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Lennart Nacke




Next Generation Testing:
Biometric Analysis of Player Experience
About Me
   Blekinge Institute of Technology
       PhD Candidate
            Digital Game Development Degree


   EU FUGA (“Fun of Gaming”) project

   Fun & player experience research

   Biometrics consulting
Numbers vs. Interviews?
Quantitative vs. Qualitative?
Quantitative AND Qualitative!
Outline

1.    Traditional playtesting



2.    Next-gen playtesting
     a.   Now with more biometrics!



3.    Takeaway
Iterative feedback loop




  Game
                  Playtesting
  Design
Traditional Playtesting
Traditional Playtesting
   Quality assurance
     Technical quality
     Design quality



 Bug reports
 Balancing
 Qualitative approaches
     Classic playtest
     Focus groups
     Think-aloud
Technical quality checks
   Make sure game functions correctly

 Bug reports depend on skill of tester
 Sometimes automatic testing


   Balancing gameplay parameters
     Really more a design check
     Trial and error
     Time-consuming
Classic Playtest

   Watch someone play the game

   Check against design intentions
     Are game rules obeyed?
     Are game goals reached in proper way?
     Do testers report this as being “fun”?

   Followed by questioning (Q&A)

   Iterate design based on feedback
Focus groups
   Modification of classic playtests

   Target audience clusters

   Group sessions

   In-depth interviews after session
Think-aloud protocol
   Add-on for classic playtests

   Player comments playing aloud
       Recorded with microphones


   Spontaneous and unfiltered
       Insights into player reasoning
Benefits of traditional
playtesting
 Get a good idea of how players like
  your game
 Answer design questions
 Watch what triggers behavior
 Collect many subjective details
 Uncover hidden gameplay problems
 Interviews allow to investigate fine
  distinctions of gameplay
Limitations of traditional
playtesting
 Hard to generalize
 Lots of bias
    Observation/Memory
    Testers
    Questions
 Subjective interpretation of behavior
 Problems with accuracy
Why QA uses traditional
playtesting…
   Works great for finding major issues
     Interaction
     Gameplay
     Content
     Interface

 Uncover nuances in interviews
 Insights into players’ minds
       Answers to “WHY?” & “HOW?”
   Direct game design feedback
Why QA should think about
adding next-gen testing…
   Much bias in qualitative techniques
       Rooted in
          Analysis
          Recording

   Scientifically questionable
     Objectivity
     Reliability
     Replicability

   Empirical power
Next-Gen Playtesting
Next-gen playtesting
   Gameplay metrics
     Event-related/triggered
     Continuous logging
     Spatial



   Psychometric surveys

   Physiological player measurement
       BIOMETRICS!
Gameplay metrics
 Provide empricial insights into player
  behavior
 Usually event-based
       Player deaths for example
   Spatial data allow level design
    analysis
       Heatmaps
   Construction of Personas
Example of Game Metrics
Example of game metrics data (see also Tychsen & Canossa
2008)
Gameplay metrics
PRO                  CON
 Objective data      Implementation
 Quantifiable         for specific
 Identify trends
                       engine
                      Missing fine
 Measure play
  behavior             granularity
                      Need statistics
 Events allow
  correlation with     experts
  biometrics          Painstaking
                       analysis
Psychometric surveys
 Standard psychological profiles
 What motivates your players?
 Standard tools from psychology
     Psychotypes
     Meyers-Briggs Type Indicator
     EPQ-R Psychoticism
     BIS/BAS Behavior
     etc.

   Categorize your players
Psychometric surveys
PRO                CON
 Categorize        Scoring can be
  players            tricky
 Correlate with    Need statistical
  personas           knowledge
 Validated         Only fully
  method             valuable in
 Quantifiable       conjunction
 Reliable
                     with other
                     measures
Measurement tools
   Facial Electromyography (EMG)
       Emotion, Blinking
   Galvanic Skin Response (GSR)
       Excitement, Arousal, Engagement
   Electroencephalogram (EEG)
       Brainwaves, Cognition, Emotion, Attention
   Eye Tracking
       Visual attention, Blinking, Cognition
   Accelerometers
   Position and pressure sensors, etc.
EMG
   Measuring facial muscle activation
       Correlates to emotions


   Russel’s circumplex model of emotion
     Valence = Positive or Negative
     Arousal = High or Low

 Brow muscle = bad mood
 Smile and Eye muscle = good mood
Facial EMG response cumulative
            means for each level

       Flow Level


Immersion Level


 Boredom Level


                       0           5           10           15
       Smile (µV)          Brow (µV)          Eye (µV)


Objective results: Valence responses
Cumulative tests for different game level types (see also
Nacke, Lindley, 2008).
Correlation of Physiological Data to Events
Physiological data is recorded together with real-time game
events, allowing for automatic data clustering and analysis
GSR
 Electrodermal activity
 Eccrine sweat gland production
 Two electrodes (conductance)
 Correlates to arousal
 Easy deployment and measurement
 Signal can be noisy
 Allows emotion mapping together
  with EMG in circumplex model
Galvanic Skin Response
      Cumulative Means for each level

       Flow Level


Immersion Level


 Boredom Level


                    0.86     0.88     0.9     0.92 0.94


Objective results: Arousal responses
More excitement peaks for one level (see also Nacke, Lindley,
2008).
Aroused


                                   Surprise
             Fear

Unpleasant                             Happy




                                                    Pleasant
                   Anger
                             Neutral


               Disgust          Calmness

             Sad
                                   Sleepiness


                     Not aroused


Russel’s circumplex model of emotion
The two dimensions of this model can also be mapped to EMG
and GSR measurement (see also Lang 1995).
EEG
   Electrodes placed on scalp (from 20 to 256)
   Measures electric potentials
   Brainwaves are described in frequency bands
      Delta (trance, sleep)
      Theta (emotions, sensations)
      Alpha (calm, mental work)
      Low beta (focus, relaxed)
      Mid beta (thinking, alert)
      High beta (alert, agitated)
      Gamma, seldom (information processing)
Game experiment Setup
EEG and EMG electrodes are being attached. The Biosemi
electrode cap consists of 32 electrodes in the areas: frontal
(F), parietal (P), temporal (T), occipital (O), central (C).
EEG Frequencies and Spectrum
EEG Analysis is difficult. After artifact scoring, values have to
be transformed for spectral analysis.
Eye Tracking
   Measures what eyes look at
       Saccades (fast movement)
            Gaze path
       Fixations (dwell times)
            Attention focus
       Pupil dilation/blink rate
 Attention precedes gaze (200ms)
 Used mainly to improve interface
       Lack of 3D analysis tools
Experimental playing session
Experimental gaming session with all logging equipment in
place.
Example of 3D Eye Tracking Visualization
Viewed game world objects can be displayed together with
their gazepaths in 3D (see also Stellmach, 2009)
Physiological measures
PRO                 CON
 Objective          Expensive
 Covert &           Intrusive
  continuous         Difficult to
  recording           analyze
 Quantifiable       Time-
 Reliable            consuming
 Replicable
 Empirical power
 Automatization
Key biometric advantages
   Data is objective
       Not dependent on memory/language


   Continuous measurement
       During event processing


   Information on player responses
     Emotional
     Attentional/Cognitive
Biofeedback applications
 Use fuzzy models
 IEEE SIG: game.itu.dk/PSM
     Player satisfaction modeling
     Cognitive models
     Affective models

 Optimal challenge
 Trigger game events with
  biofeedback (e.g. Emotiv)
 Popular approaches
       GSR, heart-rate and respiration
The Takeaway
Takeaway
1.   Metrical testing is emerging
        Now is the best time to jump on!
2.   Your company needs user research
         Ultimately your players know best!
3.   Biometrics enhance classic testing
         Qualitative supports quantitative data
4.   Understand existing and emerging
     testing methods
         Keep in touch with experts
References
Lang, P.J. The emotion probe. Studies of motivation and attention.
   American Psychologist, 50. 372-385.
R.L. Mandryk (2008). Physiological Measures for Game
   Evaluation. in Game Usability: Advice from the Experts for
   Advancing the Player Experience. (K. Isbister and N. Shaffer,
   Eds.), Morgan Kaufmann.
Nacke, L. and Lindley, C.A., Flow and Immersion in First-Person
   Shooters: Measuring the player’s gameplay experience. In
   Proceedings of the 2008 Conference on Future Play: Research,
   Play, Share, (Toronto, Canada, 2008), ACM, 81-88.
Tychsen, A. and Canossa, A., Defining personas in games using
    metrics. In 2008 Conference on Future Play: Research, Play,
    Share, (Toronto, Ontario, Canada, 2008), ACM, 73-80.
Russell, J.A. A Circumplex Model of Affect. Journal of Personality
    and Social Psychology, 39 (6). 1161-1178.
Stellmach (2009). Visual Analysis of Eye Gaze Data in Virtual
    Environments. Master’s Thesis.
Icons from smashingmagazine.com
More at Future Play!
Tomorrow, 1pm, Room 206

PANEL: Game Metrics and Biometrics:
 The Future of Player Experience
 Research

Featuring Mike Ambinder, Regan
  Mandryk, Alessandro Canossa, Tad
  Stach, and me
Contact Me
    Lennart.Nacke@bth.se
    gamescience.bth.se
    www.acagamic.com
Connect at www.linkedin.com/in/nacke
Blekinge Institute of Technology
Box 214
SE-374 24 Karlshamn
Sweden

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Next Generation Testing: Biometric Analysis of Player Experience

  • 1. Lennart Nacke Next Generation Testing: Biometric Analysis of Player Experience
  • 2. About Me  Blekinge Institute of Technology  PhD Candidate  Digital Game Development Degree  EU FUGA (“Fun of Gaming”) project  Fun & player experience research  Biometrics consulting
  • 6. Outline 1. Traditional playtesting 2. Next-gen playtesting a. Now with more biometrics! 3. Takeaway
  • 7. Iterative feedback loop Game Playtesting Design
  • 9. Traditional Playtesting  Quality assurance  Technical quality  Design quality  Bug reports  Balancing  Qualitative approaches  Classic playtest  Focus groups  Think-aloud
  • 10. Technical quality checks  Make sure game functions correctly  Bug reports depend on skill of tester  Sometimes automatic testing  Balancing gameplay parameters  Really more a design check  Trial and error  Time-consuming
  • 11. Classic Playtest  Watch someone play the game  Check against design intentions  Are game rules obeyed?  Are game goals reached in proper way?  Do testers report this as being “fun”?  Followed by questioning (Q&A)  Iterate design based on feedback
  • 12. Focus groups  Modification of classic playtests  Target audience clusters  Group sessions  In-depth interviews after session
  • 13. Think-aloud protocol  Add-on for classic playtests  Player comments playing aloud  Recorded with microphones  Spontaneous and unfiltered  Insights into player reasoning
  • 14. Benefits of traditional playtesting  Get a good idea of how players like your game  Answer design questions  Watch what triggers behavior  Collect many subjective details  Uncover hidden gameplay problems  Interviews allow to investigate fine distinctions of gameplay
  • 15. Limitations of traditional playtesting  Hard to generalize  Lots of bias  Observation/Memory  Testers  Questions  Subjective interpretation of behavior  Problems with accuracy
  • 16. Why QA uses traditional playtesting…  Works great for finding major issues  Interaction  Gameplay  Content  Interface  Uncover nuances in interviews  Insights into players’ minds  Answers to “WHY?” & “HOW?”  Direct game design feedback
  • 17. Why QA should think about adding next-gen testing…  Much bias in qualitative techniques  Rooted in  Analysis  Recording  Scientifically questionable  Objectivity  Reliability  Replicability  Empirical power
  • 19. Next-gen playtesting  Gameplay metrics  Event-related/triggered  Continuous logging  Spatial  Psychometric surveys  Physiological player measurement  BIOMETRICS!
  • 20. Gameplay metrics  Provide empricial insights into player behavior  Usually event-based  Player deaths for example  Spatial data allow level design analysis  Heatmaps  Construction of Personas
  • 21. Example of Game Metrics Example of game metrics data (see also Tychsen & Canossa 2008)
  • 22. Gameplay metrics PRO CON  Objective data  Implementation  Quantifiable for specific  Identify trends engine  Missing fine  Measure play behavior granularity  Need statistics  Events allow correlation with experts biometrics  Painstaking analysis
  • 23. Psychometric surveys  Standard psychological profiles  What motivates your players?  Standard tools from psychology  Psychotypes  Meyers-Briggs Type Indicator  EPQ-R Psychoticism  BIS/BAS Behavior  etc.  Categorize your players
  • 24. Psychometric surveys PRO CON  Categorize  Scoring can be players tricky  Correlate with  Need statistical personas knowledge  Validated  Only fully method valuable in  Quantifiable conjunction  Reliable with other measures
  • 25. Measurement tools  Facial Electromyography (EMG)  Emotion, Blinking  Galvanic Skin Response (GSR)  Excitement, Arousal, Engagement  Electroencephalogram (EEG)  Brainwaves, Cognition, Emotion, Attention  Eye Tracking  Visual attention, Blinking, Cognition  Accelerometers  Position and pressure sensors, etc.
  • 26. EMG  Measuring facial muscle activation  Correlates to emotions  Russel’s circumplex model of emotion  Valence = Positive or Negative  Arousal = High or Low  Brow muscle = bad mood  Smile and Eye muscle = good mood
  • 27. Facial EMG response cumulative means for each level Flow Level Immersion Level Boredom Level 0 5 10 15 Smile (µV) Brow (µV) Eye (µV) Objective results: Valence responses Cumulative tests for different game level types (see also Nacke, Lindley, 2008).
  • 28. Correlation of Physiological Data to Events Physiological data is recorded together with real-time game events, allowing for automatic data clustering and analysis
  • 29. GSR  Electrodermal activity  Eccrine sweat gland production  Two electrodes (conductance)  Correlates to arousal  Easy deployment and measurement  Signal can be noisy  Allows emotion mapping together with EMG in circumplex model
  • 30. Galvanic Skin Response Cumulative Means for each level Flow Level Immersion Level Boredom Level 0.86 0.88 0.9 0.92 0.94 Objective results: Arousal responses More excitement peaks for one level (see also Nacke, Lindley, 2008).
  • 31. Aroused Surprise Fear Unpleasant Happy Pleasant Anger Neutral Disgust Calmness Sad Sleepiness Not aroused Russel’s circumplex model of emotion The two dimensions of this model can also be mapped to EMG and GSR measurement (see also Lang 1995).
  • 32. EEG  Electrodes placed on scalp (from 20 to 256)  Measures electric potentials  Brainwaves are described in frequency bands  Delta (trance, sleep)  Theta (emotions, sensations)  Alpha (calm, mental work)  Low beta (focus, relaxed)  Mid beta (thinking, alert)  High beta (alert, agitated)  Gamma, seldom (information processing)
  • 33. Game experiment Setup EEG and EMG electrodes are being attached. The Biosemi electrode cap consists of 32 electrodes in the areas: frontal (F), parietal (P), temporal (T), occipital (O), central (C).
  • 34. EEG Frequencies and Spectrum EEG Analysis is difficult. After artifact scoring, values have to be transformed for spectral analysis.
  • 35. Eye Tracking  Measures what eyes look at  Saccades (fast movement)  Gaze path  Fixations (dwell times)  Attention focus  Pupil dilation/blink rate  Attention precedes gaze (200ms)  Used mainly to improve interface  Lack of 3D analysis tools
  • 36. Experimental playing session Experimental gaming session with all logging equipment in place.
  • 37. Example of 3D Eye Tracking Visualization Viewed game world objects can be displayed together with their gazepaths in 3D (see also Stellmach, 2009)
  • 38. Physiological measures PRO CON  Objective  Expensive  Covert &  Intrusive continuous  Difficult to recording analyze  Quantifiable  Time-  Reliable consuming  Replicable  Empirical power  Automatization
  • 39. Key biometric advantages  Data is objective  Not dependent on memory/language  Continuous measurement  During event processing  Information on player responses  Emotional  Attentional/Cognitive
  • 40. Biofeedback applications  Use fuzzy models  IEEE SIG: game.itu.dk/PSM  Player satisfaction modeling  Cognitive models  Affective models  Optimal challenge  Trigger game events with biofeedback (e.g. Emotiv)  Popular approaches  GSR, heart-rate and respiration
  • 42. Takeaway 1. Metrical testing is emerging  Now is the best time to jump on! 2. Your company needs user research  Ultimately your players know best! 3. Biometrics enhance classic testing  Qualitative supports quantitative data 4. Understand existing and emerging testing methods  Keep in touch with experts
  • 43. References Lang, P.J. The emotion probe. Studies of motivation and attention. American Psychologist, 50. 372-385. R.L. Mandryk (2008). Physiological Measures for Game Evaluation. in Game Usability: Advice from the Experts for Advancing the Player Experience. (K. Isbister and N. Shaffer, Eds.), Morgan Kaufmann. Nacke, L. and Lindley, C.A., Flow and Immersion in First-Person Shooters: Measuring the player’s gameplay experience. In Proceedings of the 2008 Conference on Future Play: Research, Play, Share, (Toronto, Canada, 2008), ACM, 81-88. Tychsen, A. and Canossa, A., Defining personas in games using metrics. In 2008 Conference on Future Play: Research, Play, Share, (Toronto, Ontario, Canada, 2008), ACM, 73-80. Russell, J.A. A Circumplex Model of Affect. Journal of Personality and Social Psychology, 39 (6). 1161-1178. Stellmach (2009). Visual Analysis of Eye Gaze Data in Virtual Environments. Master’s Thesis. Icons from smashingmagazine.com
  • 44. More at Future Play! Tomorrow, 1pm, Room 206 PANEL: Game Metrics and Biometrics: The Future of Player Experience Research Featuring Mike Ambinder, Regan Mandryk, Alessandro Canossa, Tad Stach, and me
  • 45. Contact Me Lennart.Nacke@bth.se gamescience.bth.se www.acagamic.com Connect at www.linkedin.com/in/nacke Blekinge Institute of Technology Box 214 SE-374 24 Karlshamn Sweden