Project description
How the brain solves the binding problem in complex multi-source environments
Imagine navigating a bustling market: to process the myriad sights and sounds, the brain tackles the 'causal inference' or 'binding' problem, determining which signals arise from common sources and integrating just those. However, solving this complex computational task optimally is challenging in real-world settings. The ERC-funded MakingSense project combines behavioural, computational and neuroimaging methods to unravel how the brain solves the causal inference problem in increasingly realistic environments. It hypothesises that the brain computes approximate solutions by sequentially selecting signals for perceptual integration based on task demands, our past experiences and expectations. This groundbreaking research has the potential to transform our understanding of human perception, inspire new AI algorithms and offer insights into the perceptual challenges faced by diverse clinical populations.
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.
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.
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
6525 XZ Nijmegen
Netherlands