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Abstract

Mental Rotation (i.e. the ability to mentally rotate representations of 2D and 3D objects) and egocentric Perspective Taking (i.e. the ability to adopt an imagined spatial perspective) represent the two most well-known and used types of spatial transformation. Yet, these two spatial transformations are conceptually, visually, and mathematically equivalent. Thus, an active debate in the field is whether these two types of spatial transformations are cognitively and neurally distinct or whether they represent different manifestation of the same underlying core mental process. In this study, we utilize a machine learning approach to extract neural activity from electroencephalography (EEG) measures and identify neural differences between mental rotation and perspective taking tasks. Our results provide novel empirical evidence in support of the view that these two types of spatial transformation correspond to district cognitive processes at the neural level. Importantly, the proposed framework provides a novel approach that can facilitate the study of the neural correlates of spatial cognition.