BEA 2011 "Gut or Game"

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  • Nice Start
  • Our first experiment surveyed largely college students (N = 497) from US and Germany (66%, or n = 328, were from Germany) to find the moral salience patterns between cultures. The patterns were quite clear and significant.
  • First, we looked at general patterns of moral violations; all of these distributions were tested using chi-square goodness-of-fit tests. For our German children, we saw their moral salience scores fall in line with what we might expect from a German population. Harm/Care and Fairness/Reciprocity were rated as most salient, and significantly more so than the other moral modules. As well, Germany children chose not to violate Harm/Care and Fairness/Reciprocity, in line with theory (“gut” decisions). For the other moral modules, violations were observed at random (“game” decisions). The story in the US was a bit different. First, we really didn’t find stark differences in moral salience patterns in the US as would have been expected (the increased salience of Authority/Respect, In-group/Loyalty, and Purity/Sanctity). However, this might not be surprising as moral foundations develop through the life-span and might not have been salient just yet (this is why we have the Elderly sample in the study). The fairness/reciprocity effect was replicated in the US children (again, not surprising as Fairness/Reciprocity importance has been found in children as young as three – it is often thought to be the module that develops first). Particularly troubling is the Harm/Care finding – that decisions to violate were random but trended toward violation even though this was the most salient module for US (and for that matter, Germans). But I think we can explain this: Methodologically, the Harm/Care scenario was “first” in the video game. We noticed in at least two experimental sessions with US participants that 100% of all participants in the study that the children chose violation. This could merely be a procedural artifact as it was the first “option” in the game (as well as in the study). We are not sure why this didn’t happen in the German sample (maybe they listen to instructions better *wink*) We found spatial presence scores to be significantly higher in the US children than the German children. This is interesting because it might suggest that US respondents approached the game as just that, a game. Approaching the simulation as a game might have given them license to violate Harm/Care, or moreover they might have tapped into a “learned expectation” regarding video game play as inherently violent – in other words, they behaved more violently because in video games, this sort of behavior is okay; they were in the “magic circle” of the video game where real-world social mores do not apply. This effect should be studied in replication.
  • With the digital immigrants, we saw the most important modules (Harm/Care and Fairness/Reciprocity) to be the least violated, both between and across nations. Our German respondents’ moral salience was right in line with earlier surveys (Harm/Care and Fairness/Reciprocity being most salient). For the us, module salience didn’t differ significantly in that all scores hovered around 4.5 on our 6.0 scale; perhaps not surprising in that our sample was a bit more liberal than the “typical” American sample – for purposes of our study, they (the US elderly) behaved more German! ODD FINDING: German participants seemed to violate morality much more overall. Presence for the German elderly was low, which suggests that they might have merely been playing around with the game and it’s options. This runs counter to our discussion above about US children having high presence and therefore making “game” reactions more, but with one key difference – in the Germans, there were not likely any learned expectations about technology. We have some anecdotal evidence to suggest that they weren’t taking the study very seriously and might have been goofing around. In other words, not playing the game for “gaming” sake but playing around to finish the study. We’re also considering measuring technophobia or other related measures to see how they are approaching games differently from other populations.
  • While our first data tables show general pattern of module salience on observed violations, we were keenly interested in the specific effect of module salience. Our predictions were concerned with how „real-world“ morality might affect decisions in virtual worlds. Interestingly, most theorizing seems to assume (perhaps ad hoc) that the relationship between moral salience and decision-making will be a linear one; that is, increased salience should lead to decreased moral violations and vise versa. While it makes sense that increase salience would lead to decreased moral violations, we‘re not as convinced that the inverse would be true (less salience would necessarily lead to more violations). To this end, we propose also a binary relationship that retains the high salience/low violations logic but also „frees up“ the low salience portion of our predictions; the binary model assumes observed violations to be random until biased by high moral salience. Both rival hypotheses are tested.
  • First, we looked at each moral module specifically, to see if increase salience of module „x“ will lead to less violations of module „x“; that is, we looked at the linear relationship between module salience and observed violations. In order for the linear relationship to be true, moral module salience should be a significant negative predictor of the probability of observed moral module violations. When looking at the assumed linear relationships, the results are not very convincing. Using a logistic regression, we only see support for our propoed lineary effect in three of our five modules, and even here the effect is sample-specific and at only at the ~80% probability range. Thus, we cannot conclude the presence of a linear effect; H1 is not supported. „ We tested H1 using logistic regressions with MFQ module salience as predictor and the decision to violate or not the corresponding module as dependent variable. Really, in none of the five modules and none of the four samples was salience a significant predictor.“
  • The first hypothesis was concerned with the linear effect, which was not found. However, this might not be surprising, because it might not make much sense that low module salience would be predictive of increased violations unilaterally. Instead, we think it might be a matter of observed violations becoming increasingly less random as moral module salience increases. To test our second (rivaling) hypotheses, that we cannot rely on logistic regression models because there is no linear effect. Instead, we need to make three assumptions that we can test empirically to examine a binary effect – that is, we might only see the relationship between moral salience and observed violations when morality is high. For this to be the case, we proposed three criterion:   If a moral module is salient among people, there should be significantly less violations than for a non salient module (a). For any binary decision (yes or no, violation or not) to be systematic, there should be a non-random distribution. In our case, a random distribution would be, half of the participants violated a module and half did not (expectancy value of .5). For a salient module, distribution should be different from random (b) for a non-salient module, the distribution should not be different from random.
  • “ In order to empirically test the – what we call binary decision model – as described before, we are confronted with the problem that we do not have a “cut off” value, deeming a moral module salient or not. But, theoretical arguments from MFT come to our help: Haidt and others often talk about the fact that moral module salience patterns are often related to one another. Thus, it seems a wise choice to determine the module, people had the highest salient score compared to all other modules as a “salient” module. On the other hand, if a module has the lowest score compared to all other modules, chances are high, that this module is not salient for this particular person.   Example: So, let’s say Peter (Vorderer!) had a score of 5 in Harm and care, 3 in Purity and 4 in the three other modules, we determined harm/and care to be his salient module and purity to be his non salient module. We then investigated how Peter behave in the Harm/Care scenario for the highest salience module and how he behaved in Purity for the lowest salience score.
  • We tested the assumptions for H2 (the binary effect) in all four samples. Recall that three requirements had to be met. For requirement A) we relied on t-tests to show us a significant difference in the salience of high and low modules; both requirement B) and C) required a binominal test against a random distribution of .5. For requirement B) we required a significant difference from a random distribution (p-level .05) and for requirement c) the distribution should not be different from a random distribution at the p < .2 level; a more conservative test because we are looking for similarities, not differences. Digital Natives: For German adolescents all three requirements were met. For US adolescents, we only met the requirement for C), as we can see a random distribution of violation for both high and low level salience. We do not yet have an explanation for this but we did find a methodological problem with the harm/care scenarios (in at least two experimental conditions, we witnessed 100% violations). We also saw that US adolescents had significantly higher presence scores, which might indicate that they approached the game as just that – a game. It might also be the case simply that the US adolescents didn’t see anything as particularly moral (scores were all around the scale For the US adolescents, we did not find a significant difference in the highest and lowest moral salience scores. THIS IS COMPELLING because it explains the other findings. After all, if there wasn't a most/lease moral salience scores AND presence scores were high, it appears (empirically) that they simply didn't see anything in the video game as moral!   Means for US adolescents were:   Harm/ care 4.47 (.99) Fairness/ reciprocity 4.36 (.91) Authority/ respect 4.27 (.95) In-group/ loyalty 4.33 (.94) Purity/ sanctity 3.97 (.99) AVERAGE MORALITY 4.28 (on a five-point scale)   So, they had rather high moral foundations overall, but the heightened presence in the game may have led them to override this morality and instead merely experiment in the game. IN OTHER WORD, heightened presence combined with the fact that these US kids made no distinction between low and high moral salience simply meant that they really didn’t activate morality at all when playing. They have no particularly important moral foundations AND they wanted to enjoy the game (and morally disengaged to do so).   Digital Immigrants: For the elderly, the first two requirements were met but the final wasn’t, as they showed a tendency to violate their least salient module. The US elderly followed predicted patterns, and violated significantly less than random on the p <.2 level (39% of violations). In the end, we see no support for the linear relationship, and we see compelling support for the binary („gut“ vs. „game“) relationship in three out of four samples
  • Some more findings: We found distinct patterns of morality across cultures, but in terms of age and nationality. The overall pattern of data suggest the binary approach to be correct, with some subtle differences particularly with the German elderly and the US adolescents
  • BEA 2011 "Gut or Game"

    1. 1. “Gut or Game”: Moral Intuitions and Virtual Environments Sven Jöckel, Dr. phil. Nicholas David Bowman, Ph.D. Leyla Dogruel ?? Paper presented at the BEA2011 Research Symposium “Media and Morality: Investigating the Connections” April 10, 2011 in Las Vegas, NV
    2. 2. “Gut or Game” • Series of studies in German and the US examining how morals guide (interactive) behaviors • Four data collections age (2) x culture (2) • Pattern of data suggest that morals do guide in-game decisions (“gut”) but only when cued; otherwise, behaviors are random (“game”) 10/11/13 (c) ND Bowman, 2011 2
    3. 3. How do these make you feel? 10/11/13 (c) ND Bowman, 2011 3
    4. 4. Moral Foundations Theory • Model of intuitive morality • Argues for a “first draft” of morality, edited by experience • Experiences differ between cultures* 10/11/13 (c) ND Bowman, 2011 4 Authority Loyalty Purity Harm/Care Fairness
    5. 5. Moral Foundations Theory 10/11/13 (c) ND Bowman, 2011 5 Authority Loyalty Purity Harm/Care Fairness
    6. 6.  Tabular rasa approach  “quandary ethics”  Focus on actions and scenarios  Cognitive (moral) reasoning  Morality constantly monitored Intuitive vs. Rational Morality 10/11/13 (c) ND Bowman, 2011 6 Intuitive Morality  Innate moral foundations  “evolutionary ethics”  Focus on culture and character  Moral dumbfounding  Morality considered on encounter Rational Morality
    7. 7. Morality and Technology 10/11/13 (c) ND Bowman, 2011 7 As we become increasingly mediated, we wonder how folks respond to said mediation. Are virtual environments real? Do we behave the same way in RL and SL?
    8. 8. Three Studies • Digital Natives and Decision-Making • Digital Immigrants and Decision-Making • Morality, Nationality, and Media Preference 10/11/13 (c) ND Bowman, 2011 8 Four samples •US •Children (n = 91, M = 12.84, 46% )♀ •Elderly (n = 62, M = 68.02, 79% )♀ •Germany •Children (n = 94, M = 13.11, 55% )♀ •Elderly (n = 54, M = 66.54, 57% )♀
    9. 9. Neverwinter Nights, modified 10/11/13 (c) ND Bowman, 2011 9
    10. 10. Digital Natives • Digital Natives = born into technology • Adjusted to social mores of Internet, gaming • Morality is “under (social) construction” 10/11/13 (c) ND Bowman, 2011 10 Germany US Salience % Violation Salience % Violation Harm/ care 4.44 (.98) 30% 4.47 (.99) 64% Fairness/ reciprocity 4.37 (.95) 12% 4.36 (.91) 24% Authority/ respect 3.89 (.96) 57% 4.27 (.95) 63% In-group/ loyalty 3.88 (.85) 38% 4.33 (.94) 48% Purity/ sanctity 3.61 (.92) 54% 3.97 (.99) 40%
    11. 11. Digital Immigrants • Digital Immigrants = still adopting technology • Still learning customs of technology • Well-established sense of mores and customs 10/11/13 (c) ND Bowman, 2011 11 Germany US Salience % Violation Salience % Violation Harm/ care 5.2 (.6) 25% 4.9 (.7) 10% Fairness/ reciprocity 5.0 (.6) 9% 4.7 (.6) 5% Authority/ respect 3.7 (.9) 72% 4.5 (.8) 34% In-group/ loyalty 4.1 (.8) 67% 4.3 (.9) 45% Purity/ sanctity 3.8 (1.0) 65% 4.0 (1.2) 32%
    12. 12. Impact of Morality on Decisions The linear relationship (H1) The binary relationship (H2) 10/11/13 (c) ND Bowman, 2011 12 OR
    13. 13. Logistic (Linear) Regression 10/11/13 (c) ND Bowman, 2011 13 Digital Natives Digital Immigrants German Adolescents American Adolescents American Elderly German Elderly Harm/ Care Ns Ns Ns (exp)B = 3.13 ((*)) Fairness/ Reciprocity Ns Ns Ns Ns Authority/ Respect Ns Ns Ns (exp)B = 0.56 ((*)) In-group/ Loyalty Ns Ns Ns Ns Purity/ Sanctity Ns (exp)B = 1.36 ((*)) Ns Ns *** = p <.001; ** = p <.01, * = p <.05 (*) = p <.1, ((*)) = p <. 2 , (ns) = p > .2
    14. 14. Binary Relationship (H2) • Expect to see fewest violations in the most important moral module • Three requirements for support a) Significantly less violations for salient modules than for not salient modules b) Non-random distributions of violations for salient modules c) Random distribution of violations in non-salient modules 10/11/13 (c) ND Bowman, 2011 14
    15. 15. What determines module salience? • Most salient moral module was determined as one which participants scored highest • Least salient moral module was determined as one which participants scored lowest • No set „cut-off“ number 10/11/13 (c) ND Bowman, 2011 15
    16. 16. Relative module importance 10/11/13 (c) ND Bowman, 2011 16 A) Sig. ∆ High vs. Low B) Non- random (highest salience) C) Random (lowest salience) Digital Natives German Adolescents Yes (.002) Yes (21%) Yes (47%) US Adolescents No (.118) No (54%) Yes (41%) Digital Immigrants German Elderly Yes (<.001) Yes (24%) No (77%) US Elderly Yes (<.001) Yes (12%) Yes* (39%)
    17. 17. Conclusions • Main findings: – If morality was high, no violation “gut” – If morality was low, violation was random “game” – Support for binary relationship, not linear one • What does it mean for digital media? – “Game” reaction is default, until “gut” is primed – Moral orientations learned in RL seem to drive decisions in the virtual world – + Presence might serve as a “moral override” 10/11/13 (c) ND Bowman, 2011 17
    18. 18. Conclusions and Future Directions • What does it mean for digital media? – “Game” reaction is default, until “gut” is primed – Moral orientations learned in RL seem to drive decisions in the virtual world – What we need to investigate in the future – Effects of moral decisions on enjoyment in interactive media – + Presence might serve as a “moral override” (still investigating…) 10/11/13 (c) ND Bowman, 2011 18
    19. 19. Questions? • Contact Nick Bowman Department of Communication Studies West Virginia University 108 Armstrong Hall Morgantown, WV 26506 nick@ndbowman.info 10/11/13 (c) ND Bowman, 2011 19

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