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DyNeRfusion SIGNED

Dynamic Network Reconstruction of Human Perceptual and Reward Learning via Multimodal Data Fusion

Total Cost €

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EC-Contrib. €

0

Partnership

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 DyNeRfusion project word cloud

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

modalities    electrophysiological    stock    integrating    trial    adaptive    uncover    empower    domain    stimulus    additional    neuroimaging    guided    reward    inferred    maximization    multimodal    eeg    prediction    multivariate    probabilistic    divergent    reported    endogenous    noisy    single    mechanism    neuronal    extends    perceptual    proposition    inspired    previously    betting    training    explanatory    computational    isolation    ultimate    unified    improvements    understand    simultaneously    lines    fuse    characterization    predictors    lasting    diagnose    ambiguous    image    literature    acquired    actions    facilitates    sensory    parametric    basis    decisions    mechanisms    neurobiological    primary    either    efforts    behaviorally    share    mechanistic    networks    spatiotemporal    data    considerable    principles    market    respectively    fmri    largely    ray    variability    error    learning    framework    power    despite    behavior    machine    representations    whereby    separate    neural    techniques   

Project "DyNeRfusion" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF GLASGOW 

Organization address
address: UNIVERSITY AVENUE
city: GLASGOW
postcode: G12 8QQ
website: www.gla.ac.uk

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 1˙996˙043 €
 EC max contribution 1˙996˙043 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-COG
 Funding Scheme ERC-COG
 Starting year 2020
 Duration (year-month-day) from 2020-09-01   to  2025-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF GLASGOW UK (GLASGOW) coordinator 1˙996˙043.00

Map

 Project objective

Training and experience can lead to long-lasting improvements in our ability to make decisions based on either ambiguous sensory or probabilistic information (e.g. learning to diagnose a noisy x-ray image or betting on the stock market). These two processes are referred to as perceptual and probabilistic/reward learning, respectively. Despite considerable efforts to uncover the neural systems involved in these processes, perceptual and reward learning have largely been studied in separate lines of research using divergent learning mechanisms. The primary aim of this proposal is to develop a unified framework for integrating these lines of research and understand the extent to which they share a common computational and neurobiological basis. Specifically, we will test the proposition that both the perceptual and reward systems could be understood in a common framework of “reward maximization”, whereby a domain-general reinforcement-guided learning mechanism – based on separate prediction error representations – facilitates future actions and adaptive behavior. To offer a comprehensive spatiotemporal characterization of the relevant networks and their computational principles we will adopt a state-of-the-art multimodal neuroimaging approach to fuse simultaneously-acquired EEG and fMRI data, via machine-learning-inspired multivariate single-trial analysis techniques and computational modelling. The project’s ultimate goal is to empower a level of neuronal and mechanistic understanding that extends beyond what could be inferred with each of these modalities in isolation. We will achieve this goal by exploiting endogenous trial-by-trial electrophysiological variability to build parametric fMRI predictors that can offer additional explanatory power than what can already be achieved by stimulus- or behaviorally-derived predictors, allowing us to go over and beyond what has been reported previously in the literature.

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

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lastchecktime (2022-05-18 16:12:25) correctly updated