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Psychophysiological Modeling of Trust In Technology: Influence of Feature Selection Methods

Authors:

Ighoyota Ben Ajenaghughrure,

Sonia Cláudia Da Costa Sousa,

David LamasAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue EICS

Article No.: 203, Pages 1 - 25

https://doi.org/10.1145/3459745

Published: 29 May 2021 Publication History

Abstract

Trust as a precursor for users' acceptance of artificial intelligence (AI) technologies that operate as a conceptual extension of humans (e.g., autonomous vehicles (AVs)) is highly influenced by users' risk perception amongst other factors. Prior studies that investigated the interplay between risk and trust perception recommended the development of real-time tools for monitoring cognitive states (e.g., trust). The primary objective of this study was to investigate a feature selection method that yields feature sets that can help develop a highly optimized and stable ensemble trust classifier model. The secondary objective of this study was to investigate how varying levels of risk perception influence users' trust and overall reliance on technology. A within-subject four-condition experiment was implemented with an AV driving game. This experiment involved 25 participants, and their electroencephalogram, electrodermal activity, and facial electromyogram psychophysiological signals were acquired. We applied wrapper, filter, and hybrid feature selection methods on the 82 features extracted from the psychophysiological signals. We trained and tested five voting-based ensemble trust classifier models using training and testing datasets containing only the features identified by the feature selection methods. The results indicate the superiority of the hybrid feature selection method over other methods in terms of model performance. In addition, the self-reported trust measurement and overall reliance of participants on the technology (AV) measured with joystick movements throughout the game reveals that a reduction in risk results in an increase in trust and overall reliance on technology.

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Cited By

Mustofa RKuncoro TAtmono DHermawan HSukirman (2025)Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among studentsComputers and Education: Artificial Intelligence10.1016/j.caeai.2025.1003798(100379)Online publication date: Jun-2025
https://doi.org/10.1016/j.caeai.2025.100379
Damaševičius R(2024)Comment on Novozhilova et al. More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts. Mach. Learn. Knowl. Extr. 2024, 6, 342–366Machine Learning and Knowledge Extraction10.3390/make60300816:3(1667-1669)Online publication date: 22-Jul-2024
https://doi.org/10.3390/make6030081
Xu CZhang CZhou YWang ZLu PHe B(2024)Trust Recognition in Human-Robot Cooperation Using EEG2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610156(7827-7833)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610156
Show More Cited By

Index Terms

Psychophysiological Modeling of Trust In Technology: Influence of Feature Selection Methods
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue EICS

EICS

June 2021

546 pages

EISSN:2573-0142

DOI:10.1145/3468527

Editor:
Jeff Nichols
Apple Inc., United States

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Published: 29 May 2021

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https://doi.org/10.1016/j.caeai.2025.100379
Damaševičius R(2024)Comment on Novozhilova et al. More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts. Mach. Learn. Knowl. Extr. 2024, 6, 342–366Machine Learning and Knowledge Extraction10.3390/make60300816:3(1667-1669)Online publication date: 22-Jul-2024
https://doi.org/10.3390/make6030081
Xu CZhang CZhou YWang ZLu PHe B(2024)Trust Recognition in Human-Robot Cooperation Using EEG2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610156(7827-7833)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610156
Martín-Moncunill DGarcía Laredo ECarlos Nieves J(2024)POTDAI: A Tool to Evaluate the Perceived Operational Trust Degree in Artificial Intelligence SystemsIEEE Access10.1109/ACCESS.2024.345406112(133097-133109)Online publication date: 2024
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Krieger JBouder FWibral MAlmeida R(2024)A systematic literature review on risk perception of Artificial Narrow IntelligenceJournal of Risk Research10.1080/13669877.2024.2350725(1-19)Online publication date: 6-Jun-2024
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Delgado Rodriguez SPrange SVergara Ossenberg CHenkel MAlt FMarky K(2022)PriKey – Investigating Tangible Privacy Control for Smart Home Inhabitants and VisitorsNordic Human-Computer Interaction Conference10.1145/3546155.3546640(1-13)Online publication date: 8-Oct-2022
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