ERP mismatch response to phonological and temporal regularities in speech

Abstract

Predictions of our sensory environment facilitate perception across domains. During speech perception, formal and temporal predictions may be made for phonotactic probability and syllable stress patterns, respectively, contributing to the efficient processing of speech input. The current experiment employed a passive eeG oddball paradigm to probe the neurophysiological processes underlying temporal and formal predictions simultaneously. The component of interest, the mismatch negativity (MMN), is considered a marker for experience-dependent change detection, where its timing and amplitude are indicative of the perceptual system's sensitivity to presented stimuli. We hypothesized that more predictable stimuli (i.e. high phonotactic probability and first syllable stress) would facilitate change detection, indexed by shorter peak latencies or greater peak amplitudes of the MMN. This hypothesis was confirmed for phonotactic probability: high phonotactic probability deviants elicited an earlier MMN than low phonotactic probability deviants. We do not observe a significant modulation of the MMN to variations in syllable stress. Our findings confirm that speech perception is shaped by formal and temporal predictability. this paradigm may be useful to investigate the contribution of implicit processing of statistical regularities during (a)typical language development.

Asymmetries in Accessing Vowel Representations Are Driven by Phonological and Acoustic Properties: Neural and Behavioral Evidence From Natural German Minimal Pairs

“…We included several theoretical, lexical, and phonotactic factors that are known to influence results. While other studies found an influence on neural data, for example, of phonotactic probabilities (Bonte et al, 2005 ; Yasin, 2007 ; Emmendorfer et al, 2020 ) or the lexical frequency of words (Alexandrov et al, 2011 ; Shtyrov et al, 2011 ; Aleksandrov et al, 2017 ), we cannot provide evidence for those factors neither on the electrophysiological nor on the behavioral data. On the contrary, we have identified a new influencing factor on MMN data: we found that neural effects were not only driven by phonemic features but also by the perceptual and psychoacoustic differences in perceived loudness in the stimuli.…”

“…For instance, since the phonological feature oppositions to distinguish different vowel qualities (i.e., high vowels vs. low vowels) are based on formants (Lahiri and Reetz, 2010 ), they also automatically imply an acoustic difference. Moreover, when words are used as stimuli, lexical features like frequency of occurrence (Alexandrov et al, 2011 ; Shtyrov et al, 2011 ) or phonotactic probability (Bonte et al, 2005 ; Yasin, 2007 ; Emmendorfer et al, 2020 ) are known to interfere in speech perception and vowel discrimination. Especially in our approach, where we tested the hypotheses of the models by using natural German spoken words, those influences may contribute to patterns of results.…”

“…This influence can even be present when testing real words in a passive oddball paradigm and can lead to a stronger MMN response for words with higher lexical frequency in opposition to deviants with a lower or intermediate frequency of occurrence (Alexandrov et al, 2011 ; Shtyrov et al, 2011 ; Aleksandrov et al, 2017 ). Furthermore, it has been shown that phonotactic probabilities (sequential order of phonemes in words) influence MMN results with higher probability, accompanied by enhanced MMN effects (Bonte et al, 2005 ; Yasin, 2007 ; Emmendorfer et al, 2020 ). Concerning vowel perception, acoustic properties, like for example fundamental frequency, vowel duration or intensity (Aaltonen et al, 1994 ; Kirmse et al, 2007 ; Peter et al, 2010 ; Partanen et al, 2011 ), have an impact on neural effects.…”

Phonological and temporal regularities lead to differential ERP effects in self- and externally generated speech

“…There is ample neural evidence supporting the aforementioned behavioral observations during speech perception, with variations in phonotactic probability and stress patterns modulations neural processing (Bonte et al, 2005; Di Liberto et al, 2019; Emmendorfer et al, 2020; Rothermich et al, 2012; Tremblay et al, 2016). However, data on the neural correlates of these features in speech production is sparse.…”

Neural Mechanisms of Hearing Recovery for Cochlear-Implanted Patients: An Electroencephalogram Follow-Up Study

“…The oddball stimuli paradigm ( Emmendorfer et al, 2020 ; Teixeira-Santos et al, 2020 ) with speech stimuli (350 ms duration with a 10-ms rise and fall time) was used in this study ( Figure 1 ). The stimuli were delivered via E-Prime 2.0 (Psychology Software Tools, Inc.) and through two loudspeakers at both ears at a comfortable level of 65 dB SPL.…”