📋 Tender Overview
Making Sense Of The Senses: Causal Inference In A Complex Dynamic Multisensory World
Objective
To interact effectively with the complex dynamic and multisensory world (e.g. traffic) the brain needs to transform the barrage of signals into a coherent percept. This requires it to solve the causal inference or binding problem - deciding which signals come from common sources and integrating those accordingly. Doing so exactly (i.e. optimally) is wildly computationally intractable for all but the simplest laboratory scenes. It is unknown how the brain computes approximate solutions for realistic scenes in the face of resource constraints.
This ambitious interdisciplinary project combines statistical, computational, behavioural and neuroimaging (3/7T-fMRI, MEG/EEG, TMS) methods to determine how, and how well, the brain solves the causal inference problem in progressively richer multisensory environments.
The key hypothesis is that observers compute approximate solutions by sequentially selecting subsets of signals for perceptual integration via attentional and active sensing mechanisms guided by the perceptual tasks they are executing, their prior expectations about the world’s causal structure, and bottom-up salience maps. I will build parallel normative/approximate Bayesian and transformer network models of these processes and combine those with behaviour and neuroimaging to unravel the neurocomputational mechanisms.
Funded Under
European Research Council (Erc)
Total Cost
€ 2 499 527,00
Eu Contribution
€ 2 499 527,00
Coordinated By
Stichting Radboud Universiteit
Netherlands
