Integration of task networks and cognitive user models using coloured Petri nets and its application to job design for safety and productivity

Abstract

Changes of task demands due to unforeseen events and technological changes can cause variations in job design such as modifications to job procedures and task allocation. Failure to adapt to job design variations can lead to human errors that may have severe consequences for system safety. Existing techniques for task modelling cannot adequately model how task networks can be adapted to changing work conditions and task demands. Therefore, there is a need to integrate task networks with cognitive user models that indicate how operators process information, make decisions, or cope with suspended tasks and errors. The work described here presents a tool for integrating task and cognitive models using coloured Petri nets. The cognitive user model comprises two modules of attention management (selective and divided attention), a module of memory management of suspended tasks and a module of work organization. Performance Shaping Factors (e.g., workload, fatigue and mental-tracking load) are calculated at any point in time to take into account the context of work (e.g., competing activities, errors and suspended tasks). Different types of human error can be modelled for rule-based behaviours required in proceduralized work environments. Simulation analysis and formal analysis techniques can be applied to process control tasks to verify job procedures, workload management strategies and task allocation schemes in response to technological changes and unfamiliar events.