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Log Who’s Playing:
Psychophysiological Game Analysis
Made Easy Through Event Logging



Lennart Nacke, Craig Lindley, Sophie Stellmach
       Blekinge Institute of Technology, Sweden
       Department of Interaction and System Design
       Game and Media Arts Laboratory
Outline
 Motivation for our work


 Conceptual design


 Automated game event logging
  – Details
  – A word about gaze logging



 Conclusion and future work
Motivation
 Understand user behavior by decoding events


 Evaluation data accompanied by video
  – Qualitative data
  – Behavioral data



 Improvement by adding even-related physiological data
  – Specific phasic response in event context



 What makes gameplay fun?
Conceptual design
 Meaningful events
   – In-game events
      User-created
      Pre-defined and triggered

   – Real-world events
      Sensors (physiological, positional, etc.)
      Video



 Record at run-time speed of 3d game engine
 Interface with physiological recording hardware
   – Parallel port (200 ms accuracy)

 Easy integration into editing tools
IMPLEMENTED IN




  SOURCE SDK
Types of logging
Event-code examples

105:
  Pressing
  red button

55:
  Player fires
  a gun

21:
  Damage to smiling
  opposing NPC
Automated game logging
 Transmission component
   – Interfacing class
   – Communication with physiological recording hardware

 Event component
   – Common (level-independent)
      Enemy is killed, etc.

   – Unique (level-dependent)
      Player enters area, etc.

 Dynamically assign unique events in editing tool
                                      CBaseEntity

     Event_Log                        Logging_Entity
                                                       Private
                          Input           Output
                                                       Data
                          Functions      Functions
                                                       Members
Dynamically assigning events
Screenshot showing the logging entities and manually defined output
codes inside the Hammer editing tool for the Half-Life 2 Source SDK.
Correlation of Physiological Data to Events
Physiological data is recorded together with real-time game events,
allowing for automatic data clustering and analysis
Main benefit: time reduction for researcher


     Data correlation is fully automated




               Experiment           Data       Analysis
Preparation
                                 Correlation
A word about gaze logging
 Automatically logs and identifies
   – Game objects
   – Graphical assets

 Tobii 1750 (SDK)
   – Video-based
   – Corneal-reflection tracking

 Logs
   – Object ID
   – Event ID
   – Time stamp
   – L+R gaze points (x,y)
Dissecting player experience
 Surveys
   – Pre-game
   – (In-game)
   – Post-game

 Physiological responses
   – Emotion
   – Cognition
   – Arousal

 Visual attention
   – Gaze tracking

 Game Metrics
Conclusion and future work
 Event-logging enables detailed understanding
   – of game experience

 Automatic data correlation saves analysis time


 Studies using the system are ongoing
 First results will be coming in shortly
Thank you kindly for listening!



              me            Craig         Sophie
                         cld@bth.se    slm@bth.se


Lennart Nacke




                                                     Find me at…
Blekinge Institute of Technology
Game and Media Arts Laboratory
Karlshamn, Sweden


Lennart.Nacke@bth.se
http://gamescience.bth.se
http://www.acagamic.com

        Download papers from: http://gamescience.bth.se/research/publications

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Log Who's Playing: Psychophysiological Game Analysis Made Easy Through Event Logging

  • 1. Log Who’s Playing: Psychophysiological Game Analysis Made Easy Through Event Logging Lennart Nacke, Craig Lindley, Sophie Stellmach Blekinge Institute of Technology, Sweden Department of Interaction and System Design Game and Media Arts Laboratory
  • 2. Outline  Motivation for our work  Conceptual design  Automated game event logging – Details – A word about gaze logging  Conclusion and future work
  • 3. Motivation  Understand user behavior by decoding events  Evaluation data accompanied by video – Qualitative data – Behavioral data  Improvement by adding even-related physiological data – Specific phasic response in event context  What makes gameplay fun?
  • 4. Conceptual design  Meaningful events – In-game events  User-created  Pre-defined and triggered – Real-world events  Sensors (physiological, positional, etc.)  Video  Record at run-time speed of 3d game engine  Interface with physiological recording hardware – Parallel port (200 ms accuracy)  Easy integration into editing tools
  • 5. IMPLEMENTED IN SOURCE SDK
  • 7. Event-code examples 105: Pressing red button 55: Player fires a gun 21: Damage to smiling opposing NPC
  • 8. Automated game logging  Transmission component – Interfacing class – Communication with physiological recording hardware  Event component – Common (level-independent)  Enemy is killed, etc. – Unique (level-dependent)  Player enters area, etc.  Dynamically assign unique events in editing tool CBaseEntity Event_Log Logging_Entity Private Input Output Data Functions Functions Members
  • 9. Dynamically assigning events Screenshot showing the logging entities and manually defined output codes inside the Hammer editing tool for the Half-Life 2 Source SDK.
  • 10. Correlation of Physiological Data to Events Physiological data is recorded together with real-time game events, allowing for automatic data clustering and analysis
  • 11. Main benefit: time reduction for researcher  Data correlation is fully automated Experiment Data Analysis Preparation Correlation
  • 12. A word about gaze logging  Automatically logs and identifies – Game objects – Graphical assets  Tobii 1750 (SDK) – Video-based – Corneal-reflection tracking  Logs – Object ID – Event ID – Time stamp – L+R gaze points (x,y)
  • 13. Dissecting player experience  Surveys – Pre-game – (In-game) – Post-game  Physiological responses – Emotion – Cognition – Arousal  Visual attention – Gaze tracking  Game Metrics
  • 14. Conclusion and future work  Event-logging enables detailed understanding – of game experience  Automatic data correlation saves analysis time  Studies using the system are ongoing  First results will be coming in shortly
  • 15. Thank you kindly for listening! me Craig Sophie cld@bth.se slm@bth.se Lennart Nacke Find me at… Blekinge Institute of Technology Game and Media Arts Laboratory Karlshamn, Sweden Lennart.Nacke@bth.se http://gamescience.bth.se http://www.acagamic.com Download papers from: http://gamescience.bth.se/research/publications