Thalamic Regulation Of Reinforcement Learning Strategies Across Prefrontalstriatal Networks Bin A Wang Mien Brabeeba Wang Norman H Lam Liu Mengxing Shumei Li Ralf D Wimmer Pedro M Pazalonso Michael M Halassa Burkhard Pleger by Bin A. Wang & Mien Brabeeba Wang & Norman H. Lam & Liu Mengxing & Shumei Li & Ralf D. Wimmer & Pedro M. Paz-alonso & Michael M. Halassa & Burkhard Pleger instant download after payment.

Human decision-making involves model-free and model-based reinforcementlearning (RL) strategies, largely implemented by prefrontal-striatal circuits.Combining human brain imaging with neural network modelling in a probabilistic reversal learning task, we identify a unique role for the mediodorsalthalamus (MD) in arbitrating between RL strategies. While both dorsal PFC andthe striatum support rule switching, the former does so when subjects predominantly adopt model-based strategy, and the latter model-free. The lateraland medial subdivisions of MD likewise engage these modes, each with distinctPFC connectivity. Notably, prefrontal transthalamic processing increasesduring the shift from stable rule use to model-based updating, with model-freeupdates at intermediate levels. Our CogLinks model shows that model-freestrategies emerge when prefrontal-thalamic mechanisms for context inferencefail, resulting in a slower overwriting of prefrontal strategy representations - aphenomenon we empirically validate with fMRI decoding analysis. Thesefindings reveal how prefrontal transthalamic pathways implement flexible RLbased cognition.