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Self-similarity and recursion as default modes in human cognition

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Martins,  Mauricio
Department of Cognitive Biology, University Vienna, Austria;
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Fischmeister, F. P., Martins, M., Beisteiner, R., & Fitch, W. T. (2017). Self-similarity and recursion as default modes in human cognition. Cortex, 97, 183-201. doi:10.1016/j.cortex.2016.08.016.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-8042-3
Abstract
Humans generate recursive hierarchies in a variety of domains, including linguistic, social and visuo-spatial modalities. The ability to represent recursive structures has been hypothesized to increase the efficiency of hierarchical processing. Theoretical work together with recent empirical findings suggests that the ability to represent the self-similar structure of hierarchical recursive stimuli may be supported by internal neural representations that compress raw external information and increase efficiency. In order to explicitly test whether the representation of recursive hierarchies depends on internalized rules we compared the processing of visual hierarchies represented either as recursive or non-recursive, using task-free resting-state fMRI data. We aimed to evaluate the relationship between task-evoked functional networks induced by cognitive representations with the corresponding resting-state architecture. We observed increased connectivity within Default Mode Network (DMN) related brain areas during the representation of recursion, while non-recursive representations yielded increased connectivity within the Fronto-Parietal Control-Network. Our results suggest that human hierarchical information processing using recursion is supported by the DMN. In particular, the representation of recursion seems to constitute an internally-biased mode of information-processing that is mediated by both the core and dorsal-medial subsystems of the DMN. Compressed internal rule representations mediated by the \DMN\ may help humans to represent and process hierarchical structures in complex environments by considerably reducing information processing load.