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Abstract

This research aims to study trust in Human-Autonomy Teaming (HAT). A multiple regression model was employed to explore potential predictors in tasks that might dynamically influence trust levels. By computing entropy across different system layers of a remotely piloted aerial vehicle system, we measured the influence of these dynamic predictors on trust. The findings suggest the presence of potential trust predictors in human-autonomy interactions as measured using entropy. Some layers had to be combined due to high multicollinearity, addressed through Principal Component Analysis (PCA). Further research is required for a more in-depth understanding of these components and their implications for trust dynamics.

Keywords

dynamical measurements team coordination human-ai teaming team cognition

Introduction

In the contemporary landscape of research and development, human-autonomy teaming has attracted substantial attention. This concept pertains to the operation of humans with intelligent and autonomous agents, working interdependently toward a common goal (Chen & Barnes, 2014; McNeese et al., 2018). For autonomous agents to be recognized as legitimate team members, they should embody a digital or cyber-physical entity that exhibits self-governance and can communicate with other teammates utilizing its own capabilities (Demir et al., 2016; Myers et al., 2019).

Effective human-autonomy teaming involves transforming autonomous systems to be better teammates, not merely making them more independent (Johnson et al., 2011). Autonomous systems must evolve to become trusted teammates (Abbass et al., 2018). Without human trust in their autonomous counterparts, situations could become risky or unpleasant, for example, drivers not trusting their autonomous cars (Hartwich et al., 2018).

Human-automation trust is defined as the attitude that an individual team member believes an agent will assist them in achieving their goals in situations of uncertainty and vulnerability (J. D. Lee & See, 2004). To measure trust, researchers have employed various methods, such as questionnaires, for example, the Trust in Automation Scale (Lewis et al., 2018) and Trust in Automated Systems Survey (Jian et al., 2000), and objective measures such as observations based on performance (Law & Scheutz, 2021).

Recent studies indicate that trust is not static but dynamic, evolving based on continuous interactions and experiences. Dynamic trust in automation can be influenced by various factors, ranging from individual characteristics like culture and age to system aspects like reliability and automation level (Yang et al., 2023). Researchers have sought to simulate real-world scenarios to better comprehend trust dynamics. For example, J. Lee and Moray (1992) utilized a simulated pasteurization task to track participants’ trust levels over multiple trials, while Manzey et al. (2012) analyzed human operator trust across both positive and negative interactions with an automation platform. Both studies identified significant trust adjustments based on system performance.

Previous researchers have used Dynamical Systems Analytical methods, such as recurrence quantification analysis to study trust. For example, Mitkidis et al. (2015) used multivariate recurrence quantification analysis to study the degree of synchrony in human-human teams, suggesting that interpersonal physiological synchrony may predict interpersonal trust. Grimm et al. (2018) applied Joint Recurrence Quantification Analysis (JRQA) to explore team communication in a remotely piloted aircraft system. In addition to JRQA, other methods utilizing information entropy (Shannon, 1949) were applied, measuring the changing amount of information reorganization in a system over time. This paper will employ this latter method to investigate the potential relationship between trust and changing entropy system states using predictive linear regression techniques.

The Current Study

While the dynamics of trust in human-autonomy teaming (HAT) have been acknowledged, the mechanisms influencing its variations over time remain underexplored. This study aims to address this gap by examining trust as a dynamic variable responding to human-automation interactions. We investigate two main questions: RQ1: How do specific task events influence dynamic trust levels in HAT? RQ2: Can entropy calculations across system layers yield insights into trust dynamics? We hypothesize that H1: Trust in automation will fluctuate in response to continuous interactions with the autonomous system, and H2: Trust in teammates will be influenced by the type of teammate (AI/synthetic versus human).

This study measures system reorganization and its relationship to subjective trust, aiming to provide a more objective view of trust evolution based on system dynamics. The goal is to contribute to optimally designed human-autonomy interactions that enhance trust and collaboration.

Methods

A total of 42 individuals, aged between 18 and 31 (M = 20.5, SD = 2.9), participated in the research, recruited from the Georgia Institute of Technology and surrounding areas. Participants were organized into 21 teams, each comprising three members, with two members per team being participants and the other either an autonomous agent pilot or confederate experimenter pilot. The study was approved by the Georgia Tech Institutional Review Board.

The research employed the Cognitive Engineering Research on Team Tasks-Remotely Piloted Aircraft System-Synthetic Task Environment (CERTT-RPAS-STE; Cooke & Shope, 2005). The task environment comprised three separate stations, each equipped with two screens, a keyboard, and a mouse. The goal was to take clear pictures of ground target waypoints. The Air Vehicle Operator (AVO) was either an autonomous agent or a trained experimenter, controlling the remotely piloted aircraft RPA’s altitude and airspeed adjustments in response to requests from the Photographer (PLO) and data from the Decision Making/Planning and Control (DEMPC). The Photographer (PLO) adjusted camera settings and negotiated altitude and airspeed with AVO to take clear photos of targets.

Participants were randomly assigned roles of either Decision Making/Planning and Control (DEMPC) or PLO. They were seated in different workstations, unable to see or hear other teammates. In the autonomous teammate condition, the pilot role was played by a synthetic teammate computer agent (Ball et al., 2010). The process began with an introduction and review of training materials, followed by a practice mission. Teams then carried out four missions, each lasting approximately 40 min. Participants completed three questionnaires: one before training, another after Mission 3, and a third after Mission 4. This study focuses on the first two questionnaires, as all participants transitioned to working with the human (experimenter) AVO at Mission 4.

Measures

Trust was assessed using two questionnaires. The first, originally developed by Mayer and Gavin (2005) and modified by Demir et al. (2021), used a 5-point Likert scale for 25 items. The second, the Checklist for Trust between People and Automation Scale (Jian et al., 2000), comprised 12 items on a 7-point Likert scale.

System Layers were analyzed to understand interactions and their potential relationships with trust dynamics. These layers, each holding a set of events or controls, included alarm warning events, turning events, change of altitude and airspeed, switch resources, AVO related events, PLO related events, DEMPC related events, AVO control, PLO control, and DEMPC control (Table 1).

Table 1.

Elements of the RPA System State Vector.

Vehicle states	Control states		Communication states
Fuel	Set shutter	Change route	PLO → AVO/DEM
Battery	Set focus	Send route	PLO → AVO
Film	Set camera	Request flight plan	PLO → DEM
Temperature	Set zoom	New queued	AVO → PLO/DEM
Left turn	Set aperture	New to	AVO → PLO
Right turn	Set lens	Set GOV	AVO → DEM
Warning/Alarm	Check required	Set airspeed	DEM → AVO/PLO
Altitude	Settings	Set altitude	DEM → AVO
Airspeed	Take photo	Refuel	DEM → PLO
	Accept photo	Charge battery

Results

Entropy was calculated across ten different events across system layers (Table 2) in the RPA task (alarm warning events, turning events, change of altitude and airspeed, switch resources, AVO related events, PLO related events, DEMPC related events, AVO control, PLO control, DEMPC control) for Mission 1, Mission 2, and Mission 3, coinciding with trust survey recordings. (Team 9 mission 4 is the average score as the original data was missing.)

Table 2.

Overview of Events and Controls in AVO, PLO, and DEM.

Events	Description	Correspondence
Warning and alarm events (warning_alarm_events)	This layer encompasses critical alerts, be they immediate hazards, system malfunctions, or situations requiring urgent attention.	Corresponding to Warning/Alarm in vehicle states.
Altitude and airspeed events (events_alt_air)	Capturing the dynamics related to changes in altitude and adjustments in airspeed, this layer portrays how the system adapts and responds to navigational needs.	Corresponding to set airspeed and set altitude in control states.
Turning events (turning_events)	This involves the system’s directional decisions, highlighting left or right turns which can indicate the system’s response to environmental stimuli or navigational demands.	Corresponding to left turn and right turn in vehicle states.
Resource events (resource_events)	Detailing the system’s resource management, covering aspects like fuel, battery, film, and temperature, and how the system manages and communicates about these resources.	Corresponding to fuel, battery, film, and temperature in vehicle states.
Events related to AVO (events_avo)	Focusing on the interactions, commands, and feedback specifically associated with the Air Vehicle Operator.	Corresponding to all communication states related to AVO.
Events related to PLO (events_plo)	Detailing the decisions, interactions, and commands stemming from the Pilot Operator.	Corresponding to all communication states related to PLO.
Events related to DEM (events_dem)	Highlighting the directives, feedback, and decisions associated with the decision-making entity mission planning cell.	Corresponding to all communication states related to DEMPC.
AVO controls (avo_controls)	Emphasizing the specific control mechanisms and inputs the AVO employs, from adjusting airspeed to setting routes.	Corresponding to the control states related to AVO.
PLO controls (plo_controls)	This layer dives into the control mechanisms the PLO utilizes, from setting camera parameters to requesting flight plans.	Corresponding to the control states related to PLO.
DEM controls (dem_controls)	Enumerating the control actions undertaken by the DEM, including aspects like setting altitudes and refueling commands.	Corresponding to the control states related to DEMPC.

Note. This table summarizes the roles, interactions, and control mechanisms of the Air Vehicle Operator (AVO), Pilot Operator (PLO), and Decision-making Entity Mission Planning Cell (DEMPC), corresponding to their respective communication and control states.

Cook’s distance evaluation found Team 5 to have a Cook’s distance in the 95th percentile, indicating a highly influential observation. Team 5 was removed from subsequent analyses. The results indicated that approximately 91.9% of the variance in the dependent variable can be explained by the model (R2 = .911, F (11, 8) = 7.470, p = .004). After adjusting for the number of predictors, the adjusted R2 was .789, indicating that the model accounts for approximately 78.9% of the variance in trust change when considering the number of predictors included. Specifically, “turning_events” (β = 166.556, p < .001) and “resource_events” (β = 64.470, p = .037) were significant predictors of trust score, with “turning_events” showing a particularly strong positive association. In contrast, “warning_alarm_events” (β = −31.854, p = .049) and “avo_controls” (β = −211.733, p = .014) were found to be negatively associated with the dependent variable, with “avo_controls” showing a notably larger negative effect. The distinction between synthetic and human teammates significantly predicted outcomes (β = −44.748, p < .001), indicating that being in a team with the synthetic teammate was associated with a decrease in the trust score (see Table 2).

Collinearity diagnostics revealed that several variables exhibited VIF values above the commonly used threshold of 5, suggesting multicollinearity. Specifically, “avo_controls” (VIF = 13.527) and “dem_controls” (VIF = 11.010) had elevated VIF values, with the types of teammates (Condition) giving a VIF of 11.451. These findings suggest that the variance of the coefficients for these predictors could be inflated due to multicollinearity. To mitigate multicollinearity, a Principal Component Analysis (PCA) with Varimax rotation was conducted. Three components with eigenvalues over 1 were retained, explaining 77.79% of cumulative variance (49.57%, 16.58%, and 11.64% respectively). The rotated component matrix is shown in Table 3.

Table 3.

Regression Analysis Summary for Trust Score (n = 20).

Predictor	Unstandardized B	Standardized Beta	SE	p	Tolerance	VIF
(Constant)	−98.460		44.607	.058
warning_alarm_events	−31.854	−.412	13.739	.049	.352	2.841
events_alt_air	−10.182	−.130	16.224	.548	.259	3.865
turning_events	166.556	1.496	27.143	<.001	.187	5.357
resource_events	64.470	.442	25.755	.037	.355	2.817
events_avo	7.698	.097	12.246	.547	.466	2.144
events_plo	16.511	.333	10.495	.154	.248	4.033
events_dem	−30.474	−.444	18.828	.144	.147	6.784
avo_controls	−211.733	−1.207	67.975	.014	.074	13.527
plo_controls	−39.699	−.212	36.088	.303	.300	3.337
dem_controls	357.320	.426	292.950	.257	.091	11.010
condition	−44.748	−2.064	7.728	<.001	.087	11.451

Note. Beta = Standardized regression coefficient; VIF = Variance Inflation Factor. The dependent variable is trust score.

After reducing the dimensionality of the predictors, a final multiple regression analysis was conducted to predict the trust score using the three principal components derived from the initial set of variables. The overall model was not significant (R² = .250, F (3, 16) = 1.780, p = .191), but component 2 reached statistical significance (β = 5.163, p = .047) (see Table 4). According to the component matrix, warning alarm events (.323), turning events (.763), resource events (.854), and condition (synthetic teammates vs human teammates) (.558) positively loaded on Component 2. This finding echoes the results of the initial regression model (see Table 5).

Table 4.

Final Regression Model Based on the PCA Result.

Components	Unstandardized B	Standardized coefficients beta	SE	p
(Constant)	−3.565		2.335	.146
Component 1	.135	.012	2.396	.956
Component 2	5.163	.467	2.396	.047
Component 3	1.995	.180	2.396	.417

Note. Dependent variable: trust score.

Table 5.

Rotated Component Matrix.

System layers	Component 1	Component 2	Component 3
warning_alarm_events	−.424	.323	.596
events_alt_air	.251	−.584	−.512
turning_events	−.483	.763	−.027
resource_events	.204	.854	.041
events_avo	.059	−.258	.822
events_plo	.436	−.696	.230
events_dem	.733	−.501	−.006
avo_controls	.854	−.176	−.297
plo_controls	.880	.013	.083
dem_controls	.868	−.047	−.395
condition	−.739	.558	−.024

Note. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Discussion

This research explored the dynamics of trust in HAT, focusing on identifying potential correlations between dynamical activities and interactions with autonomous systems and trust levels.

Hypothesis 1 proposed a correlation between trust in automation and interactions with the autonomous system. The final regression model, using principal components derived from PCA, addressed various interaction metrics. Although the overall model was not significant, Component 2 showed a statistical correlation, indicating a potential relationship between trust and vehicle states, specifically warning alarm events, turning events, and resource events. Regarding Hypothesis 2 (trust in teammates will be influenced by the type of teammate), Component 2 indicated a correlation with fluctuations in trust due to changes in teammate conditions. This supports the notion of trust as a dynamic and context-dependent variable in HAT systems.

These findings align with emerging trends in human-automation interaction research, suggesting that machine data, complemented by human data, could offer a more comprehensive understanding of trust dynamics (Yang et al., 2023). The study provides empirical support for the theory that trust dynamics in HAT are significantly influenced by the nature of interactions and the context in which they occur. The results also provide insights into humans’ perceptions of their teammates as human or AI, echoing findings by Feng et al. (2019) and Merritt and McGee (2012). There may be a trend where, in high-risk environments, humans demonstrate greater trust in perceived AI entities than in human counterparts.

Limitations of the study include the modest sample size and the focus solely on relation-based trust measurement tasks. Future research should explore how continuous interactions are associated with changes in trust, whether it strengthens, decays, or stabilizes. While this study has uncovered relationships between trust levels and human-AI team interactions, it did not delve deeply into the dynamic nature and multidimensional characteristics of trust. Future research should explore the nuances of incremental trust changes and possibly employ more frequent and diverse trust measurement methodologies.

In conclusion, the findings provide partial evidence supporting significant fluctuations in trust in automation based on continuous interactions. Understanding these dynamic changes is important for designing AI systems that are intelligent, reliable, and capable of adapting to user needs and expectations.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Exploring Dynamic Influences on Trust in Human-Autonomy Teaming: An Entropy and Event Layer Analysis

Abstract

Keywords

Introduction

The Current Study

Methods

Measures

Results

Discussion

Footnotes

Declaration of Conflicting Interests

Funding

References