Dynamic mutual predictions during social learning: A computational and interbrain model.

Area of Science:

• Cognitive Neuroscience
• Social Psychology
• Computational Neuroscience

Background:

• Social interactions facilitate continuous learning about others' thoughts, feelings, and traits.
• This bidirectional learning is vital for forming social bonds and knowledge acquisition.
• Existing models often focus on unidirectional learning, neglecting simultaneous mutual learning.

Purpose of the Study:

• To review the literature on social learning.
• To propose a novel computational and neural model of mutual prediction in social interactions.
• To characterize the mechanisms underlying developing mutual understanding.

Main Methods:

• Literature review of social learning.
• Development of a computational model where individuals minimize prediction errors for self and partner.
• Examination of neural correlates, including interbrain plasticity in specific brain networks.

Main Results:

• The proposed mutual prediction model posits that minimizing prediction errors leads to convergence of inferential models between partners.
• This convergence facilitates the development of mutual understanding.
• Interbrain plasticity, changes in neural coupling over time, is hypothesized to support this social learning process.

Conclusions:

• The mutual prediction model offers a framework for understanding how relationships evolve through simultaneous social learning.
• Interbrain plasticity within networks like the mentalizing system and hippocampus may underpin this process.
• The model provides empirically verifiable predictions for future research on social development.