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SUMMARY:Contributed Talk: Projective Shape Analysis of Spatial Orientation
  - Mihaela Pricop-Jeckstadt (University Politehnica of Bucharest)
DTSTART:20260212T133000Z
DTEND:20260212T140000Z
UID:TALK242347@talks.cam.ac.uk
DESCRIPTION:In this talk\, we apply extrinsic data analysis to the study o
 f cognitive abilities evaluated based on the learning behavior in the Dres
 den Spatial Navigation Task (DSNT) virtual navigational experiment. Projec
 tive Shape Analysis for Spatial Orientation (PSASO) is our novel mathemati
 cal modelling of spatial orientation and spatial learning that transforms 
 the 2D trajectory analysis into a 3D moving landmark problem. It is based 
 on recent concepts in object-oriented data analysis like extrinsic mean an
 d extrinsic covariance as well as novel statistical testing methods for ra
 ndom objects on manifolds. The allocentric orientation patterns in persons
  exhibiting mild cognitive impairment (MCI) and controls are detected for 
 the first time. Our research examines how trajectory patterns in the DSNT 
 reveal the interplay between spatial learning and spatial long-term memory
 \, as well as the landmark-based orientation. The one sample test for the 
 extrinsic mean suggests a classification of the landmarks in three classes
 : ''remembered'' landmarks\, ''forgettable'' landmarks and ''unlearnable''
  landmarks. The orientation pattern of the MCI group displays mostly ''unl
 earnable'' or ''forgettable'' landmarks. Finally\, the prediction of the M
 CI condition by Ada boosting proves an accuracy of 95% and offers hope for
  a new diagnostics tool for neurodegenerative diseases.
LOCATION:Seminar Room 1\, Newton Institute
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