Rhythm and vocal production learning are building blocks of human music and speech. Vocal learning has been hypothesized as a prerequisite for rhythmic capacities. Yet, no mammalian vocal learner but humans have shown the capacity to flexibly and spontaneously discriminate rhythmic patterns. Here we tested untrained rhythm discrimination in a mammalian vocal learning species, the harbour seal (). Twenty wild-born seals were exposed to music-like playbacks of conspecific call sequences varying in basic rhythmic properties. These properties were called length, sequence regularity, and overall tempo. All three features significantly influenced seals' reaction (number of looks and their duration), demonstrating spontaneous rhythm discrimination in a vocal learning mammal. This finding supports the rhythm-vocal learning hypothesis and showcases pinnipeds as promising models for comparative research on rhythmic phylogenies.
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http://dx.doi.org/10.1098/rsbl.2022.0316 | DOI Listing |
Prior research has indicated musicians show an auditory processing advantage in phonemic processing of language. The aim of the current study was to elucidate when in the auditory cortical processing stream this advantage emerges in a cocktail-party-like environment. Participants (n = 34) were aged 18-35 years and deemed to be either a musician (10+-year experience) or nonmusician (no formal training).
View Article and Find Full Text PDFHeart Rhythm
January 2025
Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK; Leeds Institute of Data Analytics, University of Leeds, Leeds, UK; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Background: Detecting atrial fibrillation (AF) after stroke is a key component of secondary prevention, but indiscriminate prolonged cardiac monitoring is costly and burdensome. Multivariable prediction models could be used to inform patient selection.
Objective: To determine the performance of available models for predicting AF after a stroke.
PLoS Biol
January 2025
Carney Institute for Brain Science, Department of Cognitive & Psychological Sciences, Brown University, Providence, Rhode Island, United States of America.
The basal ganglia (BG) play a key role in decision-making, preventing impulsive actions in some contexts while facilitating fast adaptations in others. The specific contributions of different BG structures to this nuanced behavior remain unclear, particularly under varying situations of noisy and conflicting information that necessitate ongoing adjustments in the balance between speed and accuracy. Theoretical accounts suggest that dynamic regulation of the amount of evidence required to commit to a decision (a dynamic "decision boundary") may be necessary to meet these competing demands.
View Article and Find Full Text PDFPsychiatr Serv
January 2025
Department of Psychiatry, University of Colorado, Aurora (Singhal, Mause, Dempsey); Department of Medicine, University of California San Francisco, San Francisco (Garcia, Ochoa-Frongia); Clinical and Research Library, Children's Hospital Colorado, Aurora (St. Pierre).
Objective: Immigrants and persons with a non-English language preference (NELP) face unique challenges in the mental health care system. This systematic scoping review aimed to evaluate the literature for disparities in psychiatric care delivery, beyond access and utilization barriers, experienced by these two populations.
Methods: The authors queried four databases: PubMed, PsycInfo, Web of Science, and CINAHL.
Heliyon
January 2025
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.
Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.
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