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  Self-similarity and recursion as default modes in human cognition

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-8042-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C5B9-E
Genre: Journal Article

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 Creators:
Fischmeister, Florian P.1, 2, Author
Martins, Mauricio3, 4, 5, Author              
Beisteiner, Roland1, 2, Author
Fitch, W. Tecumseh3, Author
Affiliations:
1Department of Neurology, Medical University of Vienna, Austria, ou_persistent22              
2MR Centre of Excellence, Medical University of Vienna, Austria, ou_persistent22              
3Department of Cognitive Biology, University Vienna, Austria, ou_persistent22              
4Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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Free keywords: Recursion; Hierarchies; Cognition; Default-mode; fMRI
 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.

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Language(s): eng - English
 Dates: 2016-02-152015-11-032016-08-192016-09-232017-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: BibTex Citekey: Fischmeister2016
DOI: 10.1016/j.cortex.2016.08.016
PMID: 27780529
Other: Epub 2016
 Degree: -

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Project name : -
Grant ID : FG761002
Funding program : Research Cluster Grant “Shared Neural Resources for Music and Language”
Funding organization : University of Vienna
Project name : -
Grant ID : FA103FC003
Funding program : Research Cluster Grant “Shared Neural Resources for Music and Language”
Funding organization : Medical University of Vienna
Project name : -
Grant ID : SFRH/BD/64206/2009
Funding program : -
Funding organization : Fundação para a Ciência e a Tecnologia
Project name : The Syntax of the Mind: A Comparative Computational Approach / SOMACCA
Grant ID : 230604
Funding program : Funding Programme 7
Funding organization : European Commission (EC)

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Title: Cortex
  Other : Cortex
Source Genre: Journal
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Publ. Info: Milan [etc.] : Elsevier Masson SAS
Pages: - Volume / Issue: 97 Sequence Number: - Start / End Page: 183 - 201 Identifier: ISSN: 0010-9452
CoNE: https://pure.mpg.de/cone/journals/resource/954925393344