A common neural network differentially mediates direct and social fear learning

Area of Science:

• Neuroscience
• Computational Neuroscience
• Psychology

Background:

• Fear often spreads between individuals via social learning across species.
• Neural and computational mechanisms of social fear learning remain largely unknown.

Purpose of the Study:

• To compare neural mechanisms of social and direct fear learning.
• To elucidate the computational basis of information flow in social fear acquisition.

Main Methods:

• Comparative analysis of social and direct (Pavlovian) fear learning.
• Utilized dynamic causal modeling and reinforcement learning modeling.
• Examined neural network activity in the amygdala, anterior insula (AI), and anterior cingulate cortex (ACC).

Main Results:

• Social and direct fear learning exhibit indistinguishable behavioral effects.
• Both learning types engage a common aversive learning network involving the amygdala, AI, and ACC.
• Information flow within this network differs: amygdala for direct learning, AI for social learning.
• The AI modulates learning signals based on surprise (associability) for both modalities, conveying them to the ACC.

Conclusions:

• Social fear learning involves distinct information processing pathways compared to direct fear learning within a shared neural network.
• Findings offer insights into the neural underpinnings of social fear and have implications for anxiety disorders like phobias.