The Role of Individual Differences as Predictors of Trust in Autonomous Security Robots

Lyons, Joseph B.; Nam, Chang S.; Jessup, Sarah A.; Vo, Thy; Wynne, Kevin T.

doi:10.1109/ichms49158.2020.9209544

Cited by 13 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Corroborating our results from Experiment 1, the thought detector robot was perceived as significantly eerier than the emotion detector robot. Moreover, a significant effect of agreeableness was found: Higher levels in this basic personality dimension were associated with less eeriness ascribed to the detector robots, matching the way this trait had affected user responses in prior human-robot studies (e.g., Chien et al, 2016;Lyons et al, 2020;Takayama & Pantofaru, 2009). As people high in agreeableness typically react in a tolerant and kind-mannered way to outside influences, it comes as little surprise that they also responded more positively to the presented detection robots.…”

Section: Discussionsupporting

confidence: 58%

“…People scoring high on this dimension tend to forgive wrongs that they suffered, are able to control their temper and are willing to compromise and cooperate with others . Agreeableness was a predictor of trust in an autonomous security robot (Lyons et al, 2020) and was associated with higher trust in machines in general (Chien et al, 2016). Moreover, a higher score on agreeableness correlated with keeping a lower interpersonal distance to robots (Takayama & Pantofaru, 2009).…”

Section: Agreeablenessmentioning

confidence: 99%

See 1 more Smart Citation

Mind-reading machines: Distinct user responses to thought-detecting and emotion-detecting robots.

Grundke¹,

Stein²,

Appel³

2022

Technology, Mind, and Behavior

View full text Add to dashboard Cite

Human-like robots and other systems with artificial intelligence are increasingly capable of recognizing and interpreting the mental processes of their human users. The present research examines how people evaluate these seemingly mind-reading machines based on the well-established distinction of human mind into agency (i.e., thoughts and plans) and experience (i.e., emotions and desires). Theory and research that applied this distinction to human-robot interaction showed that machines with experience were accepted less and were perceived to be eerier than those with agency. Considering that humans are not yet used to having their thoughts read by other entities and might feel uneasy about this notion, we proposed that thought-detecting robots are perceived to be eerier and are generally evaluated more negatively than emotion-detecting robots. Across two pre-registered experiments (N 1 = 335, N 2 = 536) based on text vignettes about different kinds of mind-detecting robots, we find support for our hypothesis. Furthermore, the effect remained independent of the six HEXACO personality dimensions, except for an unexpected interaction with conscientiousness. Implications and directions for future research are discussed.

show abstract

Section: Discussionsupporting

confidence: 58%

Section: Agreeablenessmentioning

confidence: 99%

Mind-reading machines: Distinct user responses to thought-detecting and emotion-detecting robots.

Grundke¹,

Stein²,

Appel³

2022

Technology, Mind, and Behavior

View full text Add to dashboard Cite

show abstract

“…C. Measures 1) Personality: The big 5 factors of personality (Extraversion, Conscientiousness, Agreeableness, Neuroticism, and Imagination) were measured using the 20-item mini-IPIP scale [15]. This 5-point Likert scale has widely been used in human-robot trust research [16].…”

Section: B Testbedmentioning

confidence: 99%

“…5) Post-experiment trust: After the experiment, we used two scales [16], [18] for assessing trust. The first one was a 9-item questionnaire with sliders while we used 6-items from the second scale all of which were 7-point Likert type questions.…”

Section: B Testbedmentioning

confidence: 99%

Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

Bhat

Lyons

Shi

et al. 2022

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

In this paper, we present a framework for trustaware sequential decision-making in a human-robot team. We model the problem as a finite-horizon Markov Decision Process with a reward-based performance metric, allowing the robotic agent to make trust-aware recommendations. Results of a human-subject experiment show that the proposed trust update model is able to accurately capture the human agent's momentto-moment trust changes. Moreover, we cluster the participants' trust dynamics into three categories, namely, Bayesian decision makers, oscillators, and disbelievers, and identify personal characteristics that could be used to predict which type of trust dynamics a person will belong to. We find that the disbelievers are less extroverted, less agreeable, and have lower expectations toward the robotic agent, compared to the Bayesian decision makers and oscillators. The oscillators are significantly more frustrated than the Bayesian decision makers.

show abstract

“…Previous studies (e.g., Merritt et al, 2015 ; Lyons and Guznov, 2018 ; Lyons et al, 2020 ) have shown large variation in the magnitudes of correlations between dispositional and situational trust measures, which made an a priori power analysis problematic. We aimed to run approximately 120 participants based on typical sample sizes in comparable simulation-based studies (e.g., Schaefer, 2016 ; Lyons and Guznov, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Trust in the Danger Zone: Individual Differences in Confidence in Robot Threat Assessments

et al. 2022

View full text Add to dashboard Cite

Effective human–robot teaming (HRT) increasingly requires humans to work with intelligent, autonomous machines. However, novel features of intelligent autonomous systems such as social agency and incomprehensibility may influence the human’s trust in the machine. The human operator’s mental model for machine functioning is critical for trust. People may consider an intelligent machine partner as either an advanced tool or as a human-like teammate. This article reports a study that explored the role of individual differences in the mental model in a simulated environment. Multiple dispositional factors that may influence the dominant mental model were assessed. These included the Robot Threat Assessment (RoTA), which measures the person’s propensity to apply tool and teammate models in security contexts. Participants (N = 118) were paired with an intelligent robot tasked with making threat assessments in an urban setting. A transparency manipulation was used to influence the dominant mental model. For half of the participants, threat assessment was described as physics-based (e.g., weapons sensed by sensors); the remainder received transparency information that described psychological cues (e.g., facial expression). We expected that the physics-based transparency messages would guide the participant toward treating the robot as an advanced machine (advanced tool mental model activation), while psychological messaging would encourage perceptions of the robot as acting like a human partner (teammate mental model). We also manipulated situational danger cues present in the simulated environment. Participants rated their trust in the robot’s decision as well as threat and anxiety, for each of 24 urban scenes. They also completed the RoTA and additional individual-difference measures. Findings showed that trust assessments reflected the degree of congruence between the robot’s decision and situational danger cues, consistent with participants acting as Bayesian decision makers. Several scales, including the RoTA, were more predictive of trust when the robot was making psychology-based decisions, implying that trust reflected individual differences in the mental model of the robot as a teammate. These findings suggest scope for designing training that uncovers and mitigates the individual’s biases toward intelligent machines.

show abstract

The Role of Individual Differences as Predictors of Trust in Autonomous Security Robots

Cited by 13 publications

References 20 publications

Mind-reading machines: Distinct user responses to thought-detecting and emotion-detecting robots.

Mind-reading machines: Distinct user responses to thought-detecting and emotion-detecting robots.

Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

Trust in the Danger Zone: Individual Differences in Confidence in Robot Threat Assessments

Contact Info

Product

Resources

About