When the statistical MMN meets the physical MMN

Vera Tsogli,Stefan Koelsch,Tatsuya Daikoku,Sebastian Jentschke

doi:10.1038/s41598-019-42066-4

Vera Tsogli, Stefan Koelsch + Show 2 more

Open Access

https://doi.org/10.1038/s41598-019-42066-4

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Abstract

How do listeners respond to prediction errors within patterned sequence of sounds? To answer this question we carried out a statistical learning study using electroencephalography (EEG). In a continuous auditory stream of sound triplets the deviations were either (a) statistical, in terms of transitional probability, (b) physical, due to a change in sound location (left or right speaker) or (c) a double deviants, i.e. a combination of the two. Statistical and physical deviants elicited a statistical mismatch negativity and a physical MMN respectively. Most importantly, we found that effects of statistical and physical deviants interacted (the statistical MMN was smaller when co-occurring with a physical deviant). Results show, for the first time, that processing of prediction errors due to statistical learning is affected by prediction errors due to physical deviance. Our findings thus show that the statistical MMN interacts with the physical MMN, implying that prediction error processing due to physical sound attributes suppresses processing of learned statistical properties of sounds.

Highlights

How do listeners respond to prediction errors within patterned sequence of sounds? To answer this question we carried out a statistical learning study using electroencephalography (EEG)
It is important to note that the critical difference between the phMMN22 and the statistical MMN (sMMN) is that the former relies on operations of the auditory sensory memory and sensory-memory-representations that are updated instantly[29,30]
Our study replicates the sMMN, an event-related potential (ERP) component triggered by events that deviate from regularities acquired within a statistical learning paradigm

Summary

Introduction

How do listeners respond to prediction errors within patterned sequence of sounds? To answer this question we carried out a statistical learning study using electroencephalography (EEG). Some studies using statistical learning paradigms began to explore neural correlates beyond those related to word segmentation (for a review, see ref.3), using the “Mismatch Negativity (MMN)” as a neurophysiological marker of processing “statistical deviance” (by comparing low and high transition probabilities). François et al.[21], found that violations to word structure (regular: ABC vs irregular: CBA) disrupted the transitional probabilities of the syllables within the word, evoking negativities within the time windows of the MMN/N200 that had a dominance over fronto-central scalp regions It appears that very few statistical learning studies with EEG have investigated effects beyond perception of word boundaries. We expected the physical deviance (elicited by a change in sound location) compared with the statistical deviance (elicited by low in comparison to high transitional probability) would be more prominent and easier to process (i.e., be cognitively less demanding). We expected that the phMMN would have a larger amplitude compared to the sMMN and that the phMMN would attenuate the sMMN

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