Brains that fire together wire together: Interbrain plasticity underlies interaction-based learning

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.