Dataset Information

Distinct representations of phonemes, syllables, and supra-syllabic sequences in the speech production network.

ABSTRACT: Functional neuroimaging studies have converged on a core network of brain regions that supports speech production, but the sublexical processing stages performed by the different parts of this network remain unclear. Using an fMRI adaptation paradigm and quantitative analysis of patterns of activation rather than contrast subtractions alone, we were able to identify a set of neural substrates predominantly engaged in phonemic, syllabic, and supra-syllabic levels of processing during speech. Phoneme-level processes were found in the left SMA, pallidum, posterior superior temporal gyrus, and superior lateral cerebellum. Syllable-level processes were found in the left ventral premotor cortex, and supra-syllabic processes related to phonological chunking were found in the right superior lateral cerebellum. Active regions that were not sensitive to sublexical manipulations included primary motor and auditory cortical areas, and medial cerebellum. These results offer a quantitative technique for localizing sublexical neural processes that are difficult to dissociate using non-invasive imaging techniques and provide the beginnings of a "brain map" for language output.

SUBMITTER: Peeva MG

PROVIDER: S-EPMC2840383 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Distinct representations of phonemes, syllables, and supra-syllabic sequences in the speech production network.

Peeva Maya G MG Guenther Frank H FH Tourville Jason A JA Nieto-Castanon Alfonso A Anton Jean-Luc JL Nazarian Bruno B Alario F-Xavier FX

NeuroImage 20091224 2

Functional neuroimaging studies have converged on a core network of brain regions that supports speech production, but the sublexical processing stages performed by the different parts of this network remain unclear. Using an fMRI adaptation paradigm and quantitative analysis of patterns of activation rather than contrast subtractions alone, we were able to identify a set of neural substrates predominantly engaged in phonemic, syllabic, and supra-syllabic levels of processing during speech. Phon ...[more]

PMID: 20035884

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Project description:At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one's own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop in infancy but also for our understanding of how they may have evolved.

Dataset Information

Distinct representations of phonemes, syllables, and supra-syllabic sequences in the speech production network.

Publications

Distinct representations of phonemes, syllables, and supra-syllabic sequences in the speech production network.

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