The present study examined the physiological responses collected during a route-learning and subsequent navigation task in a novel virtual environment. Additionally, participants were subjected to varying levels of environmental threat during the route-learning phase of the experiment to assess the impact of threat on consolidating route and survey knowledge of the directed path through the virtual environment. Physiological response measures were then utilized to develop multiple linear

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... ion (MLR) and artificial neural network (ANN) models for prediction of performance on the navigation task. Comparisons of predictive abilities between the developed models were performed to determine optimal model parameters. The ANN models were determined to better predict navigation performance based on psychophysiological responses gleaned during the initial tour through the city. The selected models were able to predict navigation performance with better than 80% accuracy. Applications of the models toward improved humancomputer interaction and psychophysiologically-based adaptive systems are discussed.