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ViMoAct SIGNED

Modelling cortical information flow during visuomotor adaptation as active inference in the human brain

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 ViMoAct project word cloud

Explore the words cloud of the ViMoAct project. It provides you a very rough idea of what is the project "ViMoAct" about.

free    hierarchy    updated    lacks    precision    hierarchical    delayed    errors    suggests    requiring    tracking    inference    dynamic    explains    manipulated    fmri    coding    assumption    recent    empirical    optimal    relative    spectral    experimentally    model    generative    relies    gap    move    appeals    proprioceptive    thereby    movements    perform    multiple    conflicts    experiment    environment    modelled    visual    optimise    sensory    tested    active    bayesian    visuoproprioceptive    function    visuomotor    allocation    self    virtual    investigation    either    predictions    predictive    principles    determines    movement    noise    levels    hemodynamic    feedback    close    error    endogenous    brain    exchange    weighting    photorealistic    updating    compatible    motor    data    stimulus    previously    representation    filtering    meg    public    bodily    world    flow    suppression    prediction    causal    predicted    generalised    formal    belief    bayes    instructed    attentional    cognitive    interdisciplinary    follows    glove    experiments    mr    cortical    contribution    actions    models    energy    manual   

Project "ViMoAct" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY COLLEGE LONDON 

Organization address
address: GOWER STREET
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 183˙454 €
 EC max contribution 183˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-11-01   to  2020-05-02

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 183˙454.00

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 Project objective

Recent research suggests that to control bodily movements the brain relies on Bayes-optimal predictive models that are updated by sensory prediction error. This assumption may be generalised within a new formal account of motor control as active (Bayesian) inference. Active inference explains motor control in terms of hierarchical Bayesian filtering or predictive coding, i.e., as belief updating and suppression of prediction error to optimise a hierarchical generative model in the brain; thereby the weighting of prediction errors by their predicted precision determines their relative impact on hierarchical inference. This novel proposal still lacks concrete empirical investigation. The proposed project will close this research gap by testing whether cortical information flow during manual actions, requiring visuomotor adaptation and cognitive control of attention, follows the principles of active inference. In two fMRI experiments and one MEG experiment, participants will move a photorealistic virtual hand model via an MR-compatible data glove to perform simple manual tracking tasks in a virtual reality environment. The precision of prediction errors at multiple levels of a previously established cortical motor control hierarchy will be experimentally manipulated via visuoproprioceptive conflicts (introduced by delayed visual movement feedback) and via attentional allocation – either stimulus-driven (via increased sensory noise) or endogenous (instructed) – to visual or proprioceptive movement feedback. Active inference’s specific predictions about information flow between and within cortical areas will be tested with recently established dynamic causal modelling of the modelled hemodynamic (fMRI) or spectral (MEG) responses. Active inference appeals to a general free-energy principle of brain function; this contribution will thus promote interdisciplinary exchange of knowledge about self- and world-representation in the brain and will be of general public interest.

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The information about "VIMOACT" are provided by the European Opendata Portal: CORDIS opendata.

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