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Recently learned environmental spaces are represented as interconnected vista spaces

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84081

Meilinger,  T
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84170

Riecke,  BE
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Meilinger, T., Riecke, B., & Bülthoff, H. (2011). Recently learned environmental spaces are represented as interconnected vista spaces. Cognition.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B918-6
Abstract
In two experiments we examined orientation dependency in human memory for environmental spaces such as cities or buildings. Participants learned an immersive virtual environment by repeatedly walking a multi-segment route in one direction. The environment consisted of seven connected corridors within which target objects were located. In the testing phase, participants were teleported to different locations in the environment, and were asked to identify their location and heading and then point towards previously learned targets. As predicted by view-dependent theories, participants pointed more accurately when oriented in the direction in which they originally learned each corridor. Being aligned with the learning direction also resulted in faster self-localization, but only when participants could see the whole corridor during testing, and not when only their immediate surrounding was visible. No support was found for a global reference direction underlying the memory of the whole layout or for an exclusive orientation-independent memory. As neither of the existing theories on human memory could completely explain the results, we propose a "network of reference frames" theory to integrate elements of the different theoretical positions and explain the current data.