Modeling Trust Dimensions and Dynamics in Human-Agent Conversation: A Trajectory Epistemic Network Analysis Approach

Li, Mengyao; Kamaraj, Amudha V.; Lee, John D.

doi:10.1080/10447318.2023.2201555

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…Additionally, the acoustic features extracted from conversations can be affectively laden. Future studies should consider using other conversational analysis techniques to extract both analytic and affective processes to understand trust ( Li et al, 2023 ). Second, the conversation design between humans and agents is restricted due to the technical limitations of the chatbot.…”

Section: Discussionmentioning

confidence: 99%

It’s Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation

Erickson

Cross

et al. 2023

Hum Factors

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Objective The objective of this study was to estimate trust from conversations using both lexical and acoustic data. Background As NASA moves to long-duration space exploration operations, the increasing need for cooperation between humans and virtual agents requires real-time trust estimation by virtual agents. Measuring trust through conversation is a novel and unintrusive approach. Method A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study with habitat system maintenance was designed to elicit various levels of trust in a conversational agent. Participants had trust-related conversations with the conversational agent at the end of each decision-making task. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). After training, model explanation was performed using variable importance and partial dependence plots. Results Results showed that a random forest algorithm, trained using the combined lexical and acoustic features, predicted trust in the conversational agent most accurately [Formula: see text]. The most important predictors were a combination of lexical and acoustic cues: average sentiment considering valence shifters, the mean of formants, and Mel-frequency cepstral coefficients (MFCC). These conversational features were identified as partial mediators predicting people’s trust. Conclusion Precise trust estimation from conversation requires lexical cues and acoustic cues. Application These results showed the possibility of using conversational data to measure trust, and potentially other dynamic mental states, unobtrusively and dynamically.

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Section: Discussionmentioning

confidence: 99%

It’s Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation

Erickson

Cross

et al. 2023

Hum Factors

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“…Additionally, users will likely vary in their affective and individual risk tolerance and risk-taking behaviors (Kannadhasan, 2015 ). Furthermore, group trust dynamics expressed through conversation will likely shape exactly how users utilize information as the trust process evolves (Li et al, 2023 ). It is therefore important to put users in a situation where we can examine how these processes unfold together and assess how trust processes manifest themselves in the AV situation.…”

Section: Introductionmentioning

confidence: 99%

Group trust dynamics during a risky driving experience in a Tesla Model X

et al. 2023

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The growing concern about the risk and safety of autonomous vehicles (AVs) has made it vital to understand driver trust and behavior when operating AVs. While research has uncovered human factors and design issues based on individual driver performance, there remains a lack of insight into how trust in automation evolves in groups of people who face risk and uncertainty while traveling in AVs. To this end, we conducted a naturalistic experiment with groups of participants who were encouraged to engage in conversation while riding a Tesla Model X on campus roads. Our methodology was uniquely suited to uncover these issues through naturalistic interaction by groups in the face of a risky driving context. Conversations were analyzed, revealing several themes pertaining to trust in automation: (1) collective risk perception, (2) experimenting with automation, (3) group sense-making, (4) human-automation interaction issues, and (5) benefits of automation. Our findings highlight the untested and experimental nature of AVs and confirm serious concerns about the safety and readiness of this technology for on-road use. The process of determining appropriate trust and reliance in AVs will therefore be essential for drivers and passengers to ensure the safe use of this experimental and continuously changing technology. Revealing insights into social group–vehicle interaction, our results speak to the potential dangers and ethical challenges with AVs as well as provide theoretical insights on group trust processes with advanced technology.

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A study of the developmental trajectory of students’ interactive dialogue model in middle school information technology course: An epistemic network analysis

Wang,

Wang

2024

Educ Inf Technol

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Modeling Trust Dimensions and Dynamics in Human-Agent Conversation: A Trajectory Epistemic Network Analysis Approach

Cited by 8 publications

References 46 publications

It’s Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation

It’s Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation

Group trust dynamics during a risky driving experience in a Tesla Model X

A study of the developmental trajectory of students’ interactive dialogue model in middle school information technology course: An epistemic network analysis

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