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Updated: May 12, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
Spatiotemporal survival analysis for movement trajectory tracking in virtual reality
Omar Fahmi Jubran1, Maximilian Philipp Wolkersdorfer2, Vera Eymann3
1Center for Cognitive Science, University of Kaiserslautern-Landau, 67663, Kaiserslautern, Germany. ojubran@rptu.de.
Abstract:
We present a novel method for analyzing response trajectory tracking data. Limiting behavioral experiments to discrete, key-press response measures, such as reaction times and accuracy, is unsatisfactory for observing the ongoing dynamics of cognition. We assessed the utility of continuous response tracking in Virtual Reality (VR) by comparing it to key-press responses in a classical N-back matching task. For elucidatory purposes, in both classical and VR versions of the task we first worked through analyses of discrete measures, before drawing information from the continuous trajectory tracking data in VR. Classical ANOVAs reproduced effects of visual working memory load in an N-back task. Violations of ANOVA assumptions suggested effects were buried in the noise; some of these were revealed in subsequent survival analyses, namely frequency neglect (a strong preference for match responses despite the infrequency of this response category) in the fast responses and category frequency-tuned response in the slow responses. Spatiotemporal survival analysis (StSA), our newly proposed method of analyzing response trajectories, revealed that all these effects also occur in the VR conditions. In addition, initial divergences towards the wrong responses were corrected later in the course of trajectories in the non-Match trials. While the StSA method is theory-free and can be used for exploratory purposes, we present examples of hypothesis testing in StSA.
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