Human trust in machines is critical for effective human-machine interaction in virtual reality (VR). Prior work defined a three-layered framework of such trust but also indicated two deficiencies. Firstly, there is an absence of a model with metrics spanning all layers to objectively capture fluctuations of the trust (trust dynamics) in real time. Secondly, there is an inadequate consideration of human and machine reliability for the trust. Herein, this study proposed a trust model by defining metrics for all the layers and evaluated this model by considering human and machine reliability. Using objective and subjective data, the evaluation was based on two VR use-cases. The outcomes of the evaluation confirmed the pertinence of the model to capture trust dynamics in the presence of human and machine reliability. The objective data was notably more sensitive to capturing trust dynamics than the subjective counterpart. The model could enable designing trustworthy and adaptive VR.

Measuring cognitive workload (CWL) is crucial for dynamic task reallocation (i.e., adaptation) between a human and a machine in a human-machine system (HMS). A conventional measurement of the CWL is based on subjectively reported scores about the six factors of the NASA Task Load Index (NASA-TLX) questionnaire. The questionnaire cannot however capture real-time fluctuations of the factors for an objective quantification. Additionally, each of the factors is associated with distinct activities and can be influenced by individual characteristics and/or task contexts. Such HMS adaptation should thus consider the objective quantification of each factor. So far, the quantification remains largely unexplored, while existing studies reveal a potential use of an electroencephalography (EEG) in measuring the CWL levels (e.g., high, medium, and low). Herein, we presented a pioneering study to propose EEG features for quantifying the factors. The pertinence of the features was demonstrated by their strong correlations with the scores of the factors across three distinct cases of visuomotor tasks. The pertinence is the stepping stone toward factor-based interventions in enabling HMS adaptation.

Haptic perception of object shape is crucial for humans to interact with machines in human-machine systems (HMS). This perception is prone to disruptions arising from the human and/or machine sides of HMS. An unexplored topic is cognitive processes of the perception. Herein, this study examined the feasibility of measuring the cognitive processes within a virtual environment (i.e., an HMS). Non-invasive electroencephalography was employed to record brain activity of human participants during a task, which was perturbed by disruptions from the human and machine sides. The cognitive processes were measured by using an engagement ratio (ER) and an attention ratio (AR) as physiological metrics, besides behavioral metrics. The results of the study confirmed the feasibility of ER and AR to measure the processes and, in turn, opens an avenue towards elucidating the processes for improving HMS.

Brain machine interfaces (BMI) are becoming increasingly prevalent in diverse applications including motor rehabilitation, virtual reality training, etc. Two critical aspects of an effective BMI are machine reliability and cognitive workload (CWL). Previous studies have reported a notable effect of machine reliability on the 6 factors of the CWL. However, it remains unclear whether this effect can be detected in cognitive processes. Electroencephalography (EEG) is a widely used technique to explore cognitive processes by recording brain activities as signals. Therefore, we utilized the event-related spectral power (ERSP) feature of EEG signals to determine the cognitive processes regarding the effect of machine reliability. The results revealed that machine reliability affected the CWL factor of performance which was reflected in the$y$band activities of the right prefrontal cortex. The findings indicate the potential of cognitive processes in detecting the effect of machine reliability. The detection could pave the way for designing adaptive BMI to balance the machine reliability and the CWL.

Reliability is an important concept contributing to building trust in human-machine systems (HMS). Existing studies have reported separate assessment of human and machine reliability. Thus, there is a gap on considering human and machine reliability concurrently in HMS. To fill the gap, this study investigated the feasibility of such concurrent consideration by using a virtual environment (VE) approach to simulate an HMS. In a developed VE, each human participant performed a task of exploring an invisible surface to perceive its shape, followed by his/her response to a recommendation about the shape made by the VE setting (the machine). Related to human reliability, the perception might be disrupted through a mismatch between the actual shape and force feedback delivered to the participant’s hand. Associated with machine reliability, the recommendation could be incorrect to induce a fault in the setting. Thus, the shape of the invisible surface became an instrument to combine human and machine reliability. The outcomes of the study confirmed the feasibility of combining human and machine reliability in the HMS. Moreover, human reliability might be dominant in the HMS to accomplish the task.