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Nacke, L., Ambinder, M., Canossa, A., Mandryk, R., Stach, T. (2009). quot;Game Metrics and
Biometrics: The Future of Player Experience Research“ Panel at Future Play 2009.
1.   Introduction of Panelists


2.   Methods Overview


3.   Discussion and Questions
1.   Mike Ambinder

2.   Alessandro Canossa

3.   Regan Mandryk

4.   Tad Stach

5.   Lennart Nacke
   User Experience Designer
       Valve Corporation


   PhD in Experimental Psychology

   Application of knowledge and
    methodologies from psychology to
    game design
3 years experience at EIDOS
      Play pattern modelling techniques (Hitman Blood
       Money, Kane & Lynch and Tomb Raider Underworld)
Play-Persona framework
      Tool used in design phase to integrate different
       players‘ needs and motivations
      Backed by game metrics
      Tool used to evaluate experience
Speaking at
      Nordic Game (Sweden)
      NLGD (Holland)
      DGExpo (North Carolina, USA)
      Future Play (Canada)
      DIGRA (Japan)
   User engagement in games
       Sensing and modeling
   Interaction techniques
       Emerging devices
   Assistant Professor
       Computer Science
   University of Saskatchewan
       Canada
www.reganmandryk.com
 PhD student
 Computer science
       Queen‘s University


   Exercise video games

   Heuristics and Usability
   Blekinge Institute of Technology
       PhD Candidate
           Digital Game Development Degree


   EU FUGA (―Fun of Gaming‖)
    project

 Fun & player experience research
 Biometrics consulting
1.   Mike: Direct Observation, Q&A,
     Verbal Reports, Surveys
2.   Alessandro: In-game metrics,
     GIS/Heatmaps, Play-Personas
3.   Regan: EMG, Skin Conductance,
     Heart-Rate
4.   Tad: Heuristics, Usability
     evaluations
5.   Lennart: EEG, Eye Tracking
   ―Typical‖ playtest
     Watch people play the game
     Observe their gameplay/behavior
     Simulate at-home experience

 Have a design goal
 Is it fun?
PRO                CON

+ Get a feel for   – Presence of
 player              observers can
 interaction         bias results
 with game         – Salient event
+ Importance of      can slant
 what people         interpretation
 do—not what       – Behavior
 they say            requires
                     interpretation
   Think-aloud protocol:
       People describe their actions as they
        play

       Unprompted and uncorrected

   In conjunction with direct
    observation
PRO                 CON

+ Enables realtime – Interferes with
 glimpse into        gameplay
 player thoughts, – Creates an
 feelings, and       artificial
 motivations         experience
+ Bring up         – Inaccurate
 unnoticed details   and biased
+ Effective for
 ‗why‘ questions
   Structured (usually) querying of
    playtesters

   Validate playtest goals

   Source of supplemental
    information
PRO                 CON

+ Answer           – Group biases
 specific design     (anchoring,
 questions           social pressure,
+ Determine          saliency, etc.)
 specific player   – People don‘t
 intent              know why they
                     do what they do
                   – Potential for
                     biased questions
   Set of standardized questions

   Forced choice responses

   Quantify feedback/opinions

   Player categorization
PRO               CON

+ Less biased     – Eliminate
 responses          nuance
+ Response        – Difficulty in
 validation         converting
+ Forced choice     ratings to
 helpful for        meaningful
 revealing          decisions
 preference       – Limited
+ Time-based        solution space
 comparisons
How challenging were the following enemies (1 = very easy; 7 = very hard)?

Boomer:                   1     2     3     4     5      6     7
Common Infected:          1     2     3     4     5      6     7
Hunter:                   1     2     3     4     5      6     7
Smoker:                   1     2     3     4     5      6     7
Tank:                     1     2     3     4     5      6     7
Witch:                    1     2     3     4     5      6     7

Please rank order your preference for the following weapons from
1 (most liked) to 12 (least liked)

Assault Rifle      ____
Auto Shotgun       ____
Dual Pistols       ____
Gas Can            ____
Hunting Rifle      ____
Molotov Cocktail   ____
Mounted Turret     ____
Pipe Bomb          ____
Pistol             ____
Propane Tank       ____
Pump Shotgun       ____
SMG                ____                                               Simulated Questions
Gameplay metrics = Player behavior
 Numerical data from game software about
  player behavior
Types:
     Continuous / Frequency / Triggered
     Spatial / Non-spatial
PRO            CON


Answers to     No answers to

     What?         Why?

     Where?        How?

     When?
Generated by a cluster visualization tool (shows
data from Fragile Alliance, it relates role at death
with cause of death)
GIS are computerized data management
systems used to capture, store,
manage, retrieve, analyze, and display
information with spatial dimension.
PRO                  CON

 Flexible           Overkill
 Off-the-shelf
                      for simple, non-
                        spatial
 Cheaper               analyses
 Minimal          Not integrated
  customization     with game
  needed            engine
                   Limited 3D
                    representation
HoD requests have been plotted and a
density kernel calculated into a heatmap
to visualize the distribution of areas with
high and low intensities of requests
Plotted deaths divided per sub-sector
aggregate descriptions of possible player behaviour:
                 a-priori metaphor (hypotheses):
    theoretical models of ideal users, expectations of the
                                                 designer




   a-posteriori lens (analyses): data-driven
    representations of player behaviors, descriptions
    of what actual, real players do during play
   Pre/Production
       Envision different play experiences
   After Launch
       Evaluation of experiences



         Hypothesising and analysing what players
    repeatedly do, sheds light on what their goals,
intentions and desires are at a precise moment in
          time and in a precise context: the game.
PRO                       CON

   Experiences              Risk of truisms
    easier to
       Design               No detection of
       Analyse
                                 problems
   Focus                         unrelated to
       Play experience           patterns of play
       Player behavior
   Map playing              Not useful for
    landscape                 usability issues
   Provide varied
    experience
Mapping the possibility space with play-
personas
Persona hypotheses emerge as relations
between parameters that have been
derived from gameplay mechanics.
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.

Canossa, A. and Drachen, A., Play-Personas: Behaviours
  and Belief systems in User-Centred Game Design.
  Interact Conference 2009. Uppsala, Sweden.

Tychsen, A. and Canossa, A., Analyzing User Behavior via
   Gameplay Metrics. Future Play 2009.
   Measured on palms and soles of
    feet- Eccrine sweat glands

   Measures electrical resistance (or
    conductance) between two
    electrodes

   Correlate to psychological arousal
PRO                 CON

 Easy to            Noisy signal
  measure            Large
 Inexpensive         individual
  hardware            variations in
 Easy to             baseline and
  interpret           responsivity
 Non-intrusive      Slow decay
  (could be built     (signals add
  into a device)      together)
Three instances of GSR when a goal was scored in NHL 2003
  – twice against the computer and once against a friend
R




                                                  QRS
                                                Complex
                                   P wave                  T wave
                                            Q
                                                       S




 Electrocardiography (EKG)
 Heart Rate (HR)
 Interbeat Interval (IBI)
 Heart Rate Variability (HRV)
     Spectral analysis of sinus arrhythmia
     Indicative of mental effort, cognitive
      load
 Blood Volume Pulse (BVP)
  (periodic)
 Blood Pressure (BP)
R




                                         QRS
                                       Complex
                          P wave                  T wave
                                   Q
                                              S

      PRO               CON

 Easy to          Intrusive to
  measure some      measure
  signals (HR)      accurately
 Inexpensive      Affected by
  hardware (HR)     many things
 Salient and       (e.g., physical
  established       activity)
  measures         Complex
                    analysis (HRV)
 Isometric tension, or detection of
  motion
 Needles or surface electrodes
 Tension in the jaw
 Forehead (smiling vs. frowning)
 Can be used on any muscles
PRO                 CON

 Analysis of       Intrusive to
  signals easy       measure
 More sensitive    Difficult to get
  than image         natural
  processing for     measures
  facial            Hardware is
  expressions        expensive
 Easy to           Interference of
  interpret          muscle groups
Intrusiveness of sensors is clear, but participants forgot about them
after a short time
a)

b)

c)

d)

e)

f)

g)

h)
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.
R.L. Mandryk, and M.S. Atkins (2007). A Fuzzy Physiological
    Approach for Continuously Modeling Emotion During Interaction
    with Play Environments. International Journal of Human-
    Computer Studies, 6(4), pg. 329-347. The original publication is
    available at Elsevier Online.
R.L. Mandryk, K.M. Inkpen, and T.W. Calvert (2006). Using
    Psychophysiological Techniques to Measure User Experience with
    Entertainment Technologies. Behaviour and Information
    Technology (Special Issue on User Experience), Vol. 25, No.2,
    March-April 2006, pg. 141-158.
R.L. Mandryk, M.S. Atkins, and K.M. Inkpen (2006). A Continuous
    and Objective Evaluation of Emotional Experience with
    Interactive Play Environments. in Proceedings of the Conference
    on Human Factors in Computing Systems (CHI 2006). Montreal,
    Canada, April 2006, pg. 1027-1036.
R.L. Mandryk (2008). A physiological approach for continuously
    modeling user emotion in interactive play environments. in Proc
    of Measuring Behavior 2008, Maastricht, NE, August 2008, pg.
    93-94.
   Few formal methods exist for evaluating
    the usability of game interfaces

   Developed usability principles for video
    game design

   Heuristics can be used to carry out
    usability inspections of video games
   Step 1: identify problems from game
    reviews
       108 reviews from GameSpot
       6 major PC game genres


   Step 2: develop problems categories
       12 common categories found


   Step 3: develop game heuristics
       10 heuristics created from problem categories
1. Provide consistent responses to user‘s actions

2. Allow users to customize video and audio settings, difficulty and game
   speed
3. Provide predictable and reasonable behaviour for computer controlled
   units
4. Provide unobstructed views that are appropriate for the user‘s current
   actions
5. Allow users to skip non-playable and frequently repeated content

6. Provide intuitive and customizable input mappings

7. Provide controls that are easy to manage, and that have an
   appropriate level of sensitivity and responsiveness
8. Provide users with information on game status

9. Provide instructions, training, and help

10. Provide visual representations that are easy to interpret and that
    minimize the need for micromanagement
PRO                    CON

   help identifying      does not address
    game-specific          engagement and
    usability              ―playability‖
    problems
                          limitations in the
   applicable to          development of
    mockups and            heuristics
    prototypes
   can be used to
    evaluate most
    games
   Pinelle, D., Wong, N., Stach, T., Gutwin, C.
    (2009) Usability Heuristics for Networked
    Multiplayer Games. To appear in GROUP 2009.

   Pinelle, D., Wong, N., Stach, T. (2008) Using
    Genres to Customize Usability Evaluations of
    Video Games. Future Play 2008, 129-136.

   Pinelle, D., Wong, N., Stach, T. (2008) Heuristic
    Evaluation for Games: Usability Principles for
    Video Game Design. CHI 2008, 1453-1462.
   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)
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 Analysis is difficult. After artifact scoring, values have to be
transformed for spectral analysis.
PRO             CON

 Objective       Intrusive
 Covert &        Expensive
  continuous      Artifact
  recording        scoring
 Quantifiable    Time-
 Reliable         consuming
 Replicable      Sometimes
 Empirical        hard to
  power            interpret
   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 gaming session with all logging equipment in place.
Viewed game world objects can be displayed together with their
gazepaths in 3D (see also Stellmach, 2009)
PRO              CON

 Easy to use     Can be
 Objective        expensive
 Covert          Lack of good

 Continuous
                   tools
                  Time-
 Quantifiable
                   consuming
 Replicable
 Empirical
  power
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.
Grimshaw, M., Lindley, C. A., & Nacke, L. (2008). Sound and
   Immersion in the First-Person Shooter: Mixed Measurement
   of the Player‘s Sonic Experience. Audio Mostly Conference
   2008, Piteå, Sweden.
Nacke, L., Lindley, C., and Stellmach, S. (2008) Log Who‘s
   Playing: Psychophysiological Game Analysis Made Easy
   through Event Logging. In Proceedings of the 2nd
   international Conference on Fun and Games (Eindhoven, The
   Netherlands, October 20 - 21, 2008). P. Markopoulos, B.
   Ruyter, W. Ijsselsteijn, and D. Rowland, Eds. Lecture Notes
   In Computer Science, vol. 5294. Springer-Verlag, Berlin,
   Heidelberg, 150-157.
Stellmach (2009). Visual Analysis of Eye Gaze Data in Virtual
   Environments. Master‘s Thesis. University of Magdeburg.
 Is an integration of the presented
  methods feasible?
 Can they be integrated in a cost-
  efficient way?
 Which methods are suitable for
  evaluating which parts of game
  development?
 Can empirical data be applied to
  game design? How?
 Should there be a discussion about
  separating quantitative from
  qualitative or do we agree on
  integrated measures?
 What can these methods be used for
  beyond evaluation? Exergames?
  Biofeedback?
 Are those methods improving games?
  If yes, how can (or should) they be
  adopted by the majority of the game
  industry?
   Audience
   Mike: www.valvesoftware.com

   Alessandro: www.dkds.dk

   Regan: www.reganmandryk.com

   Tad: equis.cs.queensu.ca

   Lennart: www.acagamic.com
       project.hkkk.fi/fuga/
Game Metrics and Biometrics: The Future of Player Experience Research

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Game Metrics and Biometrics: The Future of Player Experience Research

  • 1. Nacke, L., Ambinder, M., Canossa, A., Mandryk, R., Stach, T. (2009). quot;Game Metrics and Biometrics: The Future of Player Experience Research“ Panel at Future Play 2009.
  • 2. 1. Introduction of Panelists 2. Methods Overview 3. Discussion and Questions
  • 3. 1. Mike Ambinder 2. Alessandro Canossa 3. Regan Mandryk 4. Tad Stach 5. Lennart Nacke
  • 4. User Experience Designer  Valve Corporation  PhD in Experimental Psychology  Application of knowledge and methodologies from psychology to game design
  • 5. 3 years experience at EIDOS  Play pattern modelling techniques (Hitman Blood Money, Kane & Lynch and Tomb Raider Underworld) Play-Persona framework  Tool used in design phase to integrate different players‘ needs and motivations  Backed by game metrics  Tool used to evaluate experience Speaking at  Nordic Game (Sweden)  NLGD (Holland)  DGExpo (North Carolina, USA)  Future Play (Canada)  DIGRA (Japan)
  • 6. User engagement in games  Sensing and modeling  Interaction techniques  Emerging devices  Assistant Professor  Computer Science  University of Saskatchewan  Canada www.reganmandryk.com
  • 7.  PhD student  Computer science  Queen‘s University  Exercise video games  Heuristics and Usability
  • 8. Blekinge Institute of Technology  PhD Candidate  Digital Game Development Degree  EU FUGA (―Fun of Gaming‖) project  Fun & player experience research  Biometrics consulting
  • 9. 1. Mike: Direct Observation, Q&A, Verbal Reports, Surveys 2. Alessandro: In-game metrics, GIS/Heatmaps, Play-Personas 3. Regan: EMG, Skin Conductance, Heart-Rate 4. Tad: Heuristics, Usability evaluations 5. Lennart: EEG, Eye Tracking
  • 10. ―Typical‖ playtest  Watch people play the game  Observe their gameplay/behavior  Simulate at-home experience  Have a design goal  Is it fun?
  • 11. PRO CON + Get a feel for – Presence of player observers can interaction bias results with game – Salient event + Importance of can slant what people interpretation do—not what – Behavior they say requires interpretation
  • 12.
  • 13.
  • 14. Think-aloud protocol:  People describe their actions as they play  Unprompted and uncorrected  In conjunction with direct observation
  • 15. PRO CON + Enables realtime – Interferes with glimpse into gameplay player thoughts, – Creates an feelings, and artificial motivations experience + Bring up – Inaccurate unnoticed details and biased + Effective for ‗why‘ questions
  • 16.
  • 17. Structured (usually) querying of playtesters  Validate playtest goals  Source of supplemental information
  • 18. PRO CON + Answer – Group biases specific design (anchoring, questions social pressure, + Determine saliency, etc.) specific player – People don‘t intent know why they do what they do – Potential for biased questions
  • 19.
  • 20. Set of standardized questions  Forced choice responses  Quantify feedback/opinions  Player categorization
  • 21. PRO CON + Less biased – Eliminate responses nuance + Response – Difficulty in validation converting + Forced choice ratings to helpful for meaningful revealing decisions preference – Limited + Time-based solution space comparisons
  • 22. How challenging were the following enemies (1 = very easy; 7 = very hard)? Boomer: 1 2 3 4 5 6 7 Common Infected: 1 2 3 4 5 6 7 Hunter: 1 2 3 4 5 6 7 Smoker: 1 2 3 4 5 6 7 Tank: 1 2 3 4 5 6 7 Witch: 1 2 3 4 5 6 7 Please rank order your preference for the following weapons from 1 (most liked) to 12 (least liked) Assault Rifle ____ Auto Shotgun ____ Dual Pistols ____ Gas Can ____ Hunting Rifle ____ Molotov Cocktail ____ Mounted Turret ____ Pipe Bomb ____ Pistol ____ Propane Tank ____ Pump Shotgun ____ SMG ____ Simulated Questions
  • 23. Gameplay metrics = Player behavior  Numerical data from game software about player behavior Types:  Continuous / Frequency / Triggered  Spatial / Non-spatial
  • 24. PRO CON Answers to No answers to  What?  Why?  Where?  How?  When?
  • 25. Generated by a cluster visualization tool (shows data from Fragile Alliance, it relates role at death with cause of death)
  • 26. GIS are computerized data management systems used to capture, store, manage, retrieve, analyze, and display information with spatial dimension.
  • 27. PRO CON  Flexible  Overkill  Off-the-shelf for simple, non- spatial  Cheaper analyses  Minimal  Not integrated customization with game needed engine  Limited 3D representation
  • 28. HoD requests have been plotted and a density kernel calculated into a heatmap to visualize the distribution of areas with high and low intensities of requests
  • 29. Plotted deaths divided per sub-sector
  • 30. aggregate descriptions of possible player behaviour:  a-priori metaphor (hypotheses): theoretical models of ideal users, expectations of the designer  a-posteriori lens (analyses): data-driven representations of player behaviors, descriptions of what actual, real players do during play
  • 31. Pre/Production  Envision different play experiences  After Launch  Evaluation of experiences Hypothesising and analysing what players repeatedly do, sheds light on what their goals, intentions and desires are at a precise moment in time and in a precise context: the game.
  • 32. PRO CON  Experiences  Risk of truisms easier to  Design  No detection of  Analyse  problems  Focus unrelated to  Play experience patterns of play  Player behavior  Map playing  Not useful for landscape usability issues  Provide varied experience
  • 33. Mapping the possibility space with play- personas
  • 34. Persona hypotheses emerge as relations between parameters that have been derived from gameplay mechanics.
  • 35. 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. Canossa, A. and Drachen, A., Play-Personas: Behaviours and Belief systems in User-Centred Game Design. Interact Conference 2009. Uppsala, Sweden. Tychsen, A. and Canossa, A., Analyzing User Behavior via Gameplay Metrics. Future Play 2009.
  • 36. Measured on palms and soles of feet- Eccrine sweat glands  Measures electrical resistance (or conductance) between two electrodes  Correlate to psychological arousal
  • 37. PRO CON  Easy to  Noisy signal measure  Large  Inexpensive individual hardware variations in  Easy to baseline and interpret responsivity  Non-intrusive  Slow decay (could be built (signals add into a device) together)
  • 38. Three instances of GSR when a goal was scored in NHL 2003 – twice against the computer and once against a friend
  • 39. R QRS Complex P wave T wave Q S  Electrocardiography (EKG)  Heart Rate (HR)  Interbeat Interval (IBI)  Heart Rate Variability (HRV)  Spectral analysis of sinus arrhythmia  Indicative of mental effort, cognitive load  Blood Volume Pulse (BVP) (periodic)  Blood Pressure (BP)
  • 40. R QRS Complex P wave T wave Q S PRO CON  Easy to  Intrusive to measure some measure signals (HR) accurately  Inexpensive  Affected by hardware (HR) many things  Salient and (e.g., physical established activity) measures  Complex analysis (HRV)
  • 41.  Isometric tension, or detection of motion  Needles or surface electrodes  Tension in the jaw  Forehead (smiling vs. frowning)  Can be used on any muscles
  • 42. PRO CON  Analysis of  Intrusive to signals easy measure  More sensitive  Difficult to get than image natural processing for measures facial  Hardware is expressions expensive  Easy to  Interference of interpret muscle groups
  • 43. Intrusiveness of sensors is clear, but participants forgot about them after a short time
  • 45. 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.
  • 46. R.L. Mandryk, and M.S. Atkins (2007). A Fuzzy Physiological Approach for Continuously Modeling Emotion During Interaction with Play Environments. International Journal of Human- Computer Studies, 6(4), pg. 329-347. The original publication is available at Elsevier Online. R.L. Mandryk, K.M. Inkpen, and T.W. Calvert (2006). Using Psychophysiological Techniques to Measure User Experience with Entertainment Technologies. Behaviour and Information Technology (Special Issue on User Experience), Vol. 25, No.2, March-April 2006, pg. 141-158. R.L. Mandryk, M.S. Atkins, and K.M. Inkpen (2006). A Continuous and Objective Evaluation of Emotional Experience with Interactive Play Environments. in Proceedings of the Conference on Human Factors in Computing Systems (CHI 2006). Montreal, Canada, April 2006, pg. 1027-1036. R.L. Mandryk (2008). A physiological approach for continuously modeling user emotion in interactive play environments. in Proc of Measuring Behavior 2008, Maastricht, NE, August 2008, pg. 93-94.
  • 47. Few formal methods exist for evaluating the usability of game interfaces  Developed usability principles for video game design  Heuristics can be used to carry out usability inspections of video games
  • 48. Step 1: identify problems from game reviews  108 reviews from GameSpot  6 major PC game genres  Step 2: develop problems categories  12 common categories found  Step 3: develop game heuristics  10 heuristics created from problem categories
  • 49. 1. Provide consistent responses to user‘s actions 2. Allow users to customize video and audio settings, difficulty and game speed 3. Provide predictable and reasonable behaviour for computer controlled units 4. Provide unobstructed views that are appropriate for the user‘s current actions 5. Allow users to skip non-playable and frequently repeated content 6. Provide intuitive and customizable input mappings 7. Provide controls that are easy to manage, and that have an appropriate level of sensitivity and responsiveness 8. Provide users with information on game status 9. Provide instructions, training, and help 10. Provide visual representations that are easy to interpret and that minimize the need for micromanagement
  • 50. PRO CON  help identifying  does not address game-specific engagement and usability ―playability‖ problems  limitations in the  applicable to development of mockups and heuristics prototypes  can be used to evaluate most games
  • 51. Pinelle, D., Wong, N., Stach, T., Gutwin, C. (2009) Usability Heuristics for Networked Multiplayer Games. To appear in GROUP 2009.  Pinelle, D., Wong, N., Stach, T. (2008) Using Genres to Customize Usability Evaluations of Video Games. Future Play 2008, 129-136.  Pinelle, D., Wong, N., Stach, T. (2008) Heuristic Evaluation for Games: Usability Principles for Video Game Design. CHI 2008, 1453-1462.
  • 52. 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)
  • 53. 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).
  • 54. EEG Analysis is difficult. After artifact scoring, values have to be transformed for spectral analysis.
  • 55. PRO CON  Objective  Intrusive  Covert &  Expensive continuous  Artifact recording scoring  Quantifiable  Time-  Reliable consuming  Replicable  Sometimes  Empirical hard to power interpret
  • 56. 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
  • 57. Experimental gaming session with all logging equipment in place.
  • 58. Viewed game world objects can be displayed together with their gazepaths in 3D (see also Stellmach, 2009)
  • 59. PRO CON  Easy to use  Can be  Objective expensive  Covert  Lack of good  Continuous tools  Time-  Quantifiable consuming  Replicable  Empirical power
  • 60. 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. Grimshaw, M., Lindley, C. A., & Nacke, L. (2008). Sound and Immersion in the First-Person Shooter: Mixed Measurement of the Player‘s Sonic Experience. Audio Mostly Conference 2008, Piteå, Sweden. Nacke, L., Lindley, C., and Stellmach, S. (2008) Log Who‘s Playing: Psychophysiological Game Analysis Made Easy through Event Logging. In Proceedings of the 2nd international Conference on Fun and Games (Eindhoven, The Netherlands, October 20 - 21, 2008). P. Markopoulos, B. Ruyter, W. Ijsselsteijn, and D. Rowland, Eds. Lecture Notes In Computer Science, vol. 5294. Springer-Verlag, Berlin, Heidelberg, 150-157. Stellmach (2009). Visual Analysis of Eye Gaze Data in Virtual Environments. Master‘s Thesis. University of Magdeburg.
  • 61.  Is an integration of the presented methods feasible?  Can they be integrated in a cost- efficient way?  Which methods are suitable for evaluating which parts of game development?  Can empirical data be applied to game design? How?
  • 62.  Should there be a discussion about separating quantitative from qualitative or do we agree on integrated measures?  What can these methods be used for beyond evaluation? Exergames? Biofeedback?  Are those methods improving games? If yes, how can (or should) they be adopted by the majority of the game industry?
  • 63. Audience
  • 64. Mike: www.valvesoftware.com  Alessandro: www.dkds.dk  Regan: www.reganmandryk.com  Tad: equis.cs.queensu.ca  Lennart: www.acagamic.com  project.hkkk.fi/fuga/