Psychophysiological Modelling of Trust in Technology: Comparative Analysis of Psychophysiological Signals

Ajenaghughrure, Ighoyota Ben; Sousa, Sónia; Lamas, David

doi:10.5220/0010237701610173

Cited by 5 publications

(4 citation statements)

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“…In this study, the average decision time was 4 seconds. Employing a longer epoch time window in contexts that are not time-sensitive might improve performance for these other PBs [4].…”

Section: Discussionmentioning

confidence: 99%

“…PBs have been studied across various disciplines, including psychology, neuroscience, medicine, games and human-robot interaction [4,10,27]. In HRI, these PBs have been proposed as alternative methods for assessing trust in HRI [11].…”

Section: Measurement Of Trustmentioning

confidence: 99%

“…These behaviours indicate an individual's emotional and cognitive states during interactions with robots. By monitoring these responses, we can gain insights into the dynamic nature of trust and its impact on HRI [4].…”

Section: Introductionmentioning

confidence: 99%

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Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction

Ahmad,

Alzahrani

2023

International Conference on Multimodal Interaction

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Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human's trust in robots in real-time during HRI. Therefore, this study aims to estimate human trust in robots by examining the differences in PBs between trust and distrust states. It further investigates the changes in PBs across repeated HRI and also explores the potential of machine learning classifiers in predicting trust levels during HRI. We collected participants' electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) during repeated HRI. The results showed significant differences in HR and SKT between trust and distrust groups and no significant interaction effect of session and decision for all PBs. Random Forest classifier achieved the best accuracy of 68.6% to classify trust, while SKT, HR, BR, and BD were the important features. These findings highlight the value of PBs in measuring trust in real-time during HRI and encourage further investigation of trust measures with PBs in various HRI settings. CCS CONCEPTS• Human-centered computing → Human Robot interaction ; User studies; • Computer systems organization → Robotics.

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“…In this study, the average decision time was 4 seconds. Employing a longer epoch time window in contexts that are not time-sensitive might improve performance for these other PBs [4].…”

Section: Discussionmentioning

confidence: 99%

Section: Measurement Of Trustmentioning

confidence: 99%

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Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction

Ahmad,

Alzahrani

2023

International Conference on Multimodal Interaction

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“…In future research, we suggest to explore, in more detail, cultural and gender effects on trust in technology. As suggested by Ajenaghughrure et al [70], the development of longitudinal studies (using HRITS) to capture user trust dynamics through time, exploring novel approaches to measure unobtrusively variations, could be done in future research.…”

Section: Discussionmentioning

confidence: 99%

A Trust Scale for Human-Robot Interaction: Translation, Adaptation, and Validation of a Human Computer Trust Scale

Pinto

Sousa

Simões

et al. 2022

Human Behavior and Emerging Technologies

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Recently there has been an increasing demand for technologies (automated and intelligent machines) that brings benefits to organizations and society. Similar to the widespread use of personal computers in the past, today’s needs are towards facilitating human-machine technology appropriation, especially in highly risky and regulated industries like robotics, manufacturing, automation, military, finance, or healthcare. In this context, trust can be used as a critical element to instruct how human-machine interaction should occur. Considering the context-dependency and multidimensional trust, this study seeks to find a way to measure the effects of perceived trust in a collaborative robot (cobot), regardless of its literal credibility as a real person. This article aims at translating, adapting, and validating a Human-Computer Trust Scale (HCTM) in human-robot interaction (HRI) context and its application to cobots. The Human-Robot Interaction Trust Scale (HRITS) involved 239 participants and included eleven items. The 2nd order CFA with a general factor called “trust” have proven to be empirically robust ( CFI = .94 ; TLI = .93 ; SRMR = .04 ; and RMSEA = .05 ) [ CR = .84 ; AVE = .58 , and MaxR H = .92 ]; results indicated a good measurement of the general factor trust, and the model satisfied the criteria for measure trust. An analysis of the differences in perceptions of trust by gender was conducted using a t -test. This analysis showed that statistical differences by gender exist ( p = .04 ). This study’s results allowed for a better understanding of trust in HRI, specifically regarding cobots. The validation of a Portuguese scale for trust assessment in HRI can give a valuable contribution to designing collaborative environments between humans and robots.

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Dynamic and quantitative trust modeling and real-time estimation in human-machine co-driving process

Hu,

Huang,

Zhou

et al. 2024

Transportation Research Part F: Traffic Psychology and Behaviou

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Psychophysiological Modelling of Trust in Technology: Comparative Analysis of Psychophysiological Signals

Cited by 5 publications

References 0 publications

Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction

Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction

A Trust Scale for Human-Robot Interaction: Translation, Adaptation, and Validation of a Human Computer Trust Scale

Dynamic and quantitative trust modeling and real-time estimation in human-machine co-driving process

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