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Journal Article

The suppression of repetition enhancement: A review of fMRI studies

MPG-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons1200

Segaert,  Katrien
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Unification, MPI for Psycholinguistics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons147

Petersson,  Karl Magnus
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Unification, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons69

Hagoort,  Peter
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Unification, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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Segaert_Neuropsychologia_2013.pdf
(Publisher version), 351KB

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Citation

Segaert, K., Weber, K., De Lange, F., Petersson, K. M., & Hagoort, P. (2013). The suppression of repetition enhancement: A review of fMRI studies. Neuropsychologia, 51, 59-66. doi:10.1016/j.neuropsychologia.2012.11.006.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-4139-7
Abstract
Repetition suppression in fMRI studies is generally thought to underlie behavioural facilitation effects (i.e., priming) and it is often used to identify the neuronal representations associated with a stimulus. However, this pays little heed to the large number of repetition enhancement effects observed under similar conditions. In this review, we identify several cognitive variables biasing repetition effects in the BOLD response towards enhancement instead of suppression. These variables are stimulus recognition, learning, attention, expectation and explicit memory. We also evaluate which models can account for these repetition effects and come to the conclusion that there is no one single model that is able to embrace all repetition enhancement effects. Accumulation, novel network formation as well as predictive coding models can all explain subsets of repetition enhancement effects.