Neural Representation of the English Vowel Feature [High]: Evidence From /ε/ vs. /ɪ/

Yu, Yan H.; Shafer, Valerie L.

doi:10.3389/fnhum.2021.629517

Cited by 7 publications

(3 citation statements)

References 88 publications

(146 reference statements)

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“…We recorded EEG at a 500-Hz sampling rate. Impedances were below 50 kΩ, per recommendation [ 91 ] and existing standard (e.g., [ 72 , 92 – 95 ]) for high-input impedance amplifiers. All electrodes were referenced to Cz.…”

Section: Methodsmentioning

confidence: 99%

Attention is required for canonical brain signature of prediction error despite early encoding of the stimuli

Male

O’Shea

2023

PLoS Biol

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Prediction error is a basic component of predictive-coding theory of brain processing. According to the theory, each stage of brain processing of sensory information generates a model of the current sensory input; subsequent input is compared against the model and only if there is a mismatch, a prediction error, is further processing performed. Recently, Smout and colleagues found that a signature of prediction error, the visual (v) mismatch negativity (MMN), for a fundamental property of visual input—its orientation—was absent without endogenous attention on the stimuli. This is remarkable because the weight of evidence for MMNs from audition and vision is that they occur without endogenous attention. To resolve this discrepancy, we conducted an experiment addressing 2 alternative explanations for Smout and colleagues’ finding: that it was from a lack of reproducibility or that participants’ visual systems did not encode the stimuli when attention was on something else. We conducted a similar experiment to that of Smout and colleagues. We showed 21 participants sequences of identically oriented Gabor patches, standards, and, unpredictably, otherwise identical, Gabor patches differing in orientation by ±15°, ±30°, and ±60°, deviants. To test whether participants encoded the orientation of the standards, we varied the number of standards preceding a deviant, allowing us to search for a decrease in activity with the number of repetitions of standards—repetition suppression. We diverted participants’ attention from the oriented stimuli with a central, letter-detection task. We reproduced Smout and colleagues’ finding of no vMMN without endogenous attention, strengthening their finding. We found that our participants showed repetition suppression: They did encode the stimuli preattentively. We also found early processing of deviants. We discuss various explanations why the earlier processing did not extend into the vMMN time window, including low precision of prediction.

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Section: Methodsmentioning

confidence: 99%

Attention is required for canonical brain signature of prediction error despite early encoding of the stimuli

Male

O’Shea

2023

PLoS Biol

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“…In fact, MMN evidence for features largely arises from findings that demonstrate that some speech sounds elicit larger MMNs than others. These asymmetric results have been observed for vowels (Cornell et al, 2011;de Rue et al, 2021;Eulitz & Lahiri, 2004;Scharinger et al, 2012Scharinger et al, , 2016Yu & Shafer, 2021), consonants (Cornell et al, 2013;Fu & Monahan, 2021;Hestvik et al, 2020;Hestvik & Durvasula, 2016;Maiste et al, 1995;Schluter et al, , 2017 and lexical tone (Politzer-Ahles et al, 2016). These asymmetries are oftenalthough not exclusively (see Maiste et al, 1995)-taken to reflect the underlying featural content of the two categories consistent with underspecified representations (Lahiri & Reetz, 2002, 2010.…”

Section: Introductionmentioning

confidence: 96%

Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features

Monahan

Schertz

et al. 2022

Journal of Cognitive Neuroscience

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Spoken word recognition models and phonological theory propose that abstract features play a central role in speech processing. It remains unknown, however, whether auditory cortex encodes linguistic features in a manner beyond the phonetic properties of the speech sounds themselves. We took advantage of the fact that English phonology functionally codes stops and fricatives as voiced or voiceless with two distinct phonetic cues: Fricatives use a spectral cue, whereas stops use a temporal cue. Evidence that these cues can be grouped together would indicate the disjunctive coding of distinct phonetic cues into a functionally defined abstract phonological feature. In English, the voicing feature, which distinguishes the consonants [s] and [t] from [z] and [d], respectively, is hypothesized to be specified only for voiceless consonants (e.g., [s t]). Here, participants listened to syllables in a many-to-one oddball design, while their EEG was recorded. In one block, both voiceless stops and fricatives were the standards. In the other block, both voiced stops and fricatives were the standards. A critical design element was the presence of intercategory variation within the standards. Therefore, a many-to-one relationship, which is necessary to elicit an MMN, existed only if the stop and fricative standards were grouped together. In addition to the ERPs, event-related spectral power was also analyzed. Results showed an MMN effect in the voiceless standards block—an asymmetric MMN—in a time window consistent with processing in auditory cortex, as well as increased prestimulus beta-band oscillatory power to voiceless standards. These findings suggest that (i) there is an auditory memory trace of the standards based on the shared (voiceless) feature, which is only functionally defined; (ii) voiced consonants are underspecified; and (iii) features can serve as a basis for predictive processing. Taken together, these results point toward auditory cortex's ability to functionally code distinct phonetic cues together and suggest that abstract features can be used to parse the continuous acoustic signal.

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“…In the field of neural decoding for direct communication in brain-computer interfaces (BCIs), research is progressing for detecting spoken signals from multi-channel electrocorticograms (ECoGs) at the brain cortex (Knight and Heinze, 2008;Pasley et al, 2012;Bouchard et al, 2013;Flinker et al, 2015;Herff and Schultz, 2016;Martin et al, 2018;Anumanchipalli et al, 2019;Miller et al, 2020). If we could instead detect linguistic information from scalp EEGs, then BCIs could enjoy much wider practical applications, for instance improving the quality of life (QoL) of amyotrophic lateral sclerosis (ALS) patients, but this goal is hampered by many unsolved problems (Wang et al, 2012;Min et al, 2016;Rojas and Ramos, 2016;Yoshimura et al, 2016;Yu and Shafer, 2021;Zhao et al, 2021). While studies on spoken EEGs can leverage motor command information to help identify speech-related signals, imagined speech EEGs (that is, EEGs of silent, unspoken speech) lack that luxury (Levelt, 1993;Indefrey and Levelt, 2004), which necessitates identifying linguistic representations solely from within the EEG.…”

Section: Introductionmentioning

confidence: 99%

Linguistic representation of vowels in speech imagery EEG

Nitta

Horikawa

Iribe

et al. 2023

Front. Hum. Neurosci.

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Speech imagery recognition from electroencephalograms (EEGs) could potentially become a strong contender among non-invasive brain-computer interfaces (BCIs). In this report, first we extract language representations as the difference of line-spectra of phones by statistically analyzing many EEG signals from the Broca area. Then we extract vowels by using iterative search from hand-labeled short-syllable data. The iterative search process consists of principal component analysis (PCA) that visualizes linguistic representation of vowels through eigen-vectors φ(m), and subspace method (SM) that searches an optimum line-spectrum for redesigning φ(m). The extracted linguistic representation of Japanese vowels /i/ /e/ /a/ /o/ /u/ shows 2 distinguished spectral peaks (P1, P2) in the upper frequency range. The 5 vowels are aligned on the P1-P2 chart. A 5-vowel recognition experiment using a data set of 5 subjects and a convolutional neural network (CNN) classifier gave a mean accuracy rate of 72.6%.

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Neural Representation of the English Vowel Feature [High]: Evidence From /ε/ vs. /ɪ/

Cited by 7 publications

References 88 publications

Attention is required for canonical brain signature of prediction error despite early encoding of the stimuli

Attention is required for canonical brain signature of prediction error despite early encoding of the stimuli

Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features

Linguistic representation of vowels in speech imagery EEG

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