  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.
