Literature DB >> 27288320

Towards an understanding of the neural dynamics of intentional learning: Considering the timescale.

Hannes Ruge1, Uta Wolfensteller2.   

Abstract

Recently, Hampshire et al. (2016) published a paper in NeuroImage investigating the involvement of frontal networks in two types of 'intentional learning'. This included the standard type of deterministic feedback-driven trial-and-error learning and another type of intentional learning that has recently been studied in various facets by means of neuroimaging methods under the terms 'instruction-based learning' (Ruge and Wolfensteller, 2010) or 'rapid instructed task learning' (Cole et al., 2010). By differentiating the learning-related functional roles of different lateral frontal cortex networks and the anterior striatum, Hampshire et al. (2016) contributed valuable results to the field. The aim of this commentary is to increase the interpretability of some of their findings by connecting them to what is already known about fronto-striatal activation dynamics and its functional couplings based on related previous studies. We start with an overview of the rapidly diversifying neuroimaging research on the intentional control of learning and behaviour and its historical embedding. Based thereon we discuss ways to reconcile and integrate the new results presented by Hampshire et al. (2016) particularly regarding the nature of fronto-striatal activation dynamics and their functional couplings during instruction-based learning and during deterministic trial-and-error learning. We conclude that it is important to assess neural activation dynamics on multiple time scales in order to characterize short-term learning and automatization processes as they are evolving across the initial learning trials and further across more extended periods of practice trials.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2016        PMID: 27288320     DOI: 10.1016/j.neuroimage.2016.06.006

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  5 in total

1.  When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

Authors:  Yiquan Shi; Uta Wolfensteller; Torsten Schubert; Hannes Ruge
Journal:  Hum Brain Mapp       Date:  2017-11-02       Impact factor: 5.038

2.  Neural representation of newly instructed rule identities during early implementation trials.

Authors:  Hannes Ruge; Theo Aj Schäfer; Katharina Zwosta; Holger Mohr; Uta Wolfensteller
Journal:  Elife       Date:  2019-11-18       Impact factor: 8.140

3.  Integration and segregation of large-scale brain networks during short-term task automatization.

Authors:  Holger Mohr; Uta Wolfensteller; Richard F Betzel; Bratislav Mišić; Olaf Sporns; Jonas Richiardi; Hannes Ruge
Journal:  Nat Commun       Date:  2016-11-03       Impact factor: 14.919

4.  Deterministic response strategies in a trial-and-error learning task.

Authors:  Holger Mohr; Katharina Zwosta; Dimitrije Markovic; Sebastian Bitzer; Uta Wolfensteller; Hannes Ruge
Journal:  PLoS Comput Biol       Date:  2018-11-29       Impact factor: 4.475

5.  Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning.

Authors:  Magdalena W Sliwinska; Inês R Violante; Richard J S Wise; Robert Leech; Joseph T Devlin; Fatemeh Geranmayeh; Adam Hampshire
Journal:  J Neurosci       Date:  2017-07-04       Impact factor: 6.167

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.