Why research video games?

a) they involve millions of people

b) also for non-game research

• genuine teams for research on team processes
  • ​different levels of 'teamedness'
• intrinsically motivated experts in their native niche for computerized testing 
• quantified level of expertise (rankings/ratings)
• easily accessible for web-based experiments

c)  e-sports psychology

• professional gaming is a billion dollar business​​
• LoL - $8.000.000 price money/season plus yearly salary
  • rigorous training regimes, competitions, sponsorship...
  • pro gamers can benefit from psychological techniques 

League of Legends

• Competitive free to play PvP MOBA / action real-time strategy (ARTS)
• 67 million monthly active players, 27 million active daily

How does it work?

• From level 1 to 30 with normal games
  • 'unranked' games against other players or AI
  • award XP and IP
• Level 30 and access to 16 champions -> ranked games
  • solo, 3v3, 5v5
  • first 10 games for placement
    • MMR used to match with similar opponents

1. Two teams, 3 or 5 players each
2. 1 "champion" per player
3. different roles:

• carry
• support
• tank
• bruiser
• jungler

4.  kill towers, minions, monsters (jungle) and enemy champions to collect resources

5.  overpower the enemy and destroy their nexus (base)

League System

Sampling

Online forums and communities:

• MOBA fire
• Riot Gaming boards and forums
• Reddit League of Legends community
• League of Legends streamers on Twitch.tv
• Twitter
• Personal contact with players
• An e-sports community in Singapore
• No response from Riot

Participants

• N = 278, eligible N = 218, finished N = 146

• between ages 15 and 45 (M = 21.42, SD = 4.15)

Geographics by game regions:

• North America (N=143)

• EU-West (N=94)

• EU-North and East (N=21)

• Latin America (N=7)

• New Zealand (N=8)

• Turkey (N=2)

Play style (ranked):

• solo queue N=206
• 5v5 N= 51
• 3v3 N=18

Structure in the GEQ

• R - psych (Revelle, 2014)
• PCA, Promax rotation, r = 0.36.
• PC1 = task-oriented, a = 0.83
• PC2 = social, a = 0.79
• Distribution of the scores is not great

PCA for cognitive tasks

• R - psych (Revelle, 2014)
• 4 - component varimax
• Scoring with regression
• No missing value imputation

Spearman's correlations, solo

Spearman correlations, team

Subtitle

Regression...

Note - no imputation!

• Step-wise analysis with stepAIC function in MASS R package (Venables & Ripley, 2002)
• Best functioning models:
  • Solo rating: 
    • Flanker accuracy, planning and frustration:
      • R^2 =.16, F(3,57)=3.52, p<.05); 
        • b(RC3) = 0.22, p = 0.08
        • b(frustration) = -0.24, p = 0.06
        • b(RC4) = 0.18, p < 0.05
  • Team rating: 
    • Impossible models with TLX
    • 5v5 rating ~ task load + mental rotation:
      • R^2 = 1. , F(2,20)= 2.464e+3, p <.0001
        • b(task load) = 0.99, p < 0.0001
        • b(RC2) = 0.05, p < 0.0001
          • Similar model with RC3 and RC2
          • TLX is overpowering but non-significant on its own?

Conclusion

1. TLX works remarkably well - effort/frustration
2. Spatial span works remarkably unwell
3. Participant motivation and response styles are a huge challenge

As to the intentions...

1. Do cognitve skills and team cohesion relate to performance in League of Legends?

• Cognitive tasks appear important, but better test selection is needed
• Check out communication style instead of team cohesion, try different conative factors 

2. Which cognitive skills are relevant to gameplay in MOBAs ( + usability of GEQ and NASA TLX for game studies)

• Explore other cognitive skills - 3D rotation, task switching, ToL with CAT, go/no go
• GEQ somewhat lacks discriminativeness
• NASA TLX works great

3. Get experience building JavaScript-based web experiments ( + contribute something to jsPsych)

• Yes. Working on the plugins.

4. Gauge the usability of web-based methods and data for cognitive psychology game studies

• Yes - with care and certain reservations

• ... and more!
  •  invalid response times
  •  bugs (and poor bug reports)
  •  different hardware
  •  mobile access (!!)
  •  confounding environmental variables
  •  batteries are longer and strenuous than in survey research -> question of motivation

Question of motivation

• Bugs and poor usability:
  • drop outs
  • bad data
• Information and feedback
• Use adaptive testing
• Try some gamification
• Implement motivating features
  • login systems
  • data from API
  • spreadsheets with feedback
  • ...

Unreliable/malicious use?

• use an ORM
• aggregate AFTER cleaning up invalid trials
• discard data from 'non-serious' participants
• social network login, data from APIs
  • more reliable data
    • and more of it
  • less work for participants
  • reduces number of non-serious participants
  • anonymity?

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