Projective Shape Analysis for Spatial Orientation in Virtual Environments

Music City Center 

In this talk, we introduce and develop a projective shape analysis for the study of cognitive abilities evaluated based on learning behaviour in the DSNT (Dresden Spatial Navigation Task) virtual navigational experiment ([1]). DSNT adapts the classical water maze test for humans, and was developed at DZNE (The Research Institute for Neurodegenerative Diseases from Dresden, Germany). This new mathematical modelling of the spatial orientation and learning is based on recent concepts in object-oriented data analysis like extrinsic covariance and extrinsic cross-covariance as well as novel statistical testing methods for random objects on manifolds ([2]). Additionally, new numerical algorithms will be developed, studied and finally implemented in an open-source mathematical software like R and will be used to evaluate our conclusions and to present the data visually.

Bibliography
1. Garthe A., Kempermann G., An old test for new neurons: refining the Morris water maze to study the functional relevance of adult hippocampal neurogenesis, Front. in Neuro., 7 (2013).
2. Wong K.C., Patrangenaru V., Paige R.L.,Pricop Jeckstadt M., Extrinsic Principal Component Analysis, arXiv (2024).

object-oriented data analysis

projective shape,



nonparametric statistics,



spatial learning, virtual reality 

Section on Nonparametric Statistics