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Perception of Artificial Intelligence: GSR Analysis and Face Detection

Author

Listed:
  • Oleksii Lyulyov

    (WSB University, Dabrowa Gornicza, Poland; Sumy State University, Ukraine)

  • Tetyana Pimonenko

    (WSB University, Dabrowa Gornicza, Poland; Sumy State University, Ukraine)

  • Alfonso Infante-Moro

    (University of Huelva, Huelva, Spain)

  • Aleksy Kwilinski

    (The London Academy of Science and Business, London, UK)

Abstract
This study explored the perception of artificial intelligence (AI) through GSR analysis and facial expression detection across eight different video stimuli. The results indicate that one video elicited the highest cognitive engagement, while another showed significant engagement through both the frequency and intensity of responses. Certain videos displayed a lower frequency but higher intensity of responses. The Shapiro‒Wilk and Levene’s tests validated the use of ANOVA, confirming the normality and homogeneity of variances. Despite variations in mean GSR peaks per minute, ANOVA revealed no significant differences in physiological responses among the different interaction types. Gender analysis revealed similar high physiological responses to AI stimuli for both males and females, with most stimuli eliciting statistically significant GSR peaks per minute. The Affectiva AFFDEX SDK classifier analysed emotional responses, revealing that joy was predominantly higher in one video, while another elicited the most sadness. Anger and fear were nearly non-existent, and contempt varied, with one video showing the highest response. Disgust and surprise responses were generally low. These findings highlight the importance of emotional content in engaging viewers and the utility of GSR and facial expression analysis in understanding AI's impact on user perception. This research provides insights into cognitive and emotional engagement with AI-related stimuli, emphasizing the need for tailored content to enhance user interaction. The study's implications extend to marketing, education, and healthcare, where optimizing user engagement with AI can lead to improved outcomes and satisfaction.

Suggested Citation

  • Oleksii Lyulyov & Tetyana Pimonenko & Alfonso Infante-Moro & Aleksy Kwilinski, 2024. "Perception of Artificial Intelligence: GSR Analysis and Face Detection," Virtual Economics, The London Academy of Science and Business, vol. 7(2), pages 7-30, June.
    • Handle: RePEc:aid:journl:v:7:y:2024:i:2:p:7-30
    DOI: 10.34021/ve.2024.07.02(1)
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    References listed on IDEAS

    as
    1. Aleksy Kwilinski & Oleksii Lyulyov & Tetyana Pimonenko, 2024. "Energy Poverty and Democratic Values: A European Perspective," Energies, MDPI, vol. 17(12), pages 1-23, June.
    2. Rizwan Raheem Ahmed & Dalia Streimikiene & Zahid Ali Channar & Hassan Abbas Soomro & Justas Streimikis & Grigorios L. Kyriakopoulos, 2022. "The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    3. Ekundayo P. Mesagan & Chidi N. Olunkwa, 2020. "Energy Consumption, Capital Investment and Environmental Degradation: The African Experience," Working Papers 20/022, European Xtramile Centre of African Studies (EXCAS).
    4. Aleksy Kwilinski, 2024. "Mapping Global Research on Green Energy and Green Investment: A Comprehensive Bibliometric Study," Energies, MDPI, vol. 17(5), pages 1-24, February.
    Full references (including those not matched with items on IDEAS)

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