The critical role of the inferior frontal cortex in establishing a prediction model for generating subsequent mismatch negativity (MMN): A TMS-EEG study

The prediction violation account of automatic or pre-attentive change detection assumed that the inferior frontal cortex (IFC) is involved in establishing a prediction model for detecting unexpected changes. Evidence supporting the IFC’s contribution to prediction model is mainly based on the Mismatch Negativity (MMN) to deviants violating predictions that are established based on the frequently presented standard events. However, deviant detection involves processes, such as events comparison, other than prediction model establishment. The current study investigated the critical role of the IFC in establishing a prediction model during standards processing for subsequent deviant detection. Transcranial Magnetic Stimulation (TMS) was applied at the IFC to disrupt the processing of the initial 2 or 5 standards of a 3-, 6-, or 9-standard train, while the MMN responses to pitch deviant presented after the standard trains were recorded and compared. An abolishment of MMN was only observed when TMS was delivered to the IFC at the initial 2 standards of the 3-standard train, but not at the initial 5 standards, or when TMS at the vertex or TMS sound recording was applied. The MMNs were also preserved when IFC TMS, vertex TMS, or TMS sound recording was applied at the initial 2 or 5 standards of longer trains. The IFC plays a critical role in processing the initial standards of a short standard train for subsequent deviant detection. This result is consistent with the prediction violation account that the IFC is important for establishing the prediction model. •TMS on IFC when processing the standards abolished the subsequent MMN to deviants.•The IFC TMS effect was specific to deviant preceded by a short train of standards.•MMNs were preserved in spatial and auditory control conditions.•These results are consistent with the prediction violation hypothesis of MMN.•IFC is critical for establishing a prediction model from standards to generate MMN.