A Critical Review of the Deviance Detection Theory of Mismatch Negativity

Abstract

Mismatch negativity (MMN) is a component of the difference waveform derived from passive auditory oddball stimulation. Since its inception in 1978, this has become one of the most popular event-related potential techniques, with over two-thousand published studies using this method. This is a testament to the ingenuity and commitment of generations of researchers engaging in basic, clinical and animal research. Despite this intensive effort, high-level descriptions of the mechanisms theorized to underpin mismatch negativity have scarcely changed over the past four decades. The prevailing deviance detection theory posits that MMN reflects inattentive detection of difference between repetitive standard and infrequent deviant stimuli due to a mismatch between the unexpected deviant and a memory representation of the standard. Evidence for these mechanisms is inconclusive, and a plausible alternative sensory processing theory considers fundamental principles of sensory neurophysiology to be the primary source of differences between standard and deviant responses evoked during passive oddball stimulation. By frequently being restated without appropriate methods to exclude alternatives, the potentially flawed deviance detection theory has remained largely dominant, which could lead some researchers and clinicians to assume its veracity implicitly. It is important to have a more comprehensive understanding of the source(s) of MMN generation before its widespread application as a clinical biomarker. This review evaluates issues of validity concerning the prevailing theoretical account of mismatch negativity and the passive auditory oddball paradigm, highlighting several limitations regarding its interpretation and clinical application.

The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation

“…Accordingly, actual MMN paradigm, at least in the auditory domain, would not properly enable inferences about deviance detection and predictive coding because of state changes that can affect sensory responses. Even if our results are in line with previous findings and fit well with the predictive coding framework of MMN as well as with the predictive brain hypothesis, we cannot totally rule out that differences in sensory processing (see “Sensory Processing Theory” in O'Reilly and O'Reilly, 2021 ) or that another MMN framework such as the adaptation model (e.g., May, 2021 ) might partly explain our results. Indeed, despite intensively studied, MMN still poses a number of challenges in terms of interpretation (May, 2021 ).…”

“…Finally, O'Reilly and O'Reilly ( 2021 ) argued that equiprobable sequence would be an insufficient control because of long-term adaptation. The authors added that counterbalanced blocks would confound adaptation effects rather than eliminate them (O'Reilly and Conway, 2021 ; O'Reilly and O'Reilly, 2021 ).…”

“…Finally, O'Reilly and O'Reilly ( 2021 ) argued that equiprobable sequence would be an insufficient control because of long-term adaptation. The authors added that counterbalanced blocks would confound adaptation effects rather than eliminate them (O'Reilly and Conway, 2021 ; O'Reilly and O'Reilly, 2021 ). Accordingly, actual MMN paradigm, at least in the auditory domain, would not properly enable inferences about deviance detection and predictive coding because of state changes that can affect sensory responses.…”

“…The visual MMN (vMMN) is a differential negative event-related potential (ERP) representing the pre-attentive neural mechanism involved in the automatic detection of unpredicted visual changes among a learned regularity (Czigler et al, 2002 , 2006 ; Stefanics et al, 2015 ). In line with predictive coding framework (Rao and Ballard, 1999 ; Friston, 2005 , 2010 ), vMMN is often considered as a neural correlate of prediction error (Friston, 2005 ; Garrido et al, 2009 ; Stefanics et al, 2014 , but May, 2021 ; O'Reilly and O'Reilly, 2021 ) i.e., the difference between sensory input and predictions, based on our internal model constructed upon the regularity of standard stimuli. Thus, it appears to be particularly suitable to investigate predictive processes.…”

Recurrent neural network model of human event-related potentials in response to intensity oddball stimulation

“…It has also been pointed out that control sequences designed to verify interpretations of MMN may overlook some of aspects of sensory processing that can influence responses to control stimuli (O’Reilly and O’Reilly, 2021). For example, the roving oddball sequence induces repetition suppression and may also reflect asymmetric sensitivity of the auditory response to the physical parameter(s) under investigation (O’Reilly, 2021a).…”