WorthaEtAl_SemideusTraiingfMRI_accepted.pdf (870.74 kB)
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Neurofunctional plasticity in fraction learning: An fMRI training study

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journal contribution
posted on 2020-10-19, 09:16 authored by SM Wortha, J Bloechle, M Ninaus, K Kiili, A Lindstedt, Julia BahnmuellerJulia Bahnmueller, Korbinian MoellerKorbinian Moeller, E Klein
© 2020 Elsevier GmbH Background: Fractions are known to be difficult for children and adults. Behavioral studies suggest that magnitude processing of fractions can be improved via number line estimation (NLE) trainings, but little is known about the neural correlates of fraction learning. Method: To examine the neuro-cognitive foundations of fraction learning, behavioral performance and neural correlates were measured before and after a five-day NLE training. Results: In all evaluation tasks behavioral performance increased after training. We observed a fronto-parietal network associated with number magnitude processing to be recruited in all tasks as indicated by a numerical distance effect. For symbolic fractions, the distance effect on intraparietal activation was only observed after training. Conclusion: The absence of a distance effect of symbolic fractions before the training could indicate an initially less automatic access to their overall magnitude. NLE training facilitates processing of overall fraction magnitude as indicated by the distance effect in neural activation.

History

School

  • Science

Department

  • Mathematics Education Centre

Published in

Trends in Neuroscience and Education

Volume

21

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Trends in Neuroscience and Education and the definitive published version is available at https://doi.org/10.1016/j.tine.2020.100141

Acceptance date

2020-08-26

Publication date

2020-09-01

Copyright date

2020

ISSN

2211-9493

Language

en

Depositor

Dr Julia Bahnmuller . Deposit date: 19 October 2020

Article number

100141