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Predicting Navigation Performance with Psychophysiological Responses to Threat in a Virtual Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8021)

Abstract

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 regression (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 human-computer interaction and psychophysiologically-based adaptive systems are discussed.

Keywords

Psychophysiology Threat Simulation Navigation Route-Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of Southern CaliforniaUSA
  2. 2.Institute for Creative TechnologiesUniversity of Southern CaliforniaUSA
  3. 3.Department of PsychologyUniversity of ArizonaUSA
  4. 4.Department of PsychologyUniversity of North TexasUSA

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