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We investigated the development of early-latency and long-latency brain responses to native and non-native speech to shed light on the neurophysiological underpinnings of perceptual narrowing and early language development. Specifically, we postulated a two-level process to explain the decrease in sensitivity to non-native phonemes towards the end of infancy. Neurons at the earlier stages of the ascending auditory pathway mature rapidly during infancy facilitating the encoding of both native and non-native sounds. This growth enables neurons at the later stages of the auditory pathway to assign phonological status to speech according to the infant’s native language environment. To test this hypothesis, we collected early- latency and long-latency neural responses to native and non-native lexical tones from 85 Cantoneselearning children aged between 23 days and 24 months and 16 days. As expected, a broad range of presumably subcortical early-latency neural encoding measures grew rapidly and substantially during the first two years for both native and non-native tones. By contrast, longlatency cortical electrophysiological changes occurred on a much slower scale and showed sensitivity to nativeness at around six months. Our study provided a comprehensive understanding of early language development by revealing the complementary roles of earlier and later stages of speech processing in the developing brain.

Purpose This study aimed to construct an objective and cost-effective prognostic tool to forecast the future language and communication abilities of individual infants. Method Speech-evoked electroencephalography (EEG) data were collected from 118 infants during the first year of life during the exposure to speech stimuli that differed principally in fundamental frequency. Language and communication outcomes, namely four subtests of the MacArthur–Bates Communicative Development Inventories (MCDI)–Chinese version, were collected between 3 and 16 months after initial EEG testing. In the two-way classification, children were classified into those with future MCDI scores below the 25th percentile for their age group and those above the same percentile, while the three-way classification classified them into < 25th, 25th–75th, and > 75th percentile groups. Machine learning (support vector machine classification) with cross validation was used for model construction. Statistical significance was assessed. Results Across the four MCDI measures of early gestures, later gestures, vocabulary comprehension, and vocabulary production, the areas under the receiver-operating characteristic curve of the predictive models were respectively .92 ± .031, .91 ± .028, .90 ± .035, and .89 ± .039 for the two-way classification, and .88 ± .041, .89 ± .033, .85 ± .047, and .85 ± .050 for the three-way classification ( p < .01 for all models). Conclusions Future language and communication variability can be predicted by an objective EEG method that indicates the function of the auditory neural pathway foundational to spoken language development, with precision sufficient for individual predictions. Longer-term research is needed to assess predictability of categorical diagnostic status. Supplemental Material https://doi.org/10.23641/asha.15138546