Literature DB >> 23850467

The interaction between attention and motor prediction. An ERP study.

Alexander Jones1, Gethin Hughes, Florian Waszak.   

Abstract

Performing a voluntary action involves the anticipation of the intended effect of that action. Interaction with the environment also requires the allocation of attention. However, the effects of attention upon motor predictive processes remain unclear. Here we use a novel paradigm to investigate attention and motor prediction orthogonally. In an acquisition phase, high and low tones were associated with left and right key presses. In the following test phase, tones were presented at random and participants attended to only one ear whilst ignoring tones presented in the unattended ear. In the test phase a tone could therefore be presented at the attended or unattended ear, as well as being congruent or incongruent with prior action-effect learning. We demonstrated early and late effects of attention as well as a later independent motor prediction effect with a larger P3a for incongruent tones. Interestingly, we demonstrated an intermediate interaction, showing an action-effect negativity (NAE) for tones which were unattended, whilst no motor prediction effect was found for attended tones. This interaction pattern suggests that attention and motor prediction are not opposing processes but can both operate to modulate prediction, providing valuable new insight into the relationship between attention and the effects of motor prediction.
© 2013.

Entities:  

Keywords:  Action effect; Auditory; EEG/ERP; Motor prediction; Selective attention

Mesh:

Year:  2013        PMID: 23850467     DOI: 10.1016/j.neuroimage.2013.07.004

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


  8 in total

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Review 4.  Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges.

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Review 5.  Does Motor Simulation Theory Explain the Cognitive Mechanisms Underlying Motor Imagery? A Critical Review.

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Review 6.  Challenges to the Modularity Thesis Under the Bayesian Brain Models.

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Authors:  Tsukasa Kimura; Jun'ichi Katayama
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8.  Intention-based and sensory-based predictions.

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Journal:  Sci Rep       Date:  2021-10-06       Impact factor: 4.379

  8 in total

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