PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 12, 2026NeuroImage0 citationsOpen Access

When More Control Means Better Choices: Cognitive Control Networks Drive Expected-Value Maximization Under Uncertainty

View Full Paper
XWXia WuYGYuning GengYCYan Chen

Key Points

  • This research aims to understand how cognitive control capacity influences decision-making under uncertainty.
  • Participants with high and low cognitive control capacity performed a predictive inference task during fMRI.
  • Cognitive control capacity was assessed and linked to decision-making behaviors across uncertainty levels.
  • Hierarchical drift-diffusion modeling analyzed decision thresholds and response maximization.
  • High cognitive control capacity individuals showed a higher proportion of maximizing responses across uncertainty levels.
  • Cautious decision thresholds were identified, indicating more deliberative decision-making (p<0.05).
  • Cingulo-opercular network connectivity was essential for translating high cognitive capacity into optimal choices.

Abstract

• Cognitive control capacity promotes maximizing choices under uncertainty. • High-CCC individuals show more expected-value maximizing responses. • High-CCC individuals adopt more cautious decision thresholds. • Cingulo-opercular connectivity links control capacity to optimal choice. Human decision-making under outcome uncertainty often deviates from rational expected-value maximization, frequently falling back on the suboptimal probability matching heuristic. The neurocomputational mechanisms determining individual differences in overcoming this heuristic remain elusive. Here, we investigated how cognitive control capacity (CCC) modulates decision-making under varying levels of outcome uncertainty. Participants with high and low CCC performed a predictive inference task during functional magnetic resonance imaging. Behaviorally, high CCC individuals consistently exhibited a significantly higher proportion of maximizing responses (PMR) across all uncertainty levels. Using hierarchical drift-diffusion modeling, we demonstrated that this optimal performance was driven by more cautious decision thresholds, indicating greater deliberation to resist intuitive shortcuts. At the neural level, while localized activations in the cingulo-opercular network (CON) and frontoparietal network (FPN) reflected the general cognitive burden of escalating uncertainty, functional connectivity analyses revealed a specific neural pathway supporting optimal choices. Crucially, the connectivity within the CON (anterior insula to middle frontal gyrus) acted as a specific neural amplifier, which was absolutely necessary for translating high cognitive capacity into optimal expected-value maximization. The FPN, while tracking uncertainty, did not modulate this capacity-performance link. Together, these findings provide an integrated neurocomputational framework demonstrating how specific cognitive control networks mobilize resources to overcome heuristic tendencies and achieve optimal decisions under uncertainty.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/6a02c2fdce8c8c81e9640455https://doi.org/10.1016/j.neuroimage.2026.121989
Ask AI
Helpful
Bookmark
Share
View Full Paper