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. Fields of science natural sciencescomputer and information sciencessoftwaresoftware applicationsvirtual reality Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2016 - Individual Fellowships Call for proposal H2020-MSCA-IF-2016 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator UNIVERSITY COLLEGE LONDON Net EU contribution € 183 454,80 Address Gower street WC1E 6BT London United Kingdom See on map Region London Inner London — West Camden and City of London Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
