The mismatch negativity: a review of underlying mechanisms

Abstract

The mismatch negativity (MMN) is a brain response to violations of a rule, established by a sequence of sensory stimuli (typically in the auditory domain) [Näätänen R. Attention and brain function. Hillsdale, NJ: Lawrence Erlbaum; 1992]. The MMN reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory learning and perceptual accuracy. Although the MMN has been studied extensively, the neurophysiological mechanisms underlying the MMN are not well understood. Several hypotheses have been put forward to explain the generation of the MMN; amongst these accounts, the "adaptation hypothesis" and the "model adjustment hypothesis" have received the most attention. This paper presents a review of studies that focus on neuronal mechanisms underlying the MMN generation, discusses the two major explanatory hypotheses, and proposes predictive coding as a general framework that attempts to unify both.

Fig. 1

Scalp topography and time latency of the MMN. (a) ERP responses to standard and deviant tones overlaid on a whole scalp map of 128 EEG electrodes. (b) ERP responses to the standard and deviant tones at a fronto-central channel. (c) MMN difference wave obtained by subtracting ERP to standards from ERP to deviants. (d) MMN response averaged over the time window of 100–200 ms interpolated for a 3D scalp topography. (From Garrido et al., 2007.)

Fig. 2

MMN generators estimated from EEG and MEG data. The centre of gravity changes from temporal to frontal areas over time. Frontal sources were detected with EEG; due to their radial orientation they might not be detected by MEG. These sources were determined with minimum norm estimates (MNE). (Adapted from Rinne et al., 2000.)

Fig. 3

MMN underlying sources revealed by EEG and conjoint EEG and fMRI measures. (a) Dipoles indicated by red arrows at bilateral STG and IFG (adapted from Doeller et al., 2003). (b) Dipole locations at bilateral STG and right IFG and (c) significant fMRI activation for deviants (adapted from Opitz et al., 2002). (d) Most significant independent component (computed by ICA-LORETA analysis, adapted from Marco-Pallarés et al., 2005). This figure shows consistency for MMN sources across different modalities.

Fig. 4

The MMN interpreted in terms of predictive coding. (a) Illustrative scheme of the general framework of hierarchical Bayes and predictive coding as an explanation for ERP emerge. (b) The MMN, a concrete example and plausible underlying mechanisms.