Multi-Tasking and Cognitive Workload in an ACT-R Model of a Simplified Air Traffic Control Task

Abstract

We present a human performance model of operator control of a simplified air traffic control task developed under the Agent-based Modeling and Behavior Representation program (AMBR). The model was implemented using the ACT-R architecture of cognition. Using a well-developed cognitive architecture provided a number of benefits: 1. The reuse of common design patterns, such as unit task decomposition and retrieval/computation dichotomy, greatly simplified and accelerated model development. 2. The model inherited parameter values from previous models and required no fine-tuning of parameters. 3. The architectural learning mechanisms provided automatic learning of situations. The resulting model was quite simple, consisting of only five declarative chunks of information and 36 production rules, while providing a highly accurate model of human performance. The model matched a wide range of performance measures, including amount and type of errors, response latency and choice percentages. Performance variability is a fundamental aspect of human behavior in complex task. The model accounted not only for the average but also for the distribution of performance through the fundamentally stochastic nature of the architecture, amplified by the interaction with the dynamic simulation environment. Although ACT-R is a goal-directed architecture, the model provided a straightforward account of multi-tasking behavior through the use of interruptions triggered by the onset of messages. The model also provided a theory of cognitive workload based on architectural primitives. The port of the model to the High Level Architecture (HLA) was relatively simple and straightforward. Providing a stochastic model of behavior, however, put strong demands on the efficiency of the simulation. The implications of such demands for HLA-enabled human performance models are discussed.