Project description
Mathematical methods reduce complexity and reveal object representation in the brain
A central question in the cognitive sciences is how objects are represented cognitively and neurally. A major issue lies in the paradox between how proficient we are at recognising objects and the overwhelming amount of information we hold about each object. A solution lies in understanding how neural representations are organised, and potentially assuming that principals that are typically applied to sensorial data are also applicable to high-level object information. The EU-funded ContentMAP project is using experimental and theoretical techniques to reduce high-dimensional, object-related neural and cognitive patterns to low-dimensional representations on the cortical surface. The team’s encoding model should be able to predict the neural ‘fingerprint’ of each object, successfully describing how objects are represented in the brain.
Objective
Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. In ContentMAP I will put forth a novel understanding of how object knowledge is organized in the brain, by proposing that this knowledge is topographically laid out in the cortical surface according to object-related dimensions that code for different types of representational content – I will call this contentotopic mapping. To study this fine-grain topography, I will use a combination of fMRI, behavioral, and neuromodulation approaches. I will first obtain patterns of neural and cognitive similarity between objects, and from these extract object-related dimensions using a dimensionality reduction technique. I will then parametrically manipulate these dimensions with an innovative use of a visual field mapping technique, and test how functional selectivity changes across the cortical surface according to an object’s score on a target dimension. Moreover, I will test the tuning function of these contentotopic maps. Finally, to mirror the complexity of implementing a high-dimensional manifold onto a 2D cortical sheet, I will aggregate the topographies for the different dimensions into a composite map, and develop an encoding model to predict neural signatures for each object. To sum up, ContentMAP will have a dramatic impact in the cognitive sciences by describing how the stuff of concepts is represented in the brain, and providing a complete description of how fine-grain representations and functional selectivity within high-level complex processes are topographically implemented.
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Funding Scheme
ERC-STG - Starting GrantHost institution
3004-531 Coimbra
Portugal