Toward a Unified Theory of Learned Trust in Interpersonal and Human-Machine Interactions

Modelling attention allocation and takeover performance in two-stage takeover system via a cognitive computational model: considering the role of multiple monitoring requests

Studies have demonstrated two-stage takeover systems' feasibility and advantages. However, existing cognitive models mainly focus on simulating drivers' performance in single-stage takeover systems, with limited insights into cognitive modelling of effects of monitoring requests (MRs) within two-stage takeover systems. This study constructed a cognitive computational model for two-stage takeover systems based on queueing network-adaptive control of thought rational (QN-ACTR) architecture. Our model aims to capture variations in drivers' attention allocation and takeover performance resulting from different MR experiences. Five components, representing distinct cognitive processes, were designed to closely align with drivers' behavioural patterns. This model was validated through an experiment using metrics such as percentage time in road-centre and takeover time. Results revealed significant concordance between the model predictions and experimental data, with R-squared ≥ 0.76, RMSE ≤ 0.41, and MAPE ≤ 15%. The findings of this work extended beyond the two-stage takeover system investigation to include human factor modelling.

Trust within human-machine collectives depends on the perceived consensus about cooperative norms

With the progress of artificial intelligence and the emergence of global online communities, humans and machines are increasingly participating in mixed collectives in which they can help or hinder each other. Human societies have had thousands of years to consolidate the social norms that promote cooperation; but mixed collectives often struggle to articulate the norms which hold when humans coexist with machines. In five studies involving 7917 individuals, we document the way people treat machines differently than humans in a stylized society of beneficiaries, helpers, punishers, and trustors. We show that a different amount of trust is gained by helpers and punishers when they follow norms over not doing so. We also demonstrate that the trust-gain of norm-followers is associated with trustors' assessment about the consensual nature of cooperative norms over helping and punishing. Lastly, we establish that, under certain conditions, informing trustors about the norm-consensus over helping tends to decrease the differential treatment of both machines and people interacting with them. These results allow us to anticipate how humans may develop cooperative norms for human-machine collectives, specifically, by relying on already extant norms in human-only groups. We also demonstrate that this evolution may be accelerated by making people aware of their emerging consensus.

Trust Miscalibration Is Sometimes Necessary: An Empirical Study and a Computational Model

The literature on trust seems to have reached a consensus that appropriately calibrated trust in humans or machines is highly desirable; miscalibrated (i.e., over- or under-) trust has been thought to only have negative consequences (i.e., over-reliance or under-utilization). While not invalidating the general idea of trust calibration, a published computational cognitive model of trust in strategic interaction predicts that some local and temporary violations of the trust calibration principle are critical for sustained success in strategic situations characterized by interdependence and uncertainty (e.g., trust game, prisoner’s dilemma, and Hawk-dove). This paper presents empirical and computational modeling work aimed at testing the predictions of under- and over-trust in an extension of the trust game, the multi-arm trust game, that captures some important characteristics of real-world interpersonal and human-machine interactions, such as the ability to choose when and with whom to interact among multiple agents. As predicted by our previous model, we found that, under conditions of increased trust necessity, participants actively reconstructed their trust-investment portfolios by discounting their trust in their previously trusted counterparts and attempting to develop trust with the counterparts that they previously distrusted. We argue that studying these exceptions of the principle of trust calibration might be critical for understanding long-term trust calibration in dynamic environments.

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.