Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Humans adapt to dynamic environments by learning statistical regularities.
• Fear significantly impacts adaptive behaviors, but its underlying computational and neural mechanisms are not fully understood.

Purpose of the Study:

• To investigate how fear influences adaptation to environmental volatility.
• To elucidate the neuro-computational mechanisms by which fear impairs adaptive learning.

Main Methods:

• Conducted behavioral (n=21) and functional magnetic resonance imaging (fMRI, n=37) experiments using a novel cue-biased adaptation learning task.
• Manipulated emotional valence (fearful/neutral cues) and environmental volatility (reward probability reversals).
• Employed computational modeling and dynamic causal modeling to analyze learning rates and neural activity/connectivity.

Main Results:

• Computational modeling showed a higher learning rate in response to environmental volatility with neutral cues, but this flexible adjustment was absent with fearful cues.
• Fear suppressed adaptive learning in volatile environments, indicating a detrimental effect of fear on behavioral flexibility.
• fMRI revealed that this suppressive effect was associated with activity in the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC), and altered functional connectivity between the dACC and temporal-parietal junction (TPJ).

Conclusions:

• Fear significantly impairs the ability to adapt to changing environmental conditions, particularly in volatile settings.
• The neuro-computational findings suggest that fear's suppression of adaptive behavior occurs early in bottom-up processing, driven by TPJ activity modulated by the dACC.
• This study provides a neuro-computational framework for understanding how fear interferes with adaptation in dynamic environments.