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Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment.

Martin Čertický1, Michal Čertický2, Peter Sinčák3

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This summary is machine-generated.

This study shows that using psychophysiological measures, like heart rate and skin response, can accurately predict user enjoyment in video games, overcoming limitations of traditional surveys.

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digital entertainmentelectroencephalographyenjoymentgalvanic skin responseheart ratemachine learningmodelingpsychophysiological measuresrespiratory activityuser experience

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Area of Science:

  • Human-Computer Interaction
  • Psychophysiology
  • Machine Learning

Background:

  • Current user experience (UX) analysis in electronic entertainment relies heavily on self-reporting methods.
  • Self-reporting alone has limitations, including misinterpretation and temporal delays, making it insufficient as a sole metric.
  • There is a need for more objective and real-time methods to assess user experience.

Purpose of the Study:

  • To propose and demonstrate the modeling of consumer experience using psychophysiological measures.
  • To investigate the effectiveness of machine learning in training models for user experience assessment.
  • To provide a more accurate and reliable method for evaluating digital entertainment engagement.

Main Methods:

  • Utilized machine learning to model user experience based on real-time psychophysiological data.
  • Collected data from 31 participants playing video games for hour-long sessions.
  • Integrated multiple psychophysiological measures (heart rate, electrodermal activity, respiratory activity) with self-reporting for training data.

Main Results:

  • Compared four different machine learning models for user experience prediction.
  • The best-performing model achieved approximately 96% accuracy in assessing user experience.
  • Demonstrated a high correlation between psychophysiological responses and user enjoyment.

Conclusions:

  • Psychophysiological measures can effectively be used to assess the enjoyment of digital entertainment consumers.
  • Machine learning models trained on psychophysiological data offer a robust alternative to traditional self-reporting methods.
  • This approach enhances the objectivity and reliability of user experience research in the gaming industry.