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Record W4205467231 · doi:10.1162/jocn_a_01817

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

2022· article· en· W4205467231 on OpenAlexafffund

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Toronto ScarboroughSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoEuropean Commission
KeywordsVoicePhonologyMismatch negativityCoding (social sciences)Variation (astronomy)PhoneticsAuditory cortex

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.354
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes2
Has abstractyes

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