An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control...

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Attentional control is crucial for adapting behavior to environmental demands.
• The anterior cingulate and lateral prefrontal cortices are key for cognitive control.
• Learning mechanisms for flexible adaptation of attentional control remain unclear.

Purpose of the Study:

• To elucidate the neuro-computational mechanisms underlying adaptive attentional control.
• To investigate how the brain learns to adjust control settings in response to changing demands.
• To connect cognitive control networks with subcortical learning processes.

Main Methods:

• Model-based functional magnetic resonance imaging (fMRI).
• Employing a reinforcement learning model with a volatility-driven learning rate.
• Analyzing neural activity during tasks with varying control demands.

Main Results:

• Human adjustments to control demands align with a reinforcement learner.
• The anterior insula estimates volatility of control demand.
• The caudate nucleus predicts forthcoming demands, guided by insula activity.
• Caudate predictions optimize attentional control in cingulate and prefrontal cortices.

Conclusions:

• Adaptive behavior relies on a subcortical learning mechanism estimating environmental volatility.
• This mechanism flexibly integrates past experiences to predict future demands.
• The findings link the cingulate-prefrontal network to a novel subcortical control-learning system.