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RESEARCH PRODUCT

Distinctive Representation of Mispredicted and Unpredicted Prediction Errors in Human Electroencephalography

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The predictive coding model of perception proposes that neuronal responses are modulated by the amount of sensory input that the internal prediction cannot account for (i.e., prediction error). However, there is little consensus on what constitutes nonpredicted stimuli. Conceptually, whereas mispredicted stimuli may induce both prediction error generated by prediction that is not perceived and prediction error generated by sensory input that is not anticipated, unpredicted stimuli involves no top-down, only bottom-up, propagation of information in the system. Here, we examined the possibility that the processing of mispredicted and unpredicted stimuli are dissociable at the neurophysiological level using human electroencephalography. We presented participants with sets of five tones in which the frequency of the fifth tones was predicted, mispredicted, or unpredicted. Participants were required to press a key when they detected a softer fifth tone to maintain their attention. We found that mispredicted and unpredicted stimuli are associated with different amount of cortical activity, probably reflecting differences in prediction error. Moreover, relative to predicted stimuli, the mispredicted prediction error manifested as neuronal enhancement and the unpredicted prediction error manifested as neuronal attenuation on the N1 event-related potential component. These results highlight the importance of differentiating between the two nonpredicted stimuli in theoretical work on predictive coding.SIGNIFICANCE STATEMENTThe current research seeks to dissociate the neurophysiological processing of two types of “nonpredicted” stimuli that have long been considered interchangeable: mispredicted and unpredicted stimuli. We found that mispredicted stimuli, which violate predictions, and unpredicted stimuli, which lack predictions, are represented distinctively in the brain. The results will influence the design of experiments on the predictive coding mechanism, in which the contrast between predicted and “nonpredicted” conditions should be specifically defined to reveal the prediction error proper. This is of general interest because it concerns the logic of research investigating all levels of processing (including perceptual, motor, and cognitive processing) in many neuroscientific domains.