Trust, Interruption, and Workload in Multi-AUV Control: Part 2 – Image Classification and Human-AI Benchmarking

Multitasking environments, such as monitoring of multiple underwater drones during live rapid environmental assessment (REA), while performing image classification, require effective cognitive resource management. In this second of two papers, we examine how trust, task-switching demands, workload, auditory environment, affect image classification task performance. We also benchmark a novel machine learning classification algorithm (Bijjahalli et al., 2023) against human performance. Results of our Bayesian multilevel modelling (n=95) found trust in reliable alerts supported task focus but had limited impact on response times. Task-switching interruptions led to a decline in immediate performance, which improved over time, and that image classification accuracy was unaffected by perceived workload, although moderate increases in effort and frustration improved accuracy. We also found that silent operating environments significantly improved image classification accuracy, while female voice alerts were distracting. Machine learning predictions aligned with faster response times and accuracy, supporting their utility in a decision support system.