Pitch accents are local pitch patterns that convey differences in word prominence and modulate the information structure of the discourse. Despite the importance to discourse in languages like English, neural processing of pitch accents remains understudied. The current study investigates the neural processing of pitch accents by native and non-native English speakers while they are listening to or ignoring 45 min of continuous, natural speech. Leveraging an approach used to study phonemes in natural speech, we analyzed thousands of electroencephalography (EEG) segments time-locked to pitch accents in a prosodic transcription. The optimal neural discrimination between pitch accent categories emerged at latencies between 100 and 200 ms. During these latencies, we found a strong structural alignment between neural and phonetic representations of pitch accent categories. In the same latencies, native listeners exhibited more robust processing of pitch accent contrasts than non-native listeners. However, these group differences attenuated when the speech signal was ignored. We can reliably capture the neural processing of discrete and contrastive pitch accent categories in continuous speech. Our analytic approach also captures how language-specific knowledge and selective attention influences the neural processing of pitch accent categories.
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http://dx.doi.org/10.1016/j.neuropsychologia.2021.107883 | DOI Listing |
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Center for Environmental Solutions and Emergency Response, United States Environmental Protection Agency, Cincinnati, Ohio.
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View Article and Find Full Text PDFPLoS Comput Biol
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Electrical and Computer Engineering Department, Concordia University, Montreal, Canada.
Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways.
View Article and Find Full Text PDFPLoS One
January 2025
School of Resources and Environment, Inner Mongolia University of Technology, Hohhot, China.
The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. To address this issue, a signal modulation recognition method based on a two-way interactive temporal attention network algorithm has been developed.
View Article and Find Full Text PDFMicrosc Microanal
January 2025
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin 14195, Germany.
In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.
View Article and Find Full Text PDFJ Vis
January 2025
Neural Information Processing Group, University of Tübingen, Tübingen, Germany.
Human performance in psychophysical detection and discrimination tasks is limited by inner noise. It is unclear to what extent this inner noise arises from early noise (e.g.
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