Linguistic category structure influences early auditory processing: Converging evidence from mismatch responses and cortical oscillations

Abstract

While previous research has established that language-specific knowledge influences early auditory processing, it is still controversial as to what aspects of speech sound representations determine early speech perception. Here, we propose that early processing primarily depends on information propagated top-down from abstractly represented speech sound categories. In particular, we assume that mid-vowels (as in ‘bet’) exert less top-down effects than the high-vowels (as in ‘bit’) because of their less specific (default) tongue height position as compared to either high- or low-vowels (as in ‘bat’). We tested this assumption in a Magnetoencephalographic (MEG) study where we contrasted mid- and high-vowels, as well as the low- and high-vowels in a passive oddball paradigm. Overall, significant differences between deviants and standards indexed reliable mismatch-negativity (MMN) responses between 200 and 300 ms post stimulus onset. MMN amplitudes differed in the mid/high-vowel contrasts and were significantly reduced when a mid-vowel standard was followed by a high-vowel deviant, extending previous findings. Furthermore, mid-vowel standards showed reduced oscillatory power in the pre-stimulus beta-frequency band (18–26 Hz), compared to high-vowel standards. We take this as converging evidence for linguistic category structure to exert top-down influences on auditory processing. The findings are interpreted within the linguistic model of underspecification and the neuropsychological predictive coding framework.

Effects of Speech Phonological Features during Passive Perception on Cortical Auditory Evoked Potential in Sensorineural Hearing Loss

“…Specifically, the deprivation situation was more prominent in the auditory N1 response towards the place of articulation feature compared to voicing contrast stimulus. In contrast, there were no clear justification involving the resemblance finding between N1 and N2 components, however may indeed reflect the reliance of cortical auditory response towards phonologic features of speech signal (Bien et al 2016;Carpenter & Shahin 2013;Scharinger et al 2016). …”

Neural signals to violations of abstract rules using speech-like stimuli

“…Electroencephalography (EEG) studies have identified an event related potential (ERP) known as N400, whose amplitude is inversely correlated with the semantic predictability of words in context (Ku-tas and Hillyard, 1980;Van Petten et al, 1999;Brink et al, 2001;Federmeier, 2000, 2011;Freunberger and Roehm, 2016;DeLong et al, 2005). EEG evidence has also shown that forthcoming phonemes can be predicted using syntactic (DeLong et al, 2005), semantic (Bendixen et al, 2014;Kashino, 2006;Groppe et al, 2010), phonological (Cornell et al, 2011;Hestvik and Durvasula, 2016;Schluter et al, 2016;Scharinger et al, 2016) and phonotactic information (Dehaene-Lambertz et al, 2000;Sun et al, 2015;Ylinen et al, 2016).…”

MisMatch Negativity-study showing pre-lexical sensitivity to both primary Final Accent and secondary Initial Accent in French

“…Importantly, whereas an mmn may be elicited by a purely acoustic difference, many studies will additionally switch the position of the deviant stimulus and the standard stimulus, such that they have another condition, wherein the deviant is presented frequently, while the (formerly) standard stimulus is presented infrequently (e.g. Honbolygó & Csépe, 2013; Aguilera et al, 2014; Scharinger et al, 2016, see also Astésano et al in prep ). If the standard and deviant stimuli differ only acoustically, the mmn s in both conditions should be similar.…”

“…Winkler et al, 2009; Garrido et al, 2009) and, as such, the mmn has had a substantial contribution in investigations of underspecification of phonemic representations (e.g. Eulitz & Lahiri, 2004; Näätänen et al, 2007; Winkler et al, 2009; Deguchi et al, 2010; Ylinen et al, 2016; Scharinger et al, 2016, 2017), as well as the phonological representation of stress patterns (e.g. Ylinen et al, 2009; Honbolygó et al, 2004; Honbolygó & Csépe, 2013; Aguilera et al, 2014; Honbolygó et al, 2017; Garami et al, 2017).…”