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In the present study, cortical responses of two sustained Italian vowels, /a/ and /i/, were analyzed in perception and production tasks by using the event-related potential (ERP) technique. First, we showed that the earlier auditory waveform complex, N1/P2, was elicited in both experimental tasks by acoustically presented vowels. More interestingly, the cortical activity of auditory cortex seems to reflect different spectro-acoustical items of Italian vowels in the production task. Specifically, the vowel /a/ generated a weaker activity than /i/ as revealed by different neuronal modulations; moreover, during speech production, the auditory cortex showed a dampening and delaying of its activity in the left hemisphere.

How the brain encodes the speech acoustic signal into phonological representations is a fundamental question for the neurobiology of language. Determining whether this process is characterized by tonotopic maps in primary or secondary auditory areas, with bilateral or leftward activity, remains a long-standing challenge. Magnetoencephalographic studies failed to show hierarchical and asymmetric hints for speech processing. We employed high-density electroencephalography to map the Salento Italian vowel system onto cortical sources using the N1 auditory evoked component. We found evidence that the N1 is characterized by hierarchical and asymmetrical indexes in primary and secondary auditory areas structuring vowel representations. Importantly, the N1 was characterized by early and late phases. The early N1 peaked at 125–135 msec and was localized in the primary auditory cortex; the late N1 peaked at 145–155 msec and was localized in the left superior temporal gyrus. We showed that early in the primary auditory cortex, the cortical spatial arrangements—along the lateral-medial and anterior-posterior gradients—are broadly warped by phonemotopic patterns according to the distinctive feature principle. These phonemotopic patterns are carefully refined in the superior temporal gyrus along the inferior-superior and anterior-posterior gradients. The dynamical and hierarchical interface between primary and secondary auditory areas and the interaction effects between Height and Place features generate the categorical representation of the Salento Italian vowels.

Speech sound perception is one of the most fascinating tasks performed by the human brain. It involves a mapping from continuous acoustic waveforms onto the discrete phonological units computed to store words in the mental lexicon. In this article, we review the magnetoencephalographic studies that have explored the timing and morphology of the N1m component to investigate how vowels and consonants are computed and represented within the auditory cortex. The neurons that are involved in the N1m act to construct a sensory memory of the stimulus due to spatially and temporally distributed activation patterns within the auditory cortex. Indeed, localization of auditory fields maps in animals and humans suggested two levels of sound coding, a tonotopy dimension for spectral properties and a tonochrony dimension for temporal properties of sounds. When the stimulus is a complex speech sound, tonotopy and tonochrony data may give important information to assess whether the speech sound parsing and decoding are generated by pure bottom-up reflection of acoustic differences or whether they are additionally affected by top-down processes related to phonological categories. Hints supporting pure bottom-up processing coexist with hints supporting top-down abstract phoneme representation. Actually, N1m data (amplitude, latency, source generators, and hemispheric distribution) are limited and do not help to disentangle the issue. The nature of these limitations is discussed. Moreover, neurophysiological studies on animals and neuroimaging studies on humans have been taken into consideration. We compare also the N1m findings with the investigation of the magnetic mismatch negativity (MMNm) component and with the analogous electrical components, the N1 and the MMN. We conclude that N1 seems more sensitive to capture lateralization and hierarchical processes than N1m, although the data are very preliminary. Finally, we suggest that MEG data should be integrated with EEG data in the light of the neural oscillations framework and we propose some concerns that should be addressed by future investigations if we want to closely line up language research with issues at the core of the functional brain mechanisms.