Neural representations of beliefs in a multi-dimensional inference task

Abstract

Adaptive behavior requires maintaining and updating probabilistic beliefs about the world, yet how distributed brain circuits implement such computations remains unknown. We recorded from over 1,400 neurons across six brain regions in monkeys performing a multi-dimensional inference task requiring them to infer hidden rules through trial-and-error learning. Behavior was well-described by models based on Bayesian updating of beliefs over rule features. Neural representations of both observable variables (stimuli, rewards) and latent beliefs (rule preferences, confidence) were broadly distributed across hippocampus, amygdala, prefrontal cortex, anterior cingulate, striatum, and inferior temporal cortex. Belief representations were present throughout all task periods but exhibited region- and epoch-specific dynamics. Critically, trial-to-trial changes in population activity reflected Bayesian belief updating: neural responses evolved according to the integration of prior beliefs with new evidence. Additionally, we identified confidence representations that were independent of specific beliefs and showed distinct temporal profiles. These results demonstrate that probabilistic inference emerges from coordinated dynamics across distributed brain systems, with different regions contributing flexibly according to computational demands at different states of learning and decision-making.