When more control means better choices: Cognitive control networks drive expected-value maximization under...

Area of Science:

• Neuroscience
• Cognitive Psychology
• Decision Science

Background:

• Human decision-making under uncertainty often uses suboptimal heuristics like probability matching.
• Individual differences in overcoming these heuristics are not well understood.

Purpose of the Study:

• To investigate how cognitive control capacity (CCC) influences decision-making under varying outcome uncertainty.
• To identify the neurocomputational mechanisms underlying optimal choices.

Main Methods:

• Functional magnetic resonance imaging (fMRI) was used with participants performing a predictive inference task.
• Hierarchical drift-diffusion modeling analyzed decision thresholds and response patterns.
• Functional connectivity analyses examined neural network interactions.

Main Results:

• Higher CCC individuals showed a greater proportion of maximizing responses (PMR) across uncertainty levels.
• Optimal performance in high CCC individuals was linked to more cautious decision thresholds.
• Connectivity within the cingulo-opercular network (CON) was crucial for translating CCC into optimal decision-making.

Conclusions:

• Cognitive control networks, particularly the CON, are essential for overcoming heuristic decision-making under uncertainty.
• Specific neural pathways within the CON amplify cognitive capacity to support optimal choices.
• This study provides a neurocomputational framework for understanding optimal decision-making under uncertainty.