Project description
The neural mechanisms behind flexible dimensionality
The visual system classifies complex, high-dimensional inputs, aiming to identify key dimensions. Learning in many dimensions is challenging, and theories propose either dimensionality compression or expansion as strategies. How does the brain decide which to use? One hypothesis is that the brain may flexibly switch between the two strategies based on the task, enabling adaptable neural codes. The ERC-funded DimLearn project aims to investigate how task dimensionality affects mental representations and neural activity in learning. By focusing on visual category learning, it will utilise neuroimaging in humans, electrophysiology in rhesus monkeys, and artificial neural networks to uncover the neural mechanisms behind flexible dimensionality. This research will reveal new principles of learning and inspire future educational applications.
Objective
Our visual system frequently has to classify complex, high dimensional inputs. A key learning objective of the brain is thus to identify diagnostic dimensions. Often, tasks require simultaneous consideration of multiple dimensions. Yet, learning many dimensions is computationally challenging. Here, I ask how the visual system tackles the challenge of learning high dimensional tasks. Some theories suggest that the brain does so by compressing dimensions, while others suggest dimensionality expansion. Yet, dimensionality compression and expansion both have advantages and disadvantages, and some studies find dimensionality compression where others find expansion. This raises the hitherto unanswered question what determines whether the brain invokes either of the two strategies. I hypothesize that instead of settling on a single strategy, the brain can reap the benefits of dimensionality compression and expansion by flexibly adjusting dimensionality to the task at hand. This entails the novel prediction of flexible neural codes that can switch dimensionality. To test this theory, I build on a multimodal, multispecies approach I have developed to study learning: using the paradigmatic case of visual category learning, I will establish the effect of task dimensionality on the structure of mental representations in behavior, I will determine how task dimensionality transforms neural activity using neuroimaging in humans, I will identify the neural building blocks of flexible dimensionality using electrophysiology and causal perturbations in rhesus monkeys, and I will unravel computational principles of flexible dimensionality with artificial neural networks. This combination of species and techniques is ideally suited to unravel the neural mechanisms for coping with high dimensional tasks. By elucidating the flexibility of mental and neural representations, I aim to reveal a hitherto unknown principle governing learning and stimulate future educational applications.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2023-COG
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44801 Bochum
Germany
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