Utilizing ACT-R to investigate interactions between working memory and visuospatial attention while driving

Abstract

In an effort towards improving the safety in everyday traffic, adaptive automation has emerged as a promising technology in recent years. A key step in this approach is the accurate prediction of momentary cognitive workload while driving. Previous research has found an interaction between working memory load and visuospatial attention complicating the accurate predicition for these cognitive concepts. We have developed an ACT-R model to investigate the nature of the interaction and improve the prediction accuracy for working memory load and visuospatial attention while driving. This ACT-R model is driving on a multi-lane highway with concurring traffic and alternating lane-widths while doing a secondary n-back task using speed signs. Furthermore, it is able to handle complex driving situations like overtaking traffic and adjusting its speed according to the n-back task. The behavioral results show an increase in error rates in the secondary task with increasing n-back difficulty as well as a decrease in driving performance with increasing difficulty in the n-back task. The results of the model indicate an interaction at a common task-unspecific level.