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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.

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

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

 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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