Summary
Traditionally, brain plasticity was viewed as a change occurring in synapses, brain structures and functional networks, within an individual brain. Considering that social interactions play a major role in the acquisitions of skills and knowledge, I suggest a new approach for understanding interaction-based learning that focuses on inter-brain plasticity. The term coined here, inter-brain plasticity, will be defined as the short and long-term experience-dependent changes in brain-to-brain coupling. I argue that inter-brain plasticity in the observation-execution system develops over the course of interaction-based learning to facilitate information exchange between partners and thus learning. The proposed study aims at introducing a new theory on how inter-brain networks reorganize following training in interaction-based learning. It has three objectives: 1) To develop a model of inter-brain plasticity during learning (short-term), following training (long-term) and generalization between/within interaction partners; 2) To develop a novel dyadic-neurofeedback technology that allows drawing causal inferences between inter-brain plasticity and learning; 3) To test whether diminished inter-brain plasticity in Autism Spectrum Disorders may be reversed following training with the new dyadic neurofeedback. To address these questions, I will take advantage of the high temporal resolution of state-of-the-art dual-functional Near-Infrared Spectroscopy (fNIRS) setup and examine how intra- and inter-brain networks reconfigure during learning of various skills, ranging from motor learning to language acquisition. The proposed experiments will lay the foundation for a new field of research focusing on the dynamics of inter-brain networks. The inter-brain plasticity approach will radically change how we view neuroplasticity. It will allow for the first time probing the long-term changes in the reconfiguration of inter-brain networks.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101020091 |
| Start date: | 01-10-2021 |
| End date: | 30-09-2026 |
| Total budget - Public funding: | 2 597 700,00 Euro - 2 597 700,00 Euro |
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Original description
Traditionally, brain plasticity was viewed as a change occurring in synapses, brain structures and functional networks, within an individual brain. Considering that social interactions play a major role in the acquisitions of skills and knowledge, I suggest a new approach for understanding interaction-based learning that focuses on inter-brain plasticity. The term coined here, inter-brain plasticity, will be defined as the short and long-term experience-dependent changes in brain-to-brain coupling. I argue that inter-brain plasticity in the observation-execution system develops over the course of interaction-based learning to facilitate information exchange between partners and thus learning. The proposed study aims at introducing a new theory on how inter-brain networks reorganize following training in interaction-based learning. It has three objectives: 1) To develop a model of inter-brain plasticity during learning (short-term), following training (long-term) and generalization between/within interaction partners; 2) To develop a novel dyadic-neurofeedback technology that allows drawing causal inferences between inter-brain plasticity and learning; 3) To test whether diminished inter-brain plasticity in Autism Spectrum Disorders may be reversed following training with the new dyadic neurofeedback. To address these questions, I will take advantage of the high temporal resolution of state-of-the-art dual-functional Near-Infrared Spectroscopy (fNIRS) setup and examine how intra- and inter-brain networks reconfigure during learning of various skills, ranging from motor learning to language acquisition. The proposed experiments will lay the foundation for a new field of research focusing on the dynamics of inter-brain networks. The inter-brain plasticity approach will radically change how we view neuroplasticity. It will allow for the first time probing the long-term changes in the reconfiguration of inter-brain networks.Status
SIGNEDCall topic
ERC-2020-ADGUpdate Date
27-04-2024
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