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Predictive coding is the leading algorithmic framework to understand howexpectations shape our experience of reality. Its main tenet is that sensoryneurons encode prediction error: the residuals between a generative model ofthe sensory world and the actual sensory input. However, it is yet unclear howthis scheme generalises to the multi-level hierarchical architecture of sensoryprocessing. Theoretical accounts of predictive coding agree that neuronscomputing prediction error and the generative model exist at all levels of theprocessing hierarchy. However, there is not a current consensus of howpredictions from independent models at different stages are integrated duringthe computation of prediction error. Here we investigated predictive processingwith respect to two independent concurrent generative models in the auditorypathway using functional magnetic resonance imaging. We used two paradigmswhere human participants listened to sequences of either pure tones orFM-sweeps while we recorded BOLD responses in inferior colliculus (IC), medialgeniculate body (MGB), and auditory cortex (AC). Each paradigm included theinduction of two generative models: one based on local stimulus statistics; andanother model based on the subjective expectations induced by task instruction.We used Bayesian model comparison to test whether neural responses in IC, MGB,and AC encoded prediction error with respect to either of the two generativemodels, or a combination of both. Results showed that neural populations inbilateral IC, MGB, and AC encode prediction error with respect to a combinationof the two generative models, suggesting that the predictive architecture ofpredictive coding might be more complex than previously hypothesised.

Concurrent generative models inform prediction error in the human auditory pathway