Validating Models of Complex, Real-life Tasks Using fMRI

Abstract

Researchers in human factors and neuroergonomics increasingly use neuroimaging techniques to, for example, apply adaptive automation, investigate mental workload and develop brain-computer interfaces (e.g. Parasuraman, 2003; Parasuraman & Wilson, 2008). For example, Wilson and Russell (2004, 2007) describe a task in which operators were asked to monitor the progress of four independent uninhabited air vehicles (UAVs), download radar images, visually search those images and mark targets. To assist the operators they developed an artificial neural network that used online electroencephalography (EEG) measurements to adapt the difficulty of the task. Especially when tailored to individual operators, this improved performance dramatically (Wilson & Russell. 2007).