Long-latency event-related responses to vowels: N1-P2 decomposition by two-step principal component analysis
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Universidade Federal de Minas Gerais
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Artigo de periódico
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Resumo
The N1-P2 complex of the auditory event-related potential (ERP) has been used to examine neural activity associated with speech sound perception. Since it is thought to reflect multiple generator processes, its functional significance is difficult to infer. In the present study, a temporospatial principal component analysis (PCA) was used to decompose the N1-P2 response into latent factors underlying covariance patterns in ERP data recorded during passive listening to pairs of successive vowels. In each trial, one of six sounds drawn from an /i/−/e/ vowel continuum was followed either by an identical sound, a different token of the same vowel category, or a token from the other category. Responses were examined as to how they were modulated by within- and across-category vowel differences and by adaptation (repetition suppression) effects. Five PCA factors were identified as corresponding to three well-known N1 subcomponents and two P2 subcomponents. Results added evidence that the N1 peak reflects both generators that are sensitive to spectral information and generators that are not. For later latency ranges, different patterns of sensitivity to vowel quality were found, including category-related effects. Particularly, a subcomponent identified as the Tb wave showed release from adaptation in response to an /i/ followed by an /e/ sound. A P2 subcomponent varied linearly with spectral shape along the vowel continuum, while the other was stronger the closer the vowel was to the category boundary, suggesting separate processing of continuous and category-related information. Thus, the PCA-based decomposition of the N1-P2 complex was functionally meaningful, revealing distinct underlying processes at work during speech sound perception.
Abstract
Assunto
Percepção da fala, Categorização (Linguística), Circuitos neurais
Palavras-chave
Principal component analysis, Event-related potential, N1, P2, Speech perception, Categorization