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

Year: 2026

Type: article

Abstract: 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.

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2026.121989