Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching.

OBJECTIVE

The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible.

BACKGROUND

The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed.

METHOD

In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker.

RESULTS

Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation.

CONCLUSIONS

The multiattribute decision model provided a good fit to the data.

APPLICATIONS

The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.