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Krzysztof Kutt1, Dominika Drążyk1, Szymon Bobek1

  • 1Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI) and Institute of Applied Computer Science, Jagiellonian University, 31-007 Krakow, Poland.

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Summary
This summary is machine-generated.

Personality assessment can tailor affective intelligent systems by grouping users based on their emotional responses. This study links personality traits to psychophysiological signals and reactions, showing potential for adaptive system behavior.

Keywords:
adaptationaffective computingemotion detectionpersonality assessmentwearable sensors

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Area of Science:

  • Affective computing
  • Psychological assessment
  • Human-computer interaction

Background:

  • Affective intelligent systems aim to understand and respond to user emotions.
  • Adapting system behavior to individual emotional responses is crucial for effective interaction.
  • Current adaptation mechanisms often lack a robust psychological foundation.

Purpose of the Study:

  • To propose and evaluate personality assessment as a method for adapting affective intelligent systems.
  • To investigate the relationship between personality traits and user reactions to affective stimuli.
  • To explore the use of psychophysiological signals in conjunction with personality assessment for system adaptation.

Main Methods:

  • Conducted an experiment with 206 participants.
  • Utilized two proof-of-concept demonstrations: classical stimuli presentation and affective games.
  • Collected psychophysiological data using the BITalino (r)evolution kit, including electrocardiogram (ECG) and galvanic skin response (GSR).

Main Results:

  • Identified significant correlations between personality traits and psychophysiological signals (ECG, GSR).
  • Found significant links between personality traits and user reactions within a complex affective stimulus environment.
  • Demonstrated the feasibility of grouping users based on personality for adaptive system adjustments.

Conclusions:

  • Personality assessment offers a psychologically-grounded approach to adapting affective intelligent systems.
  • The findings support the potential of using personality traits to predict and modulate user responses to affective stimuli.
  • This mechanism can lead to more personalized and effective human-computer interactions in affective systems.