ow does the brain know what is out there and what is not? Living organisms cannot rely solely on sensory signals for perception because they are noisy and ambiguous. To transform sensory signals into stable percepts, the brain uses its prior knowledge or beliefs. Current theories describe perceptual beliefs as probability distributions over the features of the stimuli, summarised by their mean and variance. Beliefs are updated by feature prediction errors: the mismatch between expected and observed feature values. This framework explains how the brain encodes unexpected changes in stimulus features (e.g., higher or lower pitch, stronger or weaker motion). How the brain updates beliefs about a stimulus' presence or absence is, however, unclear.
We propose that the detection of absence relies on a distinct form of prediction error dedicated to reducing the beliefs on stimulus occurrence. We call this signal absence prediction error. Using the human auditory system as a model for sensory processing, we developed a paradigm designed to test this hypothesis. fMRI results showed that absence prediction error is encoded in the auditory thalamus and cortex, indicating that absence is explicitly represented in subcortical sensory pathways. Moreover, while feature prediction error is already encoded in the auditory midbrain, absence prediction error was not, implying that absence-related error signals are supported by a different circuit.
These results identify a neural mechanism for the detection of sensory absence. Such mechanisms may be disrupted in conditions such as psychosis, where predictions about absence and presence are impaired.