Next Generation Testing: Biometric Analysis of Player Experience

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

by Lennart Nacke on May 15, 2009

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