Precursors to infant sensorimotor synchronization to speech and non-speech rhythms: A longitudinal study

Impaired sensorimotor synchronization (SMS) to acoustic rhythm may be a marker of atypical language development. Here, Motion Capture was used to assess gross motor rhythmic movement at six time points between 5- and 11 months of age. Infants were recorded drumming to acoustic stimuli of varying linguistic and temporal complexity: drumbeats, repeated syllables and nursery rhymes. Here we show, for the first time, developmental change in infants' movement timing in response to auditory stimuli over the first year of life. Longitudinal analyses revealed that whilst infants could not yet reliably synchronize their movement to auditory rhythms, infant spontaneous motor tempo became faster with age, and by 11 months, a subset of infants decelerate from their spontaneous motor tempo, which better accords with the incoming tempo. Further, infants became more regular drummers with age, with marked decreases in the variability of spontaneous motor tempo and variability in response to drumbeats. This latter effect was subdued in response to linguistic stimuli. The current work lays the foundation for using individual differences in precursors of SMS in infancy to predict later language outcomes. RESEARCH HIGHLIGHT: We present the first longitudinal investigation of infant rhythmic movement over the first year of life Whilst infants generally move more quickly and with higher regularity over their first year, by 11 months infants begin to counter this pattern when hearing slower infant-directed song Infant movement is more variable to speech than non-speech stimuli In the context of the larger Cambridge UK BabyRhythm Project, we lay the foundation for rhythmic movement in infancy to predict later language outcomes.

Decoding speech information from EEG data with 4-, 7- and 11-month-old infants: Using convolutional neural network, mutual information-based and backward linear models

BACKGROUND

Computational models that successfully decode neural activity into speech are increasing in the adult literature, with convolutional neural networks (CNNs), backward linear models, and mutual information (MI) models all being applied to neural data in relation to speech input. This is not the case in the infant literature.

NEW METHOD

Three different computational models, two novel for infants, were applied to decode low-frequency speech envelope information. Previously-employed backward linear models were compared to novel CNN and MI-based models. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung or chanted nursery rhymes) presented by video with a female singer.

RESULTS

Each model computed speech information for these nursery rhymes in two different low-frequency bands, delta and theta, thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band neural activity from 4 months of age, with two of three models also showing significant performance for theta-band activity. All models also demonstrated higher accuracy for the delta-band neural responses. None of the models showed developmental (age-related) effects.

COMPARISONS WITH EXISTING METHODS

The data demonstrate that the choice of algorithm used to decode speech envelope information from neural activity in the infant brain determines the developmental conclusions that can be drawn.

CONCLUSIONS

The modelling shows that better understanding of the strengths and weaknesses of each modelling approach is fundamental to improving our understanding of how the human brain builds a language system.

Neural phase angle from two months when tracking speech and non-speech rhythm linked to language performance from 12 to 24 months

Atypical phase alignment of low-frequency neural oscillations to speech rhythm has been implicated in phonological deficits in developmental dyslexia. Atypical phase alignment to rhythm could thus also characterize infants at risk for later language difficulties. Here, we investigate phase-language mechanisms in a neurotypical infant sample. 122 two-, six- and nine-month-old infants were played speech and non-speech rhythms while EEG was recorded in a longitudinal design. The phase of infants' neural oscillations aligned consistently to the stimuli, with group-level convergence towards a common phase. Individual low-frequency phase alignment related to subsequent measures of language acquisition up to 24 months of age. Accordingly, individual differences in language acquisition are related to the phase alignment of cortical tracking of auditory and audiovisual rhythms in infancy, an automatic neural mechanism. Automatic rhythmic phase-language mechanisms could eventually serve as biomarkers, identifying at-risk infants and enabling intervention at the earliest stages of development.

Expectations about precision bias metacognition and awareness

Bayesian models of the mind suggest that we estimate the reliability or "precision" of incoming sensory signals to guide perceptual inference and to construct feelings of confidence or uncertainty about what we are perceiving. However, accurately estimating precision is likely to be challenging for bounded systems like the brain. One way observers could overcome this challenge is to form expectations about the precision of their perceptions and use these to guide metacognition and awareness. Here we test this possibility. Participants made perceptual decisions about visual motion stimuli, while providing confidence ratings (Experiments 1 and 2) or ratings of subjective visibility (Experiment 3). In each experiment, participants acquired probabilistic expectations about the likely strength of upcoming signals. We found these expectations about precision altered metacognition and awareness-with participants feeling more confident and stimuli appearing more vivid when stronger sensory signals were expected, without concomitant changes in objective perceptual performance. Computational modeling revealed that this effect could be well explained by a predictive learning model that infers the precision (strength) of current signals as a weighted combination of incoming evidence and top-down expectation. These results support an influential but untested tenet of Bayesian models of cognition, suggesting that agents do not only "read out" the reliability of information arriving at their senses, but also take into account prior knowledge about how reliable or "precise" different sources of information are likely to be. This reveals that expectations about precision influence how the sensory world appears and how much we trust our senses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cortical Tracking of Sung Speech in Adults vs Infants: A Developmental Analysis

Here we duplicate a neural tracking paradigm, previously published with infants (aged 4 to 11 months), with adult participants, in order to explore potential developmental similarities and differences in entrainment. Adults listened and watched passively as nursery rhymes were sung or chanted in infant-directed speech. Whole-head EEG (128 channels) was recorded, and cortical tracking of the sung speech in the delta (0.5–4 Hz), theta (4–8 Hz) and alpha (8–12 Hz) frequency bands was computed using linear decoders (multivariate Temporal Response Function models, mTRFs). Phase-amplitude coupling (PAC) was also computed to assess whether delta and theta phases temporally organize higher-frequency amplitudes for adults in the same pattern as found in the infant brain. Similar to previous infant participants, the adults showed significant cortical tracking of the sung speech in both delta and theta bands. However, the frequencies associated with peaks in stimulus-induced spectral power (PSD) in the two populations were different. PAC was also different in the adults compared to the infants. PAC was stronger for theta- versus delta- driven coupling in adults but was equal for delta- versus theta-driven coupling in infants. Adults also showed a stimulus-induced increase in low alpha power that was absent in infants. This may suggest adult recruitment of other cognitive processes, possibly related to comprehension or attention. The comparative data suggest that while infant and adult brains utilize essentially the same cortical mechanisms to track linguistic input, the operation of and interplay between these mechanisms may change with age and language experience.

Neural detection of changes in amplitude rise time in infancy

Amplitude rise times play a crucial role in the perception of rhythm in speech, and reduced perceptual sensitivity to differences in rise time is related to developmental language difficulties. Amplitude rise times also play a mechanistic role in neural entrainment to the speech amplitude envelope. Using an ERP paradigm, here we examined for the first time whether infants at the ages of seven and eleven months exhibit an auditory mismatch response to changes in the rise times of simple repeating auditory stimuli. We found that infants exhibited a mismatch response (MMR) to all of the oddball rise times used for the study. The MMR was more positive at seven than eleven months of age. At eleven months, there was a shift to a mismatch negativity (MMN) that was more pronounced over left fronto-central electrodes. The MMR over right fronto-central electrodes was sensitive to the size of the difference in rise time. The results indicate that neural processing of changes in rise time is present at seven months, supporting the possibility that early speech processing is facilitated by neural sensitivity to these important acoustic cues.

Delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants

The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding at multiple time scales. Neurophysiological signals are known to track the amplitude envelope of adult-directed speech (ADS), particularly in the theta-band. Acoustic analysis of infant-directed speech (IDS) has revealed significantly greater modulation energy than ADS in an amplitude-modulation (AM) band centred on ∼2 Hz. Accordingly, cortical tracking of IDS by delta-band neural signals may be key to language acquisition. Speech also contains acoustic information within its higher-frequency bands (beta, gamma). Adult EEG and MEG studies reveal an oscillatory hierarchy, whereby low-frequency (delta, theta) neural phase dynamics temporally organize the amplitude of high-frequency signals (phase amplitude coupling, PAC). Whilst consensus is growing around the role of PAC in the matured adult brain, its role in the development of speech processing is unexplored. Here, we examined the presence and maturation of low-frequency (<12 Hz) cortical speech tracking in infants by recording EEG longitudinally from 60 participants when aged 4-, 7- and 11- months as they listened to nursery rhymes. After establishing stimulus-related neural signals in delta and theta, cortical tracking at each age was assessed in the delta, theta and alpha [control] bands using a multivariate temporal response function (mTRF) method. Delta-beta, delta-gamma, theta-beta and theta-gamma phase-amplitude coupling (PAC) was also assessed. Significant delta and theta but not alpha tracking was found. Significant PAC was present at all ages, with both delta and theta -driven coupling observed.

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG

Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language disorders. However, infant brain recordings are noisy. As a first step to developing accurate neural biomarkers, we investigate whether infant brain responses to rhythmic stimuli can be classified reliably using EEG from 95 eight-week-old infants listening to natural stimuli (repeated syllables or drumbeats). Both Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches were employed. Applied to one infant at a time, the CNN discriminated syllables from drumbeats with a mean AUC of 0.87, against two levels of noise. The SVM classified with AUC 0.95 and 0.86 respectively, showing reduced performance as noise increased. Our proof-of-concept modelling opens the way to the development of clinical biomarkers for language disorders related to rhythmic entrainment.