Cross-language speech perception experiments indicate that for many vowel contrasts, discrimination is easier when the same pair of vowels is presented in one direction compared to the reverse direction. According to one account, these directional asymmetries reflect a universal bias favoring "focal" vowels (i.e., vowels with prominent spectral peaks formed by the convergence of adjacent formants). An alternative account is that such effects reflect an experience-dependent bias favoring prototypical exemplars of native-language vowel categories. Here, we tested the predictions of these accounts by recording the auditory frequency-following response in English-speaking listeners to two synthetic variants of the vowel /u/ that differed in the proximity of their first and second formants and prototypicality, with stimuli arranged in oddball and reversed-oddball blocks. Participants showed evidence of neural discrimination when the more-focal/less-prototypic /u/ served as the deviant stimulus, but not when the less-focal/more-prototypic /u/ served as the deviant, consistent with the focalization account.
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http://dx.doi.org/10.1016/j.bandl.2019.05.002 | DOI Listing |
Brain Res
January 2025
epartment of Basic Medicine, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang 310015, China; Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang 310015, China. Electronic address:
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View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430070, Hubei, China.
In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-class instances typically correspond to critical events, such as system faults in power grids or abnormal health occurrences in medical monitoring. Despite being rare and random, these events are highly significant. The dynamic spatial-temporal relationships between minority-class instances and other instances make them more prone to interference from neighboring instances during classification.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany.
This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive fatigue and resultant injury, and increase efficiency and safety.
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