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MakingSense

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Making sense of the senses: Causal Inference in a complex dynamic multisensory w...

Making sense of the senses: Causal Inference in a complex dynamic multisensory world 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... ver más

31/03/2029

STICHTING RADBOUD...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

STICHTING RADBOUD UNIVERSITEIT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-11-15

ERC-2022-ADG: ERC ADVANCED GRANTS Scope:Objectives

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Cerrada hace 2 años

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1 Participantes

STICHTING RADBOUD UNIVERSITE...

2.50M€ | Lider

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Duración del proyecto: 64 meses Fecha Inicio: 2023-11-15
Fecha Fin: 2029-03-31

Líder del proyecto

STICHTING RADBOUD UNIVERSITEIT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto 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. The project will develop a novel computational and neuromechanistic account of causal inference in more realistic multisensory scenes, addressing fundamental questions about binding, inference and probabilistic computations. By bringing lab research closer to the real world it will radically alter our perspectives - shifting from near-optimal passive perception in simple scenes to active information gathering in the service of approximate solutions in more realistic scenes. It has the potential to inspire new AI algorithms and drive transformative insights into the perceptual difficulties older and clinical populations face in the real-world.

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