those who were proficient in Russian (100%) thus fell in the non-FPS game condition.
3.d. To check if Arabic familial origin was comparable across all three conditions a crosstabulation was conducted and found no significant effect X2 (2, N = 115) = 4.8, p = .091; suggesting randomization had succeeded. An identical randomization check found there to be a medium-to-large and statistically significant association between Russian familial origin and the three conditions however X2 (2, N = 115) = 11.5, p = .003, V = .32, leading us to include it as a covariate in the final analysis. six (86%) of those who identified as having Russian familial origin fell in the non-FPS condition, with only one such respondent (14%) falling in the non-atrocity-driven narrative condition.
3.e. In order to check if Prior exposure to the game narrative was comparable across all conditions, a one-way ANOVA was conducted, which showed that this variable did not differ significantly among participants in the non-FPS condition (M = 0.68, SD = 1.30), non-atrocity-driven FPS narrative condition (M = 0.53, SD = 1.31), and atrocity-driven FPS narrative condition (M= 0.84, SD= 1.72), F(2, 114) = 0.45, p = .638; suggesting randomization of the variable succeeded.
3.f. To check if the Type of device used to carry out the survey was comparable across our three conditions, a crosstabulation was conducted and found no significant effect X2 (4, N = 119) = 1.3, p = .864; suggesting that randomization had succeeded.
3.g. In order to check if participants had recognized the in- and outgroups projected by our two FPS narratives, a crosstabulation was conducted, which showed that there was an extremely large and statistically significant association between Recognition of the projected in-and outgroups, and it’s distribution across both conditions, X2 (2, N = 81) = 19.59, p < .001, V = .49. For instance, whereas only 26% of participants accurately described the ethnicity of the projected in- and outgroups in the non-atrocity driven FPS narrative condition, this number rose to 72% in the atrocity-driven FPS narrative condition. While partial recognition of the in- and outgroups among those in the atrocity- driven FPS narrative (23%) was lower than among those in the non-atrocity-driven narrative conditions (40%), this trend reversed again when one considers that 34% of those in the non- atrocity-driven FPS narrative condition recognized neither of the groups, compared to only 5% in the atrocity-driven FPS narrative condition. The fact that far more people seemed to recognize the in- and outgroups in the atrocity-driven narrative condition compared to those in the simple FPS narrative meant that this variable would be entered into our final analysis as a covariate for variables related to the animalization, mechanization and humanization of our various in- and outgroups.
3.h. Table 3: Results for initial single factor Animalization of projected outgroup ANCOVA SS df MS F p η2 Game narrative 1.81 2 0.91 0.44 0.643 0.01 Russian familial 0.32 1 0.32 0.15 0.695 < 0.01 origin Proficiency in 0.00 1 0.00 0.00 0.989 < 0.01 Russian Recognition of 3.69 1 3.69 1.81 0.182 0.02 projected in- and outgroup ethnicities Error 222.71 109 2.04 Total 227.13 114 Note: N = 115
3.i. Table 4: Results for initial single factor mechanization of projected outgroup ANCOVA SS df MS F p η2 Game narrative 4.00 2 2.00 1.14 0.325 0.02 Russian familial 0.97 1 0.97 0.55 0.460 0.01 origin Proficiency in 0.24 1 0.24 0.14 0.71 < 0.01 Russian Recognition of 0.49 1 0.49 0.28 0.60 < 0.01 projected in- and outgroup ethnicities Error 191.80 109 1.76 Total 197.74 114 Note: N = 115
3.j. Table 5: Results for initial single factor humanization of projected outgroup ANCOVA SS df MS F p η2 Game narrative 0.62 2 0.31 0.24 0.790 < 0.01 Russian familial 0.04 1 0.04 0.03 0.856 < 0.01 origin Proficiency in 0.29 1 0.29 0.22 0.642 < 0.01 Russian Recognition of 8.08 1 8.08 6.13 0.015 0.05 projected in- and outgroup ethnicities Error 143.66 109 1.32 Total 157.62 114 Note: N = 115
3.k. Table 6: Results for initial single factor Animalization of projected ingroup ANCOVA SS df MS F p η2 Game narrative 6.41 2 3.20 1.46 0.238 0.03 Russian familial 0.85 1 0.85 0.39 0.536 < 0.01 origin Proficiency in 0.01 1 0.01 < 0.01 0.947 < 0.01 Russian Recognition of 5.15 1 5.15 2.34 0.129 0.02 projected in- and outgroup ethnicities Error 239.98 109 2.02 Total 248.29 114 Note: N = 115
3.l. Table 7: Results for initial single factor Mechanization of projected ingroup ANCOVA SS df MS F p η2 Game narrative 1.76 2 0.88 0.59 0.558 0.01 Russian familial 0.63 1 0.63 4.22 0.517 < 0.01 origin Proficiency in 0.20 1 0.20 0.13 0.716 < 0.01 Russian Recognition of 11.93 1 11.93 7.96 0.006 0.07 projected in- and outgroup ethnicities Error 163.42 109 1.50 Total 182.65 114 Note: N = 115
3.m. Table 8: Results for initial single factor Humanization of projected ingroup ANCOVA SS df MS F p η2 Game narrative 2.78 2 1.39 0.96 0.387 0.02 Russian familial 0.02 1 0.02 0.02 0.902 < 0.01 origin Proficiency in 2.92 1 2.92 2.01 0.159 0.02 Russian Recognition of 0.92 1 0.92 0.63 0.428 0.01 projected in- and outgroup ethnicities Error 158.19 109 1.45 Total 172.83 114 Note: N = 115
3.n.
3.o.
3.p.
3.q.
3.r.