Although challenging, adults can learn non-native phonetic contrasts with extensive training [1, 2], indicative of perceptual learning beyond an early sensitivity period [3, 4]. Training can alter low-level sensory encoding of newly acquired speech sound patterns [5]; however, the time-course, behavioral relevance, and long-term retention of such sensory plasticity is unclear. Some theories argue that sensory plasticity underlying signal enhancement is immediate and critical to perceptual learning [6, 7]. Others, like the reverse hierarchy theory (RHT), posit a slower time-course for sensory plasticity [8]. RHT proposes that higher-level categorical representations guide immediate, novice learning, while lower-level sensory changes do not emerge until expert stages of learning [9]. We trained 20 English-speaking adults to categorize a non-native phonetic contrast (Mandarin lexical tones) using a criterion-dependent sound-to-category training paradigm. Sensory and perceptual indices were assayed across operationally defined learning phases (novice, experienced, over-trained, and 8-week retention) by measuring the frequency-following response, a neurophonic potential that reflects fidelity of sensory encoding, and the perceptual identification of a tone continuum. Our results demonstrate that while robust changes in sensory encoding and perceptual identification of Mandarin tones emerged with training and were retained, such changes followed different timescales. Sensory changes were evidenced and related to behavioral performance only when participants were over-trained. In contrast, changes in perceptual identification reflecting improvement in categorical percept emerged relatively earlier. Individual differences in perceptual identification, and not sensory encoding, related to faster learning. Our findings support the RHT-sensory plasticity accompanies, rather than drives, expert levels of non-native speech learning.

The findings suggest that bilingual language exposure is not associated with additional challenges for the development of the dominant language in children with ASD. The lack of negative associations in our sample is not likely to be due to the comparatively early diagnosis and/or intervention that are available in other countries. We discuss implications for decisions regarding the linguistic environment of children with ASD.

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IntroductionScalp‐recorded electrophysiological responses to complex, periodic auditory signals reflect phase‐locked activity from neural ensembles within the auditory system. These responses, referred to as frequency‐following responses (FFRs), have been widely utilized to index typical and atypical representation of speech signals in the auditory system. One of the major limitations in FFR is the low signal‐to‐noise ratio at the level of single trials. For this reason, the analysis relies on averaging across thousands of trials. The ability to examine the quality of single‐trial FFRs will allow investigation of trial‐by‐trial dynamics of the FFR, which has been impossible due to the averaging approach.MethodsIn a novel, data‐driven approach, we used machine learning principles to decode information related to the speech signal from single trial FFRs. FFRs were collected from participants while they listened to two vowels produced by two speakers. Scalp‐recorded electrophysiological responses were projected onto a low‐dimensional spectral feature space independently derived from the same two vowels produced by 40 speakers, which were not presented to the participants. A novel supervised machine learning classifier was trained to discriminate vowel tokens on a subset of FFRs from each participant, and tested on the remaining subset.ResultsWe demonstrate reliable decoding of speech signals at the level of single‐trials by decomposing the raw FFR based on information‐bearing spectral features in the speech signal that were independently derived.ConclusionsTaken together, the ability to extract interpretable features at the level of single‐trials in a data‐driven manner offers unchartered possibilities in the noninvasive assessment of human auditory function.

While lifelong language experience modulates subcortical encoding of pitch patterns, there is emerging evidence that short-term training introduced in adulthood also shapes subcortical pitch encoding. Here we use a cross-language design to examine the stability of language experience-dependent subcortical plasticity over multiple days. We then examine the extent to which behavioral relevance induced by sound-to-category training leads to plastic changes in subcortical pitch encoding in adulthood relative to adolescence, a period of ongoing maturation of subcortical and cortical auditory processing. Frequency-following responses (FFRs), which reflect phase-locked activity from subcortical neural ensembles, were elicited while participants passively listened to pitch patterns reflective of Mandarin tones. In , FFRs were recorded across three consecutive days from native Chinese-speaking ( = 10) and English-speaking ( = 10) adults. In , FFRs were recorded from native English-speaking adolescents ( = 20) and adults ( = 15) before, during, and immediately after a session of sound-to-category training, as well as a day after training ceased. demonstrated the stability of language experience-dependent subcortical plasticity in pitch encoding across multiple days of passive exposure to linguistic pitch patterns. In contrast, revealed an enhancement in subcortical pitch encoding that emerged a day after the sound-to-category training, with some developmental differences observed. Taken together, these findings suggest that behavioral relevance is a critical component for the observation of plasticity in the subcortical encoding of pitch. We examine the timescale of experience-dependent auditory plasticity to linguistically relevant pitch patterns. We find extreme stability in lifelong experience-dependent plasticity. We further demonstrate that subcortical function in adolescents and adults is modulated by a single session of sound-to-category training. Our results suggest that behavioral relevance is a necessary ingredient for neural changes in pitch encoding to be observed throughout human development. These findings contribute to the neurophysiological understanding of long- and short-term experience-dependent modulation of pitch.

Purpose Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)–based approaches to analyzing speech-evoked neurophysiological responses. Method Two categories of ML-based approaches are introduced: decoding models, which generate a speech stimulus output using the features from the neurophysiological responses, and encoding models, which use speech stimulus features to predict neurophysiological responses. In this review, we focus on (a) a decoding model classification approach, wherein speech-evoked neurophysiological responses are classified as belonging to 1 of a finite set of possible speech events (e.g., phonological categories), and (b) an encoding model temporal response function approach, which quantifies the transformation of a speech stimulus feature to continuous neural activity. Results We illustrate the utility of the classification approach to analyze early electroencephalographic (EEG) responses to Mandarin lexical tone categories from a traditional experimental design, and to classify EEG responses to English phonemes evoked by natural continuous speech (i.e., an audiobook) into phonological categories (plosive, fricative, nasal, and vowel). We also demonstrate the utility of temporal response function to predict EEG responses to natural continuous speech from acoustic features. Neural metrics from the 3 examples all exhibit statistically significant effects at the individual level. Conclusion We propose that ML-based approaches can complement traditional analysis approaches to analyze neurophysiological responses to speech signals and provide a deeper understanding of natural speech and language processing using ecologically valid paradigms in both typical and clinical populations.

Key Points Question Is a reference standard measure of autism spectrum disorder (ASD), the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), systematically biased across sex and race? Findings In this cross-sectional study of 6269 children evaluated at an ASD specialty clinic in the US, 11% of ADOS-2 diagnostic items demonstrated bias for Black/African American vs White children and for female vs male children. The magnitude of bias was moderate to large for only 2 repetitive/restricted behavior interest items. Meaning Although the ADOS-2 demonstrated minimal bias overall, these findings suggest that further research is needed to address some evidence of underdetection of ASD symptoms in Black/African American children and female children.

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