4.6 Article

Self-similarity and recursion as default modes in human cognition

Journal

CORTEX
Volume 97, Issue -, Pages 183-201

Publisher

ELSEVIER MASSON, CORP OFF
DOI: 10.1016/j.cortex.2016.08.016

Keywords

Recursion; Hierarchies; Cognition; Default-mode; fMRI

Funding

  1. University of Vienna [FG761002]
  2. FCT grant [SFRH/BD/64206/2009]
  3. ERC Advanced Grant SOMACCA [230604]
  4. Medical University of Vienna [FA103FC003]
  5. Fundação para a Ciência e a Tecnologia [SFRH/BD/64206/2009] Funding Source: FCT

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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. (C) 2016 Elsevier Ltd. All rights reserved.

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