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Bernard N'Kaoua; Jérôme Rodrigues; Hélène Sauzéon; Grégory Wallet (2009)
Publisher: University of Applied Sciences Dusseldorf
Journal: Journal of Virtual Reality and Broadcasting
Languages: English
Types: Article
Subjects: Virtual reality, Spatial knowledge [swd], //dewey.info/class/004/ [http], Exploration mode, Instruments and machines, DOAJ:Technology and Engineering, Mathematics, Route [swd], Spatial cognition, Exocentric/Egocentric referential/strategy, Virtual reality [swd], DOAJ:Computer Science, Q, Knowledge transfer, 004, Route complexity, Electronic computers. Computer science, Science, Know-how-transfer [swd], QA75.5-76.95, QA1-939, QA71-90, Wayfinding
ddc: ddc:004
The use of virtual reality as tool in the area of spatial cognition raises the question of the quality of learning transfer from a virtual to a real environment. It is first necessary to determine with healthy subjects, the cognitive aids that improve the quality of transfer and the conditions required, especially since virtual reality can be used as effective tool in cognitive rehabilitation. The purpose of this study was to investigate the influence of the exploration mode of virtual environment (Passive vs. Active) according to Route complexity (Simple vs. Complex) on the quality of spatial knowledge transfer in three spatial tasks. Ninety subjects (45 men and 45 women) participated. Spatial learning was evaluated by Wayfinding, sketch-mapping and picture classification tasks in the context of the Bordeaux district. In the Wayfinding task, results indicated that active learning in a Virtual Environment (VE) increased the performances compared to the passive learning condition, irrespective of the route complexity factor. In the Sketch-mapping task, active learning in a VE helped the subjects to transfer their spatial knowledge from the VE to reality, but only when the route was complex. In the Picture classification task, active learning in a VE when the route was complex did not help the subjects to transfer their spatial knowledge. These results are explained in terms of knowledge levels and frame/strategy of reference [SW75, PL81, TH82].

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