Frontiers in Human Neuroscience (Feb 2021)
Do Auditory Mismatch Responses Differ Between Acoustic Features?
Abstract
Mismatch negativity (MMN) is the electroencephalographic (EEG) waveform obtained by subtracting event-related potential (ERP) responses evoked by unexpected deviant stimuli from responses evoked by expected standard stimuli. While the MMN is thought to reflect an unexpected change in an ongoing, predictable stimulus, it is unknown whether MMN responses evoked by changes in different stimulus features have different magnitudes, latencies, and topographies. The present study aimed to investigate whether MMN responses differ depending on whether sudden stimulus change occur in pitch, duration, location or vowel identity, respectively. To calculate ERPs to standard and deviant stimuli, EEG signals were recorded in normal-hearing participants (N = 20; 13 males, 7 females) who listened to roving oddball sequences of artificial syllables. In the roving paradigm, any given stimulus is repeated several times to form a standard, and then suddenly replaced with a deviant stimulus which differs from the standard. Here, deviants differed from preceding standards along one of four features (pitch, duration, vowel or interaural level difference). The feature levels were individually chosen to match behavioral discrimination performance. We identified neural activity evoked by unexpected violations along all four acoustic dimensions. Evoked responses to deviant stimuli increased in amplitude relative to the responses to standard stimuli. A univariate (channel-by-channel) analysis yielded no significant differences between MMN responses following violations of different features. However, in a multivariate analysis (pooling information from multiple EEG channels), acoustic features could be decoded from the topography of mismatch responses, although at later latencies than those typical for MMN. These results support the notion that deviant feature detection may be subserved by a different process than general mismatch detection.
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