The neural basis for uncertainty processing in hierarchical decision making

Mien Brabeeba Wang, Nancy Lynch and Michael M. Halassa ()
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Mien Brabeeba Wang: Massachusetts Institute of Technology
Nancy Lynch: Massachusetts Institute of Technology
Michael M. Halassa: Tufts University

Nature Communications, 2025, vol. 16, issue 1, 1-25

Abstract: Abstract Hierarchical decisions in natural environments require processing uncertainty across multiple levels, but existing models struggle to explain how animals perform flexible, goal-directed behaviors under such conditions. Here we introduce CogLinks, biologically grounded neural architectures that combine corticostriatal circuits for reinforcement learning and frontal thalamocortical networks for executive control. Through mathematical analysis and targeted lesion, we show that these systems specialize in different forms of uncertainty, and their interaction supports hierarchical decisions by regulating efficient exploration, and strategy switching. We apply CogLinks to a computational psychiatry problem, linking neural dysfunction in schizophrenia to atypical reasoning patterns in decision making. Overall, CogLink fills an important gap in the computational landscape, providing a bridge from neural substrates to higher cognition.

Date: 2025
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DOI: 10.1038/s41467-025-63994-y

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