4.7 Article

Subjective Fear in Virtual Reality: A Linear Mixed-Effects Analysis of Skin Conductance

Journal

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 13, Issue 4, Pages 2047-2057

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2022.3197842

Keywords

Fear; mixed-effects modeling; skin conductance; virtual reality

Funding

  1. Italian Ministry of Education and Research (MIUR)
  2. European Union Horizon 2020 Programme [824153]

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This study proposes a new method of inducing and measuring fear using virtual reality and skin conductance, aiming to overcome the difficulties in studying fear perception. The researchers developed a VR scenario to induce fear in participants and recorded skin conductance signals during the process. The results showed that the fearful scenario elicited greater sympathetic activation compared to other scenarios, and there was a significant relationship between features extracted from the skin conductance signals and fear perception.
The investigation of the physiological and pathological processes involved in fear perception is complicated due to the difficulties in reliably eliciting and measuring the complex construct of fear. This study proposes a novel approach to induce and measure subjective fear and its physiological correlates combining virtual reality (VR) with a mixed-effects model based on skin conductance (SC). Specifically, we developed a new VR scenario applying specific guidelines derived from horror movies and video games. Such a VR environment was used to induce fear in eighteen volunteers in an experimental protocol, including two relaxation scenarios and a neutral virtual environment. The SC signal was acquired throughout the experiment, and after each virtual scenario, the emotional state and fear perception level were assessed using psychometric scales. We statistically evaluated the greatest sympathetic activation induced by the fearful scenario compared to the others, showing significant results for most SC-derived features. Finally, we developed a rigorous mixed-effects model to explain the perceived fear as a function of the SC features. Model-fitting results showed a significant relationship between the fear perception scores and a combination of features extracted from both fast- and slow-varying SC components, proposing a novel solution for a more objective fear assessment.

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