Orthogonal representational geometry in dACC underpins human hierarchical reasoning

Abstract

Flexible adaptation requires inferring the causes of feedback and adjusting accordingly. When tasks are hierarchically structured with high-level rules governing low-level perceptual judgements, good performance requires hierarchical reasoning. Yet the computational and neural mechanisms underlying this process remain unclear. Here, building on Bayesian modelling and recurrent neural network simulation of hierarchical reasoning, we verified that such algorithm and representations are implemented in human dorsal anterior cingulate cortex (dACC). dACC encodes accumulated errors and perceptual difficulty to estimate high-level rule switch confidence. These two variables were represented along two separable dimensions, and could be approximated by Gaussian basis functions. Orthogonality between these two dimensions yielded an optimal two-dimensional representation for hierarchical reasoning. A closer-to-orthogonal representational geometry also predicted better performance. Together, these findings provide an integrated computational and neural representational basis for hierarchical reasoning.