Computational signatures of uncertainty are reflected in motor cortex excitatory neurochemistry

Abstract

Abstract How individuals process and respond to uncertainty has important implications for cognition and mental health. Here, we use computational phenotyping to examine inter-individual differences in uncertainty processing in relation to neurometabolites and trait anxiety in humans. We introduce a categorical state-transition extension of the Hierarchical Gaussian Filter to model individuals’ evolving beliefs about transition probabilities in a four-choice probabilistic sensorimotor learning task with a reversal. Using 7-Tesla Magnetic Resonance Spectroscopy, we measure neurotransmitter levels in the primary motor cortex. Model-based results reveal dynamic belief updating in response to environmental changes. We further find region-specific relationships between baseline primary motor cortex glutamate+ glutamine levels and prediction errors and volatility beliefs. High trait anxiety is associated with faster post-reversal responses. This study establishes a direct neurochemical correlate of hierarchical belief updating, identifying motor cortex glutamate + glutamine as an important neural marker of inter-individual differences in uncertainty processing.