Change detection support for supervisory controllers of highly automated systems: Effects on performance, mental workload, and recovery of situation awareness following interruptions

Abstract

a b s t r a c tDynamic Positioning (DP) is a computer-controlled process to automatically keep a floating vessel at a specific position or to follow a pre-defined path (tracking) by using its own propellers and thrusters. The human supervisory controller has no direct need to constantly know what the status is of all parts of the automation and the system it is controlling, because the highly automated DPS is controlling all components itself. Only after a failure arises, the operator needs to take over manual control and take appropriate action(s) to prevent the failure from harming the operation. As the supervisory controller may be out of the loop, swiftly taking over control may be problematic when failures arise. The purpose of the current study was to investigate whether automation of change detection enables human operators with low awareness of the automation and the system it is controlling to quickly recover awareness in emergency take-over situations. A 2 by 2 within subjects experiment was conducted using a DP simulation (n ¼ 22). Within-subjects factors were support (Yes, No) and interruption (Yes, No). Results showed that change detection support helps in the process of recovering situation awareness after it has been reduced, due to an interruption of the primary task of overseeing the automation. Interestingly, support was not beneficial to the participants in all conditions. In non-interrupted conditions the support unexpectedly resulted in higher workload, raising questions whether supervisory controllers should be supported continuously or only when it is required. Relevance to industry: The results show that change detection support has potential value in operational maritime environments, especially in situations where the DP operator has low situation awareness. Future research should investigate whether adaptive aiding could alleviate some of the negative effects of non-adaptive operator support in maritime environments.

Effects of coherent, integrated, and context-dependent adaptable user interfaces on operators’ situation awareness, performance, and workload

“…Several operators indicated that they would prefer more elaborate forms of prognosis information and information about alternative routes. However, previous research also indicates that complex change detection for SA support can result in higher operator workload (van der Kleij et al 2018). It, therefore, is questionable whether more elaborated context-dependent information visualization would result in even better support for operators' SA, workload, and task performance.…”

“…In case of our study, this could have explained our findings if the workload of operators' working with a coherent UI was all right, while the workload with the other two UI was either too high or too low. Adding support to an already highly automated system should be done with caution as under continuous, non-interrupted conditions, the change detection for SA support can result in higher operator workload (van der Kleij et al 2018). Since no significant difference in workload was found, this apparently was not the case.…”

The role of supervisor work-life support on the correlation between work autonomy, workload and performance: perspective from Tanzania banking employees

“…Employees who receive more emotional instrumental support from their work supervisors in coping with life and family concerns can less prone to experience job interfering with their personal lives and causing them to feel burdened, even when the workload is high [2,58]. Workers perceive supervisory support as an indication that their bosses care about their happiness and their needs, thus decreasing the resource depletion caused by work-family conflict and job demands (workload) [59].…”

Eye Tracking to Evaluate the Effects of Interruptions and Workload in a Complex Task

“…Typical solutions involve presenting users with an event timeline or log of changes (Hodgetts et al, 2015), an “instant replay” of events (e.g., St. John et al, 2005), or an indicator next to the changed values (van der Kleij et al, 2018) to support operators in their resumption of the primary task after an interruption. The theoretical underpinning for such tools is the Memory for Goals theory (MfG; Altmann & Trafton, 2002), which suggests that goal activation levels decay with time, with the current goal having the highest activation level.…”