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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.

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

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

Map

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox

 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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