Neurochemical markers of uncertainty processing in humans

Abstract

How individuals process and respond to uncertainty has important implications for cognition and mental health. Here we use computational phenotyping to examine individualised "uncertainty fingerprints" in relation to neurometabolites and trait anxiety in humans. We introduce a novel categorical state-transition extension of the Hierarchical Gaussian Filter (HGF) to capture implicit learning in a four-choice probabilistic sensorimotor reversal learning task by tracking beliefs about stimulus transitions. Using 7-Tesla Magnetic Resonance Spectroscopy, we measured baseline neurotransmitter levels in the primary motor cortex (M1). Model-based results revealed dynamic belief updating in response to environmental changes. We further found region-specific relationships between M1 glutamate+ glutamine levels and prediction errors and volatility beliefs, revealing an important neural marker of probabilistic reversal learning in humans. High trait anxiety was associated with faster post-reversal responses. By integrating computational modelling with neurochemical assessments, this study provides novel insights into the neurocomputations that drive individual differences in processing uncertainty.