A computational hierarchy in human cortex

Hierarchies feature prominently in anatomical accounts of cortical organisation. An open question is which computational (algorithmic) processes are implemented by these hierarchies. One renowned hypothesis is that cortical hierarchies implement a model of the world's causal structure and serve to infer environmental states from sensory inputs. This view, which casts perception as hierarchical Bayesian inference, has become a highly influential concept in both basic and clinical neuroscience. So far, however, a direct correspondence between the predicted order of hierarchical Bayesian computations and the sequence of evoked neuronal activity has not been demonstrated. Here, we present evidence for this correspondence from neuroimaging and electrophysiological data in healthy volunteers. Trial-wise sequences of hierarchical computations were inferred from participants' behaviour during a social learning task that required multi-level inference about intentions. We found that the temporal sequence of neuronal activity matched the order of computations as predicted by the theory. These findings provide strong evidence for the operation of hierarchical Bayesian inference in human cortex. Furthermore, our approach offers a novel strategy for the combined computational-physiological phenotyping of patients with disorders of perception, such as schizophrenia or autism.