Opendata, web and dolomites


Neural & Computational Principles of Multisensory Integration during Active Sensing and Decision-Making

Total Cost €


EC-Contrib. €






Project "NeuCoDe" data sheet

The following table provides information about the project.


Organization address
city: LEEDS
postcode: LS2 9JT

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 224˙933 €
 EC max contribution 224˙933 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-RI
 Starting year 2019
 Duration (year-month-day) from 2019-04-01   to  2021-03-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF LEEDS UK (LEEDS) coordinator 224˙933.00


 Project objective

Perceptual decisions rely on the integration of information from the environment, which typically involves the combination of stimuli from different senses. The quality of sensory evidence depends highly on our actions that affect how we acquire information from the external world. Importantly, the processing of this multisensory information requires the interaction of multiple neural processes over time. However, the neural mechanisms underlying this complex human behaviour remain elusive. In this project, I will employ a novel active sensing paradigm coupled with state-of-the-art neuroimaging and computational modelling to probe how the brain samples, processes and integrates multisensory information in order to make fast and accurate decisions. I will devise a reaction-time task where human subjects will actively sense and discriminate the amplitude of two texture stimuli a) using only visual information, b) using only haptic information and c) combining the two sensory cues, while electroencephalograms (EEG) will be recorded. To study this, I will develop a novel computational methodology for the joint analysis of brain activity (EEG), sensorimotor signals (movement kinematics) and behavioural measurements (choice and response time). First, behavioural modelling will provide a mechanistic account of the constituent processes underlying decision fomation. Then, model predictions will inform the joint analysis of neural and sensorimotor signals, to characterize the neural and behavioural basis of active multi-sensing and decision-making. To achieve this, I will devise an information-theoretic methodology that quantifies a) the contribution of each sensory modality to perception and b) the interaction of their neural representations to drive perceptual decisions. Ultimately, this project will elucidate the brain networks involved in active multisensory decision-making and characterize their respective functional roles in behavioural performance.

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

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