Junge, C. | bioRxiv Science

bioRxiv2026

Neural tracking of stressed syllables in Dutch nursery rhymes relates to vocabulary outcomes in a large, longitudinal sample

Speech consists of regularities at different timescales. Already during infancy, neural electrophysiological activity aligns to these rhythms. The degree to which infants exhibit neural tracking of speech can be linked to their language development. In this study, we examined how the neural tracking of sung speech develops across age, from infancy to early childhood, and across different frequency bands (i.e., at the stress, syllabic, and phonemic rates), and whether neural tracking at each frequency and age predicts childrens language outcomes. We included 2565 children of the longitudinal YOUth cohort. Children listened to Dutch sung nursery rhymes while EEG was recorded at three measurement waves. After preprocessing the data, we included 955 children at 5 months, 1048 children at 10 months, and 795 children at 2-4 years. The final sample consisted of 750 children who also completed a receptive vocabulary test at 2-4 years. Children from 5 months onwards showed significant neural tracking of stressed syllables, syllables, and phonemes, measured with speech-brain coherence (SBC). Unexpectedly, there were no developmental changes in SBC across different frequency bands from infancy to early childhood. As expected, children with larger receptive vocabularies showed increased SBC in the stressed syllable rate. These findings suggest that stronger tracking of stressed syllables is related to individual differences in language ability.

Developmental Biology↗

bioRxiv2020

Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from over 5,000 videos of over 2,500 dogs and ran the algorithms in production on over 11 million days of device data. We then surveyed project participants representing 10,550 dogs, they provided us 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking, petting, rubbing, scratching, and sniffing. We show that the devices position on the collar has no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1,514 users), and of 94.9% (among 1,491 users) for drinking. The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

animal behavior and cognition↗